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swarm</title> <meta name="description" content="Search results for: flocking swarm"> <meta name="keywords" content="flocking swarm"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="flocking swarm"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 236</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: flocking swarm</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">236</span> Flocking Swarm of Robots Using Artificial Innate Immune System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muneeb%20Ahmad">Muneeb Ahmad</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20Raza"> Ali Raza</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A computational method inspired by the immune system (IS) is presented, leveraging its shared characteristics of robustness, fault tolerance, scalability, and adaptability with swarm intelligence. This method aims to showcase flocking behaviors in a swarm of robots (SR). The innate part of the IS offers a variety of reactive and probabilistic cell functions alongside its self-regulation mechanism which have been translated to enable swarming behaviors. Although, the research is specially focused on flocking behaviors in a variety of simulated environments using e-puck robots in a physics-based simulator (CoppeliaSim); the artificial innate immune system (AIIS) can exhibit other swarm behaviors as well. The effectiveness of the immuno-inspired approach has been established with extensive experimentations, for scalability and adaptability, using standard swarm benchmarks as well as the immunological regulatory functions (i.e., Dendritic Cells’ Maturity and Inflammation). The AIIS-based approach has proved to be a scalable and adaptive solution for emulating the flocking behavior of SR. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20innate%20immune%20system" title="artificial innate immune system">artificial innate immune system</a>, <a href="https://publications.waset.org/abstracts/search?q=flocking%20swarm" title=" flocking swarm"> flocking swarm</a>, <a href="https://publications.waset.org/abstracts/search?q=immune%20system" title=" immune system"> immune system</a>, <a href="https://publications.waset.org/abstracts/search?q=swarm%20intelligence" title=" swarm intelligence"> swarm intelligence</a> </p> <a href="https://publications.waset.org/abstracts/168936/flocking-swarm-of-robots-using-artificial-innate-immune-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168936.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">104</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">235</span> An Algorithm for Herding Cows by a Swarm of Quadcopters</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jeryes%20Danial">Jeryes Danial</a>, <a href="https://publications.waset.org/abstracts/search?q=Yosi%20Ben%20Asher"> Yosi Ben Asher</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Algorithms for controlling a swarm of robots is an active research field, out of which cattle herding is one of the most complex problems to solve. In this paper, we derive an independent herding algorithm that is specifically designed for a swarm of quadcopters. The algorithm works by devising flight trajectories that cause the cows to run-away in the desired direction and hence herd cows that are distributed in a given field towards a common gathering point. Unlike previously proposed swarm herding algorithms, this algorithm does not use a flocking model but rather stars each cow separately. The effectiveness of this algorithm is verified experimentally using a simulator. We use a special set of experiments attempting to demonstrate that the herding times of this algorithm correspond to field diameter small constant regardless of the number of cows in the field. This is an optimal result indicating that the algorithm groups the cows into intermediate groups and herd them as one forming ever closing bigger groups. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=swarm" title="swarm">swarm</a>, <a href="https://publications.waset.org/abstracts/search?q=independent" title=" independent"> independent</a>, <a href="https://publications.waset.org/abstracts/search?q=distributed" title=" distributed"> distributed</a>, <a href="https://publications.waset.org/abstracts/search?q=algorithm" title=" algorithm"> algorithm</a> </p> <a href="https://publications.waset.org/abstracts/134795/an-algorithm-for-herding-cows-by-a-swarm-of-quadcopters" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/134795.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">177</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">234</span> An Algorithm of Set-Based Particle Swarm Optimization with Status Memory for Traveling Salesman Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Takahiro%20Hino">Takahiro Hino</a>, <a href="https://publications.waset.org/abstracts/search?q=Michiharu%20Maeda"> Michiharu Maeda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Particle swarm optimization (PSO) is an optimization approach that achieves the social model of bird flocking and fish schooling. PSO works in continuous space and can solve continuous optimization problem with high quality. Set-based particle swarm optimization (SPSO) functions in discrete space by using a set. SPSO can solve combinatorial optimization problem with high quality and is successful to apply to the large-scale problem. In this paper, we present an algorithm of SPSO with status memory to decide the position based on the previous position for solving traveling salesman problem (TSP). In order to show the effectiveness of our approach. We examine SPSOSM for TSP compared to the existing algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=combinatorial%20optimization%20problems" title="combinatorial optimization problems">combinatorial optimization problems</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=set-based%20particle%20swarm%20optimization" title=" set-based particle swarm optimization"> set-based particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=traveling%20salesman%20problem" title=" traveling salesman problem"> traveling salesman problem</a> </p> <a href="https://publications.waset.org/abstracts/47282/an-algorithm-of-set-based-particle-swarm-optimization-with-status-memory-for-traveling-salesman-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47282.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">553</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">233</span> A Biomimetic Approach for the Multi-Objective Optimization of Kinetic Façade Design</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Do-Jin%20Jang">Do-Jin Jang</a>, <a href="https://publications.waset.org/abstracts/search?q=Sung-Ah%20Kim"> Sung-Ah Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A kinetic façade responds to user requirements and environmental conditions.  In designing a kinetic façade, kinetic patterns play a key role in determining its performance. This paper proposes a biomimetic method for the multi-objective optimization for kinetic façade design. The autonomous decentralized control system is combined with flocking algorithm. The flocking agents are autonomously reacting to sensor values and bring about kinetic patterns changing over time. A series of experiments were conducted to verify the potential and limitations of the flocking based decentralized control. As a result, it could show the highest performance balancing multiple objectives such as solar radiation and openness among the comparison group. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biomimicry" title="biomimicry">biomimicry</a>, <a href="https://publications.waset.org/abstracts/search?q=flocking%20algorithm" title=" flocking algorithm"> flocking algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=autonomous%20decentralized%20control" title=" autonomous decentralized control"> autonomous decentralized control</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a> </p> <a href="https://publications.waset.org/abstracts/71381/a-biomimetic-approach-for-the-multi-objective-optimization-of-kinetic-facade-design" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71381.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">517</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">232</span> Intelligent Swarm-Finding in Formation Control of Multi-Robots to Track a Moving Target</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anh%20Duc%20Dang">Anh Duc Dang</a>, <a href="https://publications.waset.org/abstracts/search?q=Joachim%20Horn"> Joachim Horn</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a new approach to control robots, which can quickly find their swarm while tracking a moving target through the obstacles of the environment. In this approach, an artificial potential field is generated between each free-robot and the virtual attractive point of the swarm. This artificial potential field will lead free-robots to their swarm. The swarm-finding of these free-robots dose not influence the general motion of their swarm and nor other robots. When one singular robot approaches the swarm then its swarm-search will finish, and it will further participate with its swarm to reach the position of the target. The connections between member-robots with their neighbours are controlled by the artificial attractive/repulsive force field between them to avoid collisions and keep the constant distances between them in ordered formation. The effectiveness of the proposed approach has been verified in simulations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=formation%20control" title="formation control">formation control</a>, <a href="https://publications.waset.org/abstracts/search?q=potential%20field%20method" title=" potential field method"> potential field method</a>, <a href="https://publications.waset.org/abstracts/search?q=obstacle%20avoidance" title=" obstacle avoidance"> obstacle avoidance</a>, <a href="https://publications.waset.org/abstracts/search?q=swarm%20intelligence" title=" swarm intelligence"> swarm intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-agent%20systems" title=" multi-agent systems"> multi-agent systems</a> </p> <a href="https://publications.waset.org/abstracts/3582/intelligent-swarm-finding-in-formation-control-of-multi-robots-to-track-a-moving-target" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3582.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">440</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">231</span> Searching k-Nearest Neighbors to be Appropriate under Gaming Environments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jae%20Moon%20Lee">Jae Moon Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In general, algorithms to find continuous k-nearest neighbors have been researched on the location based services, monitoring periodically the moving objects such as vehicles and mobile phone. Those researches assume the environment that the number of query points is much less than that of moving objects and the query points are not moved but fixed. In gaming environments, this problem is when computing the next movement considering the neighbors such as flocking, crowd and robot simulations. In this case, every moving object becomes a query point so that the number of query point is same to that of moving objects and the query points are also moving. In this paper, we analyze the performance of the existing algorithms focused on location based services how they operate under gaming environments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=flocking%20behavior" title="flocking behavior">flocking behavior</a>, <a href="https://publications.waset.org/abstracts/search?q=heterogeneous%20agents" title=" heterogeneous agents"> heterogeneous agents</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity" title=" similarity"> similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a> </p> <a href="https://publications.waset.org/abstracts/8228/searching-k-nearest-neighbors-to-be-appropriate-under-gaming-environments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8228.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">302</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">230</span> Improved Particle Swarm Optimization with Cellular Automata and Fuzzy Cellular Automata</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ramin%20Javadzadeh">Ramin Javadzadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The particle swarm optimization are Meta heuristic optimization method, which are used for clustering and pattern recognition applications are abundantly. These algorithms in multimodal optimization problems are more efficient than genetic algorithms. A major drawback in these algorithms is their slow convergence to global optimum and their weak stability can be considered in various running of these algorithms. In this paper, improved Particle swarm optimization is introduced for the first time to overcome its problems. The fuzzy cellular automata is used for improving the algorithm efficiently. The credibility of the proposed approach is evaluated by simulations, and it is shown that the proposed approach achieves better results can be achieved compared to the Particle swarm optimization algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cellular%20automata" title="cellular automata">cellular automata</a>, <a href="https://publications.waset.org/abstracts/search?q=cellular%20learning%20automata" title=" cellular learning automata"> cellular learning automata</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20search" title=" local search"> local search</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a> </p> <a href="https://publications.waset.org/abstracts/24739/improved-particle-swarm-optimization-with-cellular-automata-and-fuzzy-cellular-automata" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24739.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">607</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">229</span> Half-Circle Fuzzy Number Threshold Determination via Swarm Intelligence Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20W.%20Tsai">P. W. Tsai</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20W.%20Chen"> J. W. Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20W.%20Chen"> C. W. Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Y.%20Chen"> C. Y. Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, many researchers are involved in the field of fuzzy theory. However, there are still a lot of issues to be resolved. Especially on topics related to controller design such as the field of robot, artificial intelligence, and nonlinear systems etc. Besides fuzzy theory, algorithms in swarm intelligence are also a popular field for the researchers. In this paper, a concept of utilizing one of the swarm intelligence method, which is called Bacterial-GA Foraging, to find the stabilized common P matrix for the fuzzy controller system is proposed. An example is given in in the paper, as well. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=half-circle%20fuzzy%20numbers" title="half-circle fuzzy numbers">half-circle fuzzy numbers</a>, <a href="https://publications.waset.org/abstracts/search?q=predictions" title=" predictions"> predictions</a>, <a href="https://publications.waset.org/abstracts/search?q=swarm%20intelligence" title=" swarm intelligence"> swarm intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=Lyapunov%20method" title=" Lyapunov method"> Lyapunov method</a> </p> <a href="https://publications.waset.org/abstracts/11233/half-circle-fuzzy-number-threshold-determination-via-swarm-intelligence-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11233.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">685</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">228</span> Particle Swarm Optimization and Quantum Particle Swarm Optimization to Multidimensional Function Approximation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Diogo%20Silva">Diogo Silva</a>, <a href="https://publications.waset.org/abstracts/search?q=Fadul%20Rodor"> Fadul Rodor</a>, <a href="https://publications.waset.org/abstracts/search?q=Carlos%20Moraes"> Carlos Moraes</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work compares the results of multidimensional function approximation using two algorithms: the classical Particle Swarm Optimization (PSO) and the Quantum Particle Swarm Optimization (QPSO). These algorithms were both tested on three functions - The Rosenbrock, the Rastrigin, and the sphere functions - with different characteristics by increasing their number of dimensions. As a result, this study shows that the higher the function space, i.e. the larger the function dimension, the more evident the advantages of using the QPSO method compared to the PSO method in terms of performance and number of necessary iterations to reach the stop criterion. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=PSO" title="PSO">PSO</a>, <a href="https://publications.waset.org/abstracts/search?q=QPSO" title=" QPSO"> QPSO</a>, <a href="https://publications.waset.org/abstracts/search?q=function%20approximation" title=" function approximation"> function approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=AI" title=" AI"> AI</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=multidimensional%20functions" title=" multidimensional functions"> multidimensional functions</a> </p> <a href="https://publications.waset.org/abstracts/81790/particle-swarm-optimization-and-quantum-particle-swarm-optimization-to-multidimensional-function-approximation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81790.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">589</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">227</span> Solving the Set Covering Problem Using the Binary Cat Swarm Optimization Metaheuristic</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Broderick%20Crawford">Broderick Crawford</a>, <a href="https://publications.waset.org/abstracts/search?q=Ricardo%20Soto"> Ricardo Soto</a>, <a href="https://publications.waset.org/abstracts/search?q=Natalia%20Berrios"> Natalia Berrios</a>, <a href="https://publications.waset.org/abstracts/search?q=Eduardo%20Olguin"> Eduardo Olguin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a binary cat swarm optimization for solving the Set covering problem. The set covering problem is a well-known NP-hard problem with many practical applications, including those involving scheduling, production planning and location problems. Binary cat swarm optimization is a recent swarm metaheuristic technique based on the behavior of discrete cats. Domestic cats show the ability to hunt and are curious about moving objects. The cats have two modes of behavior: seeking mode and tracing mode. We illustrate this approach with 65 instances of the problem from the OR-Library. Moreover, we solve this problem with 40 new binarization techniques and we select the technical with the best results obtained. Finally, we make a comparison between results obtained in previous studies and the new binarization technique, that is, with roulette wheel as transfer function and V3 as discretization technique. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=binary%20cat%20swarm%20optimization" title="binary cat swarm optimization">binary cat swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=binarization%20methods" title=" binarization methods"> binarization methods</a>, <a href="https://publications.waset.org/abstracts/search?q=metaheuristic" title=" metaheuristic"> metaheuristic</a>, <a href="https://publications.waset.org/abstracts/search?q=set%20covering%20problem" title=" set covering problem"> set covering problem</a> </p> <a href="https://publications.waset.org/abstracts/47183/solving-the-set-covering-problem-using-the-binary-cat-swarm-optimization-metaheuristic" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47183.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">396</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">226</span> Optimized Algorithm for Particle Swarm Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fuzhang%20Zhao">Fuzhang Zhao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Particle swarm optimization (PSO) is becoming one of the most important swarm intelligent paradigms for solving global optimization problems. Although some progress has been made to improve PSO algorithms over the last two decades, additional work is still needed to balance parameters to achieve better numerical properties of accuracy, efficiency, and stability. In the optimal PSO algorithm, the optimal weightings of (√ 5 − 1)/2 and (3 − √5)/2 are used for the cognitive factor and the social factor, respectively. By the same token, the same optimal weightings have been applied for intensification searches and diversification searches, respectively. Perturbation and constriction effects are optimally balanced. Simulations of the de Jong, the Rosenbrock, and the Griewank functions show that the optimal PSO algorithm indeed achieves better numerical properties and outperforms the canonical PSO algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=diversification%20search" title="diversification search">diversification search</a>, <a href="https://publications.waset.org/abstracts/search?q=intensification%20search" title=" intensification search"> intensification search</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20weighting" title=" optimal weighting"> optimal weighting</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a> </p> <a href="https://publications.waset.org/abstracts/36390/optimized-algorithm-for-particle-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36390.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">582</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">225</span> Printed Thai Character Recognition Using Particle Swarm Optimization Algorithm </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Phawin%20Sangsuvan">Phawin Sangsuvan</a>, <a href="https://publications.waset.org/abstracts/search?q=Chutimet%20Srinilta"> Chutimet Srinilta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This Paper presents the applications of Particle Swarm Optimization (PSO) Method for Thai optical character recognition (OCR). OCR consists of the pre-processing, character recognition and post-processing. Before enter into recognition process. The Character must be “Prepped” by pre-processing process. The PSO is an optimization method that belongs to the swarm intelligence family based on the imitation of social behavior patterns of animals. Route of each particle is determined by an individual data among neighborhood particles. The interaction of the particles with neighbors is the advantage of Particle Swarm to determine the best solution. So PSO is interested by a lot of researchers in many difficult problems including character recognition. As the previous this research used a Projection Histogram to extract printed digits features and defined the simple Fitness Function for PSO. The results reveal that PSO gives 67.73% for testing dataset. So in the future there can be explored enhancement the better performance of PSO with improve the Fitness Function. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=character%20recognition" title="character recognition">character recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=histogram%20projection" title=" histogram projection"> histogram projection</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition%20techniques" title=" pattern recognition techniques "> pattern recognition techniques </a> </p> <a href="https://publications.waset.org/abstracts/25613/printed-thai-character-recognition-using-particle-swarm-optimization-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25613.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">477</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">224</span> An Online Priority-Configuration Algorithm for Obstacle Avoidance of the Unmanned Air Vehicles Swarm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lihua%20Zhu">Lihua Zhu</a>, <a href="https://publications.waset.org/abstracts/search?q=Jianfeng%20Du"> Jianfeng Du</a>, <a href="https://publications.waset.org/abstracts/search?q=Yu%20Wang"> Yu Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhiqiang%20Wu"> Zhiqiang Wu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Collision avoidance problems of a swarm of unmanned air vehicles (UAVs) flying in an obstacle-laden environment are investigated in this paper. Given that the UAV swarm needs to adapt to the obstacle distribution in dynamic operation, a priority configuration is designed to guide the UAVs to pass through the obstacles in turn. Based on the collision cone approach and the prediction of the collision time, a collision evaluation model is established to judge the urgency of the imminent collision of each UAV, and the evaluation result is used to assign the priority of each UAV to further instruct them going through the obstacles in descending order. At last, the simulation results provide the promising validation in terms of the efficiency and scalability of the proposed approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=UAV%20swarm" title="UAV swarm">UAV swarm</a>, <a href="https://publications.waset.org/abstracts/search?q=collision%20avoidance" title=" collision avoidance"> collision avoidance</a>, <a href="https://publications.waset.org/abstracts/search?q=complex%20environment" title=" complex environment"> complex environment</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20priority%20design" title=" online priority design"> online priority design</a> </p> <a href="https://publications.waset.org/abstracts/93689/an-online-priority-configuration-algorithm-for-obstacle-avoidance-of-the-unmanned-air-vehicles-swarm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/93689.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">214</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">223</span> Using Swarm Intelligence to Forecast Outcomes of English Premier League Matches</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hans%20Schumann">Hans Schumann</a>, <a href="https://publications.waset.org/abstracts/search?q=Colin%20Domnauer"> Colin Domnauer</a>, <a href="https://publications.waset.org/abstracts/search?q=Louis%20Rosenberg"> Louis Rosenberg</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, machine learning techniques were deployed on real-time human swarm data to forecast the likelihood of outcomes for English Premier League matches in the 2020/21 season. These techniques included ensemble models in combination with neural networks and were tested against an industry standard of Vegas Oddsmakers. Predictions made from the collective intelligence of human swarm participants managed to achieve a positive return on investment over a full season on matches, empirically proving the usefulness of a new artificial intelligence valuing human instinct and intelligence. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title="artificial intelligence">artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20science" title=" data science"> data science</a>, <a href="https://publications.waset.org/abstracts/search?q=English%20Premier%20League" title=" English Premier League"> English Premier League</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20swarming" title=" human swarming"> human swarming</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=sports%20betting" title=" sports betting"> sports betting</a>, <a href="https://publications.waset.org/abstracts/search?q=swarm%20intelligence" title=" swarm intelligence"> swarm intelligence</a> </p> <a href="https://publications.waset.org/abstracts/141854/using-swarm-intelligence-to-forecast-outcomes-of-english-premier-league-matches" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141854.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">213</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">222</span> Demand Forecasting Using Artificial Neural Networks Optimized by Particle Swarm Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Daham%20Owaid%20Matrood">Daham Owaid Matrood</a>, <a href="https://publications.waset.org/abstracts/search?q=Naqaa%20Hussein%20Raheem"> Naqaa Hussein Raheem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Evolutionary algorithms and Artificial neural networks (ANN) are two relatively young research areas that were subject to a steadily growing interest during the past years. This paper examines the use of Particle Swarm Optimization (PSO) to train a multi-layer feed forward neural network for demand forecasting. We use in this paper weekly demand data for packed cement and towels, which have been outfitted by the Northern General Company for Cement and General Company of prepared clothes respectively. The results showed superiority of trained neural networks using particle swarm optimization on neural networks trained using error back propagation because their ability to escape from local optima. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title="artificial neural network">artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=demand%20forecasting" title=" demand forecasting"> demand forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=weight%20optimization" title=" weight optimization"> weight optimization</a> </p> <a href="https://publications.waset.org/abstracts/45069/demand-forecasting-using-artificial-neural-networks-optimized-by-particle-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45069.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">452</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">221</span> An Enhanced Particle Swarm Optimization Algorithm for Multiobjective Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Houda%20Abadlia">Houda Abadlia</a>, <a href="https://publications.waset.org/abstracts/search?q=Nadia%20Smairi"> Nadia Smairi</a>, <a href="https://publications.waset.org/abstracts/search?q=Khaled%20Ghedira"> Khaled Ghedira</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Multiobjective Particle Swarm Optimization (MOPSO) has shown an effective performance for solving test functions and real-world optimization problems. However, this method has a premature convergence problem, which may lead to lack of diversity. In order to improve its performance, this paper presents a hybrid approach which embedded the MOPSO into the island model and integrated a local search technique, Variable Neighborhood Search, to enhance the diversity into the swarm. Experiments on two series of test functions have shown the effectiveness of the proposed approach. A comparison with other evolutionary algorithms shows that the proposed approach presented a good performance in solving multiobjective optimization problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title="particle swarm optimization">particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=migration" title=" migration"> migration</a>, <a href="https://publications.waset.org/abstracts/search?q=variable%20neighborhood%20search" title=" variable neighborhood search"> variable neighborhood search</a>, <a href="https://publications.waset.org/abstracts/search?q=multiobjective%20optimization" title=" multiobjective optimization"> multiobjective optimization</a> </p> <a href="https://publications.waset.org/abstracts/99544/an-enhanced-particle-swarm-optimization-algorithm-for-multiobjective-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99544.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">167</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">220</span> Software Architecture Optimization Using Swarm Intelligence Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arslan%20Ellahi">Arslan Ellahi</a>, <a href="https://publications.waset.org/abstracts/search?q=Syed%20Amjad%20Hussain"> Syed Amjad Hussain</a>, <a href="https://publications.waset.org/abstracts/search?q=Fawaz%20Saleem%20Bokhari"> Fawaz Saleem Bokhari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Optimization of software architecture can be done with respect to a quality attributes (QA). In this paper, there is an analysis of multiple research papers from different dimensions that have been used to classify those attributes. We have proposed a technique of swarm intelligence Meta heuristic ant colony optimization algorithm as a contribution to solve this critical optimization problem of software architecture. We have ranked quality attributes and run our algorithm on every QA, and then we will rank those on the basis of accuracy. At the end, we have selected the most accurate quality attributes. Ant colony algorithm is an effective algorithm and will perform best in optimizing the QA’s and ranking them. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=complexity" title="complexity">complexity</a>, <a href="https://publications.waset.org/abstracts/search?q=rapid%20evolution" title=" rapid evolution"> rapid evolution</a>, <a href="https://publications.waset.org/abstracts/search?q=swarm%20intelligence" title=" swarm intelligence"> swarm intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=dimensions" title=" dimensions"> dimensions</a> </p> <a href="https://publications.waset.org/abstracts/94992/software-architecture-optimization-using-swarm-intelligence-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/94992.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">261</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">219</span> Optimization of Cloud Classification Using Particle Swarm Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Riffi%20Mohammed%20Amine">Riffi Mohammed Amine</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A cloud is made up of small particles of liquid water or ice suspended in the atmosphere, which generally do not reach the ground. Various methods are used to classify clouds. This article focuses specifically on a technique known as particle swarm optimization (PSO), an AI approach inspired by the collective behaviors of animals living in groups, such as schools of fish and flocks of birds, and a method used to solve complex classification and optimization problems with approximate solutions. The proposed technique was evaluated using a series of second-generation METOSAT images taken by the MSG satellite. The acquired results indicate that the proposed method gave acceptable results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title="remote sensing">remote sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=clouds" title=" clouds"> clouds</a>, <a href="https://publications.waset.org/abstracts/search?q=meteorological%20image" title=" meteorological image"> meteorological image</a> </p> <a href="https://publications.waset.org/abstracts/192148/optimization-of-cloud-classification-using-particle-swarm-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192148.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">17</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">218</span> Uncovering Underwater Communication for Multi-Robot Applications via CORSICA</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Niels%20Grataloup">Niels Grataloup</a>, <a href="https://publications.waset.org/abstracts/search?q=Micael%20S.%20Couceiro"> Micael S. Couceiro</a>, <a href="https://publications.waset.org/abstracts/search?q=Manousos%20Valyrakis"> Manousos Valyrakis</a>, <a href="https://publications.waset.org/abstracts/search?q=Javier%20Escudero"> Javier Escudero</a>, <a href="https://publications.waset.org/abstracts/search?q=Patricia%20A.%20Vargas"> Patricia A. Vargas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper benchmarks the possible underwater communication technologies that can be integrated into a swarm of underwater robots by proposing an underwater robot simulator named CORSICA (Cross platfORm wireleSs communICation simulator). Underwater exploration relies increasingly on the use of mobile robots, called Autonomous Underwater Vehicles (AUVs). These robots are able to reach goals in harsh underwater environments without resorting to human divers. The introduction of swarm robotics in these scenarios would facilitate the accomplishment of complex tasks with lower costs. However, swarm robotics requires implementation of communication systems to be operational and have a non-deterministic behaviour. Inter-robot communication is one of the key challenges in swarm robotics, especially in underwater scenarios, as communication must cope with severe restrictions and perturbations. This paper starts by presenting a list of the underwater propagation models of acoustic and electromagnetic waves, it also reviews existing transmitters embedded in current robots and simulators. It then proposes CORSICA, which allows validating the choices in terms of protocol and communication strategies, whether they are robot-robot or human-robot interactions. This paper finishes with a presentation of possible integration according to the literature review, and the potential to get CORSICA at an industrial level. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=underwater%20simulator" title="underwater simulator">underwater simulator</a>, <a href="https://publications.waset.org/abstracts/search?q=robot-robot%20underwater%20communication" title=" robot-robot underwater communication"> robot-robot underwater communication</a>, <a href="https://publications.waset.org/abstracts/search?q=swarm%20robotics" title=" swarm robotics"> swarm robotics</a>, <a href="https://publications.waset.org/abstracts/search?q=transceiver%20and%20communication%20models" title=" transceiver and communication models"> transceiver and communication models</a> </p> <a href="https://publications.waset.org/abstracts/43591/uncovering-underwater-communication-for-multi-robot-applications-via-corsica" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43591.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">301</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">217</span> Time-Domain Expressions for Bridge Self-Excited Aerodynamic Forces by Modified Particle Swarm Optimizer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hao-Su%20Liu">Hao-Su Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Jun-Qing%20Lei"> Jun-Qing Lei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study introduces the theory of modified particle swarm optimizer and its application in time-domain expressions for bridge self-excited aerodynamic forces. Based on the indicial function expression and the rational function expression in time-domain expression for bridge self-excited aerodynamic forces, the characteristics of the two methods, i.e. the modified particle swarm optimizer and conventional search method, are compared in flutter derivatives’ fitting process. Theoretical analysis and numerical results indicate that adopting whether the indicial function expression or the rational function expression, the fitting flutter derivatives obtained by modified particle swarm optimizer have better goodness of fit with ones obtained from experiment. As to the flutter derivatives which have higher nonlinearity, the self-excited aerodynamic forces, using the flutter derivatives obtained through modified particle swarm optimizer fitting process, are much closer to the ones simulated by the experimental. The modified particle swarm optimizer was used to recognize the parameters of time-domain expressions for flutter derivatives of an actual long-span highway-railway truss bridge with double decks at the wind attack angle of 0°, -3° and +3°. It was found that this method could solve the bounded problems of attenuation coefficient effectively in conventional search method, and had the ability of searching in unboundedly area. Accordingly, this study provides a method for engineering industry to frequently and efficiently obtain the time-domain expressions for bridge self-excited aerodynamic forces. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=time-domain%20expressions" title="time-domain expressions">time-domain expressions</a>, <a href="https://publications.waset.org/abstracts/search?q=bridge%20self-excited%20aerodynamic%20forces" title=" bridge self-excited aerodynamic forces"> bridge self-excited aerodynamic forces</a>, <a href="https://publications.waset.org/abstracts/search?q=modified%20particle%20swarm%20optimizer" title=" modified particle swarm optimizer"> modified particle swarm optimizer</a>, <a href="https://publications.waset.org/abstracts/search?q=long-span%20highway-railway%20truss%20bridge" title=" long-span highway-railway truss bridge"> long-span highway-railway truss bridge</a> </p> <a href="https://publications.waset.org/abstracts/69848/time-domain-expressions-for-bridge-self-excited-aerodynamic-forces-by-modified-particle-swarm-optimizer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69848.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">314</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">216</span> Parallel Particle Swarm Optimization Optimized LDI Controller with Lyapunov Stability Criterion for Nonlinear Structural Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20W.%20Tsai">P. W. Tsai</a>, <a href="https://publications.waset.org/abstracts/search?q=W.%20L.%20Hong"> W. L. Hong</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20W.%20Chen"> C. W. Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Y.%20Chen"> C. Y. Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a neural network (NN) based approach represent a nonlinear Tagagi-Sugeno (T-S) system. A linear differential inclusion (LDI) state-space representation is utilized to deal with the NN models. Taking advantage of the LDI representation, the stability conditions and controller design are derived for a class of nonlinear structural systems. Moreover, the concept of utilizing the Parallel Particle Swarm Optimization (PPSO) algorithm to solve the common P matrix under the stability criteria is given in this paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lyapunov%20stability" title="Lyapunov stability">Lyapunov stability</a>, <a href="https://publications.waset.org/abstracts/search?q=parallel%20particle%20swarm%20optimization" title=" parallel particle swarm optimization"> parallel particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20differential%20inclusion" title=" linear differential inclusion"> linear differential inclusion</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a> </p> <a href="https://publications.waset.org/abstracts/6974/parallel-particle-swarm-optimization-optimized-ldi-controller-with-lyapunov-stability-criterion-for-nonlinear-structural-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6974.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">656</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">215</span> Investigating Data Normalization Techniques in Swarm Intelligence Forecasting for Energy Commodity Spot Price</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuhanis%20Yusof">Yuhanis Yusof</a>, <a href="https://publications.waset.org/abstracts/search?q=Zuriani%20Mustaffa"> Zuriani Mustaffa</a>, <a href="https://publications.waset.org/abstracts/search?q=Siti%20Sakira%20Kamaruddin"> Siti Sakira Kamaruddin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data mining is a fundamental technique in identifying patterns from large data sets. The extracted facts and patterns contribute in various domains such as marketing, forecasting, and medical. Prior to that, data are consolidated so that the resulting mining process may be more efficient. This study investigates the effect of different data normalization techniques, which are Min-max, Z-score, and decimal scaling, on Swarm-based forecasting models. Recent swarm intelligence algorithms employed includes the Grey Wolf Optimizer (GWO) and Artificial Bee Colony (ABC). Forecasting models are later developed to predict the daily spot price of crude oil and gasoline. Results showed that GWO works better with Z-score normalization technique while ABC produces better accuracy with the Min-Max. Nevertheless, the GWO is more superior that ABC as its model generates the highest accuracy for both crude oil and gasoline price. Such a result indicates that GWO is a promising competitor in the family of swarm intelligence algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20bee%20colony" title="artificial bee colony">artificial bee colony</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20normalization" title=" data normalization"> data normalization</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=Grey%20Wolf%20optimizer" title=" Grey Wolf optimizer"> Grey Wolf optimizer</a> </p> <a href="https://publications.waset.org/abstracts/18294/investigating-data-normalization-techniques-in-swarm-intelligence-forecasting-for-energy-commodity-spot-price" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18294.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">476</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">214</span> Improvement Image Summarization using Image Processing and Particle swarm optimization Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hooman%20Torabifard">Hooman Torabifard</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the last few years, with the progress of technology and computers and artificial intelligence entry into all kinds of scientific and industrial fields, the lifestyles of human life have changed and in general, the way of humans live on earth has many changes and development. Until now, some of the changes has occurred in the context of digital images and image processing and still continues. However, besides all the benefits, there have been disadvantages. One of these disadvantages is the multiplicity of images with high volume and data; the focus of this paper is on improving and developing a method for summarizing and enhancing the productivity of these images. The general method used for this purpose in this paper consists of a set of methods based on data obtained from image processing and using the PSO (Particle swarm optimization) algorithm. In the remainder of this paper, the method used is elaborated in detail. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20summarization" title="image summarization">image summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20threshold" title=" image threshold"> image threshold</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a> </p> <a href="https://publications.waset.org/abstracts/138289/improvement-image-summarization-using-image-processing-and-particle-swarm-optimization-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138289.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">133</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">213</span> Swarm Optimization of Unmanned Vehicles and Object Localization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Venkataramana%20Sovenahalli%20Badigar">Venkataramana Sovenahalli Badigar</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20M.%20Suryakanth"> B. M. Suryakanth</a>, <a href="https://publications.waset.org/abstracts/search?q=Akshar%20Prasanna"> Akshar Prasanna</a>, <a href="https://publications.waset.org/abstracts/search?q=Karthik%20Veeramalai"> Karthik Veeramalai</a>, <a href="https://publications.waset.org/abstracts/search?q=Vishwak%20Ram%20Vishwak%20Ram"> Vishwak Ram Vishwak Ram</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Technological advances have led to widespread autonomy in vehicles. Empowering these autonomous with the intelligence to cooperate amongst themselves leads to a more efficient use of the resources available to them. This paper proposes a demonstration of a swarm algorithm implemented on a group of autonomous vehicles. The demonstration involves two ground bots and an aerial drone which cooperate amongst them to locate an object of interest. The object of interest is modelled using a high-intensity light source which acts as a beacon. The ground bots are light sensitive and move towards the beacon. The ground bots and the drone traverse in random paths and jointly locate the beacon. This finds application in various scenarios in where human interference is difficult such as search and rescue during natural disasters, delivering crucial packages in perilous situations, etc. Experimental results show that the modified swarm algorithm implemented in this system has better performance compared to fully random based moving algorithm for object localization and tracking. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=swarm%20algorithm" title="swarm algorithm">swarm algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20localization" title=" object localization"> object localization</a>, <a href="https://publications.waset.org/abstracts/search?q=ground%20bots" title=" ground bots"> ground bots</a>, <a href="https://publications.waset.org/abstracts/search?q=drone" title=" drone"> drone</a>, <a href="https://publications.waset.org/abstracts/search?q=beacon" title=" beacon"> beacon</a> </p> <a href="https://publications.waset.org/abstracts/52839/swarm-optimization-of-unmanned-vehicles-and-object-localization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52839.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">257</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">212</span> A Hybrid Particle Swarm Optimization-Nelder- Mead Algorithm (PSO-NM) for Nelson-Siegel- Svensson Calibration</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sofia%20Ayouche">Sofia Ayouche</a>, <a href="https://publications.waset.org/abstracts/search?q=Rachid%20Ellaia"> Rachid Ellaia</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajae%20Aboulaich"> Rajae Aboulaich</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Today, insurers may use the yield curve as an indicator evaluation of the profit or the performance of their portfolios; therefore, they modeled it by one class of model that has the ability to fit and forecast the future term structure of interest rates. This class of model is the Nelson-Siegel-Svensson model. Unfortunately, many authors have reported a lot of difficulties when they want to calibrate the model because the optimization problem is not convex and has multiple local optima. In this context, we implement a hybrid Particle Swarm optimization and Nelder Mead algorithm in order to minimize by least squares method, the difference between the zero-coupon curve and the NSS curve. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimization" title="optimization">optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=zero-coupon%20curve" title=" zero-coupon curve"> zero-coupon curve</a>, <a href="https://publications.waset.org/abstracts/search?q=Nelson-Siegel-Svensson" title=" Nelson-Siegel-Svensson"> Nelson-Siegel-Svensson</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=Nelder-Mead%20algorithm" title=" Nelder-Mead algorithm"> Nelder-Mead algorithm</a> </p> <a href="https://publications.waset.org/abstracts/48619/a-hybrid-particle-swarm-optimization-nelder-mead-algorithm-pso-nm-for-nelson-siegel-svensson-calibration" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48619.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">430</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">211</span> Optimal Allocation of Distributed Generation Sources for Loss Reduction and Voltage Profile Improvement by Using Particle Swarm Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Zaheer%20Babar">Muhammad Zaheer Babar</a>, <a href="https://publications.waset.org/abstracts/search?q=Amer%20Kashif"> Amer Kashif</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Rizwan%20Javed"> Muhammad Rizwan Javed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays distributed generation integration is best way to overcome the increasing load demand. Optimal allocation of distributed generation plays a vital role in reducing system losses and improves voltage profile. In this paper, a Meta heuristic technique is proposed for allocation of DG in order to reduce power losses and improve voltage profile. The proposed technique is based on Multi Objective Particle Swarm optimization. Fewer control parameters are needed in this algorithm. Modification is made in search space of PSO. The effectiveness of proposed technique is tested on IEEE 33 bus test system. Single DG as well as multiple DG scenario is adopted for proposed method. Proposed method is more effective as compared to other Meta heuristic techniques and gives better results regarding system losses and voltage profile. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Distributed%20generation%20%28DG%29" title="Distributed generation (DG)">Distributed generation (DG)</a>, <a href="https://publications.waset.org/abstracts/search?q=Multi%20Objective%20Particle%20Swarm%20Optimization%20%28MOPSO%29" title=" Multi Objective Particle Swarm Optimization (MOPSO)"> Multi Objective Particle Swarm Optimization (MOPSO)</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization%20%28PSO%29" title=" particle swarm optimization (PSO)"> particle swarm optimization (PSO)</a>, <a href="https://publications.waset.org/abstracts/search?q=IEEE%20standard%20Test%20System" title=" IEEE standard Test System"> IEEE standard Test System</a> </p> <a href="https://publications.waset.org/abstracts/42467/optimal-allocation-of-distributed-generation-sources-for-loss-reduction-and-voltage-profile-improvement-by-using-particle-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42467.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">454</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">210</span> Unified Coordinate System Approach for Swarm Search Algorithms in Global Information Deficit Environments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rohit%20Dey">Rohit Dey</a>, <a href="https://publications.waset.org/abstracts/search?q=Sailendra%20Karra"> Sailendra Karra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper aims at solving the problem of multi-target searching in a Global Positioning System (GPS) denied environment using swarm robots with limited sensing and communication abilities. Typically, existing swarm-based search algorithms rely on the presence of a global coordinate system (vis-à-vis, GPS) that is shared by the entire swarm which, in turn, limits its application in a real-world scenario. This can be attributed to the fact that robots in a swarm need to share information among themselves regarding their location and signal from targets to decide their future course of action but this information is only meaningful when they all share the same coordinate frame. The paper addresses this very issue by eliminating any dependency of a search algorithm on the need of a predetermined global coordinate frame by the unification of the relative coordinate of individual robots when within the communication range, therefore, making the system more robust in real scenarios. Our algorithm assumes that all the robots in the swarm are equipped with range and bearing sensors and have limited sensing range and communication abilities. Initially, every robot maintains their relative coordinate frame and follow Levy walk random exploration until they come in range with other robots. When two or more robots are within communication range, they share sensor information and their location w.r.t. their coordinate frames based on which we unify their coordinate frames. Now they can share information about the areas that were already explored, information about the surroundings, and target signal from their location to make decisions about their future movement based on the search algorithm. During the process of exploration, there can be several small groups of robots having their own coordinate systems but eventually, it is expected for all the robots to be under one global coordinate frame where they can communicate information on the exploration area following swarm search techniques. Using the proposed method, swarm-based search algorithms can work in a real-world scenario without GPS and any initial information about the size and shape of the environment. Initial simulation results show that running our modified-Particle Swarm Optimization (PSO) without global information we can still achieve the desired results that are comparable to basic PSO working with GPS. In the full paper, we plan on doing the comparison study between different strategies to unify the coordinate system and to implement them on other bio-inspired algorithms, to work in GPS denied environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bio-inspired%20search%20algorithms" title="bio-inspired search algorithms">bio-inspired search algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=decentralized%20control" title=" decentralized control"> decentralized control</a>, <a href="https://publications.waset.org/abstracts/search?q=GPS%20denied%20environment" title=" GPS denied environment"> GPS denied environment</a>, <a href="https://publications.waset.org/abstracts/search?q=swarm%20robotics" title=" swarm robotics"> swarm robotics</a>, <a href="https://publications.waset.org/abstracts/search?q=target%20searching" title=" target searching"> target searching</a>, <a href="https://publications.waset.org/abstracts/search?q=unifying%20coordinate%20systems" title=" unifying coordinate systems"> unifying coordinate systems</a> </p> <a href="https://publications.waset.org/abstracts/128283/unified-coordinate-system-approach-for-swarm-search-algorithms-in-global-information-deficit-environments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/128283.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">137</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">209</span> Thinned Elliptical Cylindrical Antenna Array Synthesis Using Particle Swarm Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rajesh%20Bera">Rajesh Bera</a>, <a href="https://publications.waset.org/abstracts/search?q=Durbadal%20Mandal"> Durbadal Mandal</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajib%20Kar"> Rajib Kar</a>, <a href="https://publications.waset.org/abstracts/search?q=Sakti%20P.%20Ghoshal"> Sakti P. Ghoshal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes optimal thinning of an Elliptical Cylindrical Array (ECA) of uniformly excited isotropic antennas which can generate directive beam with minimum relative Side Lobe Level (SLL). The Particle Swarm Optimization (PSO) method, which represents a new approach for optimization problems in electromagnetic, is used in the optimization process. The PSO is used to determine the optimal set of ‘ON-OFF’ elements that provides a radiation pattern with maximum SLL reduction. Optimization is done without prefixing the value of First Null Beam Width (FNBW). The variation of SLL with element spacing of thinned array is also reported. Simulation results show that the number of array elements can be reduced by more than 50% of the total number of elements in the array with a simultaneous reduction in SLL to less than -27dB. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=thinned%20array" title="thinned array">thinned array</a>, <a href="https://publications.waset.org/abstracts/search?q=Particle%20Swarm%20Optimization" title=" Particle Swarm Optimization"> Particle Swarm Optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=Elliptical%20Cylindrical%20Array" title=" Elliptical Cylindrical Array"> Elliptical Cylindrical Array</a>, <a href="https://publications.waset.org/abstracts/search?q=Side%20Lobe%20Label." title=" Side Lobe Label."> Side Lobe Label.</a> </p> <a href="https://publications.waset.org/abstracts/4068/thinned-elliptical-cylindrical-antenna-array-synthesis-using-particle-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4068.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">443</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">208</span> Screen Method of Distributed Cooperative Navigation Factors for Unmanned Aerial Vehicle Swarm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Can%20Zhang">Can Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Qun%20Li"> Qun Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Yonglin%20Lei"> Yonglin Lei</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhi%20Zhu"> Zhi Zhu</a>, <a href="https://publications.waset.org/abstracts/search?q=Dong%20Guo"> Dong Guo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Aiming at the problem of factor screen in distributed collaborative navigation of dense UAV swarm, an efficient distributed collaborative navigation factor screen method is proposed. The method considered the balance between computing load and positioning accuracy. The proposed algorithm utilized the factor graph model to implement a distributed collaborative navigation algorithm. The GNSS information of the UAV itself and the ranging information between the UAVs are used as the positioning factors. In this distributed scheme, a local factor graph is established for each UAV. The positioning factors of nodes with good geometric position distribution and small variance are selected to participate in the navigation calculation. To demonstrate and verify the proposed methods, the simulation and experiments in different scenarios are performed in this research. Simulation results show that the proposed scheme achieves a good balance between the computing load and positioning accuracy in the distributed cooperative navigation calculation of UAV swarm. This proposed algorithm has important theoretical and practical value for both industry and academic areas. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=screen%20method" title="screen method">screen method</a>, <a href="https://publications.waset.org/abstracts/search?q=cooperative%20positioning%20system" title=" cooperative positioning system"> cooperative positioning system</a>, <a href="https://publications.waset.org/abstracts/search?q=UAV%20swarm" title=" UAV swarm"> UAV swarm</a>, <a href="https://publications.waset.org/abstracts/search?q=factor%20graph" title=" factor graph"> factor graph</a>, <a href="https://publications.waset.org/abstracts/search?q=cooperative%20navigation" title=" cooperative navigation"> cooperative navigation</a> </p> <a href="https://publications.waset.org/abstracts/166690/screen-method-of-distributed-cooperative-navigation-factors-for-unmanned-aerial-vehicle-swarm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/166690.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">79</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">207</span> Three-Dimensional Off-Line Path Planning for Unmanned Aerial Vehicle Using Modified Particle Swarm Optimization </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lana%20Dalawr%20Jalal">Lana Dalawr Jalal </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper addresses the problem of ofﬂine path planning for Unmanned Aerial Vehicles (UAVs) in complex three-dimensional environment with obstacles, which is modelled by 3D Cartesian grid system. Path planning for UAVs require the computational intelligence methods to move aerial vehicles along the flight path effectively to target while avoiding obstacles. In this paper Modified Particle Swarm Optimization (MPSO) algorithm is applied to generate the optimal collision free 3D flight path for UAV. The simulations results clearly demonstrate effectiveness of the proposed algorithm in guiding UAV to the final destination by providing optimal feasible path quickly and effectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=obstacle%20avoidance" title="obstacle avoidance">obstacle avoidance</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=three-dimensional%20path%20planning%20unmanned%20aerial%20vehicles" title=" three-dimensional path planning unmanned aerial vehicles"> three-dimensional path planning unmanned aerial vehicles</a> </p> <a href="https://publications.waset.org/abstracts/26160/three-dimensional-off-line-path-planning-for-unmanned-aerial-vehicle-using-modified-particle-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26160.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">410</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=flocking%20swarm&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=flocking%20swarm&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=flocking%20swarm&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=flocking%20swarm&page=5">5</a></li> <li class="page-item"><a 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