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intelligence</title> <meta name="description" content="Search results for: swarm intelligence"> <meta name="keywords" content="swarm intelligence"> <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 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action="https://publications.waset.org/abstracts/search"> <div 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="swarm intelligence"> <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> 1750</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: swarm intelligence</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1750</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">1749</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">212</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">1748</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">1747</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">1746</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">1745</span> Discrete Breeding Swarm for Cost Minimization of Parallel Job Shop Scheduling Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tarek%20Aboueldahab">Tarek Aboueldahab</a>, <a href="https://publications.waset.org/abstracts/search?q=Hanan%20Farag"> Hanan Farag</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Parallel Job Shop Scheduling Problem (JSP) is a multi-objective and multi constrains NP- optimization problem. Traditional Artificial Intelligence techniques have been widely used; however, they could be trapped into the local minimum without reaching the optimum solution, so we propose a hybrid Artificial Intelligence model (AI) with Discrete Breeding Swarm (DBS) added to traditional Artificial Intelligence to avoid this trapping. This model is applied in the cost minimization of the Car Sequencing and Operator Allocation (CSOA) problem. The practical experiment shows that our model outperforms other techniques in cost minimization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=parallel%20job%20shop%20scheduling%20problem" title="parallel job shop scheduling problem">parallel job shop scheduling problem</a>, <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=discrete%20breeding%20swarm" title=" discrete breeding swarm"> discrete breeding swarm</a>, <a href="https://publications.waset.org/abstracts/search?q=car%20sequencing%20and%20operator%20allocation" title=" car sequencing and operator allocation"> car sequencing and operator allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=cost%20minimization" title=" cost minimization"> cost minimization</a> </p> <a href="https://publications.waset.org/abstracts/132701/discrete-breeding-swarm-for-cost-minimization-of-parallel-job-shop-scheduling-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132701.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">187</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">1744</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">475</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">1743</span> Wind Speed Prediction Using Passive Aggregation Artificial Intelligence Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tarek%20Aboueldahab">Tarek Aboueldahab</a>, <a href="https://publications.waset.org/abstracts/search?q=Amin%20Mohamed%20Nassar"> Amin Mohamed Nassar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wind energy is a fluctuating energy source unlike conventional power plants, thus, it is necessary to accurately predict short term wind speed to integrate wind energy in the electricity supply structure. To do so, we present a hybrid artificial intelligence model of short term wind speed prediction based on passive aggregation of the particle swarm optimization and neural networks. As a result, improvement of the prediction accuracy is obviously obtained compared to the standard artificial intelligence method. <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=neural%20networks" title=" neural networks"> neural networks</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=passive%20aggregation" title=" passive aggregation"> passive aggregation</a>, <a href="https://publications.waset.org/abstracts/search?q=wind%20speed%20prediction" title=" wind speed prediction"> wind speed prediction</a> </p> <a href="https://publications.waset.org/abstracts/45705/wind-speed-prediction-using-passive-aggregation-artificial-intelligence-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45705.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">450</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">1742</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">1741</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">655</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">1740</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">1739</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">1738</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> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1737</span> Discrete Swarm with Passive Congregation for Cost Minimization of the Multiple Vehicle Routing Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tarek%20Aboueldahab">Tarek Aboueldahab</a>, <a href="https://publications.waset.org/abstracts/search?q=Hanan%20Farag"> Hanan Farag</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cost minimization of Multiple Vehicle Routing Problem becomes a critical issue in the field of transportation because it is NP-hard optimization problem and the search space is complex. Many researches use the hybridization of artificial intelligence (AI) models to solve this problem; however, it can not guarantee to reach the best solution due to the difficulty of searching the whole search space. To overcome this problem, we introduce the hybrid model of Discrete Particle Swarm Optimization (DPSO) with a passive congregation which enable searching the whole search space to compromise between both local and global search. The practical experiment shows that our model obviously outperforms other hybrid models in cost minimization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cost%20minimization" title="cost minimization">cost minimization</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-vehicle%20routing%20problem" title=" multi-vehicle routing problem"> multi-vehicle routing problem</a>, <a href="https://publications.waset.org/abstracts/search?q=passive%20congregation" title=" passive congregation"> passive congregation</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20swarm" title=" discrete swarm"> discrete swarm</a>, <a href="https://publications.waset.org/abstracts/search?q=passive%20congregation" title=" passive congregation"> passive congregation</a> </p> <a href="https://publications.waset.org/abstracts/157025/discrete-swarm-with-passive-congregation-for-cost-minimization-of-the-multiple-vehicle-routing-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157025.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">98</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">1736</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">606</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">1735</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">176</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">1734</span> A Parallel Implementation of Artificial Bee Colony Algorithm within CUDA Architecture</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Selcuk%20Aslan">Selcuk Aslan</a>, <a href="https://publications.waset.org/abstracts/search?q=Dervis%20Karaboga"> Dervis Karaboga</a>, <a href="https://publications.waset.org/abstracts/search?q=Celal%20Ozturk"> Celal Ozturk</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Artificial Bee Colony (ABC) algorithm is one of the most successful swarm intelligence based metaheuristics. It has been applied to a number of constrained or unconstrained numerical and combinatorial optimization problems. In this paper, we presented a parallelized version of ABC algorithm by adapting employed and onlooker bee phases to the Compute Unified Device Architecture (CUDA) platform which is a graphical processing unit (GPU) programming environment by NVIDIA. The execution speed and obtained results of the proposed approach and sequential version of ABC algorithm are compared on functions that are typically used as benchmarks for optimization algorithms. Tests on standard benchmark functions with different colony size and number of parameters showed that proposed parallelization approach for ABC algorithm decreases the execution time consumed by the employed and onlooker bee phases in total and achieved similar or better quality of the results compared to the standard sequential implementation of the ABC algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Artificial%20Bee%20Colony%20algorithm" title="Artificial Bee Colony algorithm">Artificial Bee Colony algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=GPU%20computing" title=" GPU computing"> GPU computing</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=parallelization" title=" parallelization"> parallelization</a> </p> <a href="https://publications.waset.org/abstracts/44876/a-parallel-implementation-of-artificial-bee-colony-algorithm-within-cuda-architecture" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44876.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">378</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">1733</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">1732</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">1731</span> [Keynote]: No-Trust-Zone Architecture for Securing Supervisory Control and Data Acquisition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Michael%20Okeke">Michael Okeke</a>, <a href="https://publications.waset.org/abstracts/search?q=Andrew%20Blyth"> Andrew Blyth</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Supervisory Control And Data Acquisition (SCADA) as the state of the art Industrial Control Systems (ICS) are used in many different critical infrastructures, from smart home to energy systems and from locomotives train system to planes. Security of SCADA systems is vital since many lives depend on it for daily activities and deviation from normal operation could be disastrous to the environment as well as lives. This paper describes how No-Trust-Zone (NTZ) architecture could be incorporated into SCADA Systems in order to reduce the chances of malicious intent. The architecture is made up of two distinctive parts which are; the field devices such as; sensors, PLCs pumps, and actuators. The second part of the architecture is designed following lambda architecture, which is made up of a detection algorithm based on Particle Swarm Optimization (PSO) and Hadoop framework for data processing and storage. Apache Spark will be a part of the lambda architecture for real-time analysis of packets for anomalies detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=industrial%20control%20system%20%28ics" title="industrial control system (ics">industrial control system (ics</a>, <a href="https://publications.waset.org/abstracts/search?q=no-trust-zone%20%28ntz%29" title=" no-trust-zone (ntz)"> no-trust-zone (ntz)</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimisation%20%28pso%29" title=" particle swarm optimisation (pso)"> particle swarm optimisation (pso)</a>, <a href="https://publications.waset.org/abstracts/search?q=supervisory%20control%20and%20data%20acquisition%20%28scada%29" title=" supervisory control and data acquisition (scada)"> supervisory control and data acquisition (scada)</a>, <a href="https://publications.waset.org/abstracts/search?q=swarm%20intelligence%20%28SI%29" title=" swarm intelligence (SI)"> swarm intelligence (SI)</a> </p> <a href="https://publications.waset.org/abstracts/53994/keynote-no-trust-zone-architecture-for-securing-supervisory-control-and-data-acquisition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53994.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">345</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">1730</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">581</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">1729</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">552</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">1728</span> A Novel Approach of NPSO on Flexible Logistic (S-Shaped) Model for Software Reliability Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pooja%20Rani">Pooja Rani</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20S.%20Mahapatra"> G. S. Mahapatra</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20K.%20Pandey"> S. K. Pandey</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose a novel approach of Neural Network and Particle Swarm Optimization methods for software reliability prediction. We first explain how to apply compound function in neural network so that we can derive a Flexible Logistic (S-shaped) Growth Curve (FLGC) model. This model mathematically represents software failure as a random process and can be used to evaluate software development status during testing. To avoid trapping in local minima, we have applied Particle Swarm Optimization method to train proposed model using failure test data sets. We drive our proposed model using computational based intelligence modeling. Thus, proposed model becomes Neuro-Particle Swarm Optimization (NPSO) model. We do test result with different inertia weight to update particle and update velocity. We obtain result based on best inertia weight compare along with Personal based oriented PSO (pPSO) help to choose local best in network neighborhood. The applicability of proposed model is demonstrated through real time test data failure set. The results obtained from experiments show that the proposed model has a fairly accurate prediction capability in software reliability. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=software%20reliability" title="software reliability">software reliability</a>, <a href="https://publications.waset.org/abstracts/search?q=flexible%20logistic%20growth%20curve%20model" title=" flexible logistic growth curve model"> flexible logistic growth curve model</a>, <a href="https://publications.waset.org/abstracts/search?q=software%20cumulative%20failure%20prediction" title=" software cumulative failure prediction"> software cumulative failure prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</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/36601/a-novel-approach-of-npso-on-flexible-logistic-s-shaped-model-for-software-reliability-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36601.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">344</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">1727</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">1726</span> Optimal Injected Current Control for Shunt Active Power Filter Using Artificial Intelligence</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Brahim%20Berbaoui">Brahim Berbaoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a new particle swarm optimization (PSO) based method is proposed for the implantation of optimal harmonic power flow in power systems. In this algorithm approach, proportional integral controller for reference compensating currents of active power filter is performed in order to minimize the total harmonic distortion (THD). The simulation results show that the new control method using PSO approach is not only easy to be implanted, but also very effective in reducing the unwanted harmonics and compensating reactive power. The studies carried out have been accomplished using the MATLAB Simulink Power System Toolbox. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=shunt%20active%20power%20filter" title="shunt active power filter">shunt active power filter</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20quality" title=" power quality"> power quality</a>, <a href="https://publications.waset.org/abstracts/search?q=current%20control" title=" current control"> current control</a>, <a href="https://publications.waset.org/abstracts/search?q=proportional%20integral%20controller" title=" proportional integral controller"> proportional integral controller</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/19698/optimal-injected-current-control-for-shunt-active-power-filter-using-artificial-intelligence" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19698.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">615</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">1725</span> Traffic Signal Control Using Citizens’ Knowledge through the Wisdom of the Crowd</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aleksandar%20Jovanovic">Aleksandar Jovanovic</a>, <a href="https://publications.waset.org/abstracts/search?q=Katarina%20Kukic"> Katarina Kukic</a>, <a href="https://publications.waset.org/abstracts/search?q=Ana%20Uzelac"> Ana Uzelac</a>, <a href="https://publications.waset.org/abstracts/search?q=Dusan%20Teodorovic"> Dusan Teodorovic</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wisdom of the Crowd (WoC) is a decentralized method that uses the collective intelligence of humans. Individual guesses may be far from the target, but when considered as a group, they converge on optimal solutions for a given problem. We will utilize WoC to address the challenge of controlling traffic lights within intersections from the streets of Kragujevac, Serbia. The problem at hand falls within the category of NP-hard problems. We will employ an algorithm that leverages the swarm intelligence of bees: Bee Colony Optimization (BCO). Data regarding traffic signal timing at a single intersection will be gathered from citizens through a survey. Results obtained in that manner will be compared to the BCO results for different traffic scenarios. We will use Vissim traffic simulation software as a tool to compare the performance of bees’ and humans’ collective intelligence. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wisdom%20of%20the%20crowd" title="wisdom of the crowd">wisdom of the crowd</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20signal%20control" title=" traffic signal control"> traffic signal control</a>, <a href="https://publications.waset.org/abstracts/search?q=combinatorial%20optimization" title=" combinatorial optimization"> combinatorial optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=bee%20colony%20optimization" title=" bee colony optimization"> bee colony optimization</a> </p> <a href="https://publications.waset.org/abstracts/174794/traffic-signal-control-using-citizens-knowledge-through-the-wisdom-of-the-crowd" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/174794.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">108</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">1724</span> Correlation between Potential Intelligence Explanatory Study in the Perspective of Multiple Intelligence Theory by Using Dermatoglyphics and Culture Approaches </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Efnie%20Indrianie">Efnie Indrianie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Potential Intelligence constitutes one essential factor in every individual. This intelligence can be a provision for the development of Performance Intelligence if it is supported by surrounding environment. Fingerprint analysis is a method in recognizing this Potential Intelligence. This method is grounded on pattern and number of finger print outlines that are assumed symmetrical with the number of nerves in our brain, in which these areas have their own function among another. These brain’s functions are later being transposed into intelligence components in accordance with the Multiple Intelligences theory. This research tested the correlation between Potential Intelligence and the components of its Performance Intelligence. Statistical test results that used Pearson correlation showed that five components of Potential Intelligence correlated with Performance Intelligence. Those five components are Logic-Math, Logic, Linguistic, Music, Kinesthetic, and Intrapersonal. Also, this research indicated that cultural factor had a big role in shaping intelligence. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=potential%20intelligence" title="potential intelligence">potential intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=performance%20intelligence" title=" performance intelligence"> performance intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20intelligences" title=" multiple intelligences"> multiple intelligences</a>, <a href="https://publications.waset.org/abstracts/search?q=fingerprint" title=" fingerprint"> fingerprint</a>, <a href="https://publications.waset.org/abstracts/search?q=environment" title=" environment"> environment</a>, <a href="https://publications.waset.org/abstracts/search?q=brain" title=" brain"> brain</a> </p> <a href="https://publications.waset.org/abstracts/9449/correlation-between-potential-intelligence-explanatory-study-in-the-perspective-of-multiple-intelligence-theory-by-using-dermatoglyphics-and-culture-approaches" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9449.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">535</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">1723</span> A Two-Stage Airport Ground Movement Speed Profile Design Methodology 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=Zhang%20Tianci">Zhang Tianci</a>, <a href="https://publications.waset.org/abstracts/search?q=Ding%20Meng"> Ding Meng</a>, <a href="https://publications.waset.org/abstracts/search?q=Zuo%20Hongfu"> Zuo Hongfu</a>, <a href="https://publications.waset.org/abstracts/search?q=Zeng%20Lina"> Zeng Lina</a>, <a href="https://publications.waset.org/abstracts/search?q=Sun%20Zejun"> Sun Zejun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Automation of airport operations can greatly improve ground movement efficiency. In this paper, we study the speed profile design problem for advanced airport ground movement control and guidance. The problem is constrained by the surface four-dimensional trajectory generated in taxi planning. A decomposed approach of two stages is presented to solve this problem efficiently. In the first stage, speeds are allocated at control points which ensure smooth speed profiles can be found later. In the second stage, detailed speed profiles of each taxi interval are generated according to the allocated control point speeds with the objective of minimizing the overall fuel consumption. We present a swarm intelligence based algorithm for the first-stage problem and a discrete variable driven enumeration method for the second-stage problem since it only has a small set of discrete variables. Experimental results demonstrate the presented methodology performs well on real world speed profile design problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=airport%20ground%20movement" title="airport ground movement">airport ground movement</a>, <a href="https://publications.waset.org/abstracts/search?q=fuel%20consumption" title=" fuel consumption"> fuel consumption</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=smoothness" title=" smoothness"> smoothness</a>, <a href="https://publications.waset.org/abstracts/search?q=speed%20profile%20design" title=" speed profile design"> speed profile design</a> </p> <a href="https://publications.waset.org/abstracts/32846/a-two-stage-airport-ground-movement-speed-profile-design-methodology-using-particle-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32846.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">1722</span> An Application of Path Planning Algorithms for Autonomous Inspection of Buried Pipes with Swarm Robots</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Richard%20Molyneux">Richard Molyneux</a>, <a href="https://publications.waset.org/abstracts/search?q=Christopher%20Parrott"> Christopher Parrott</a>, <a href="https://publications.waset.org/abstracts/search?q=Kirill%20Horoshenkov"> Kirill Horoshenkov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper aims to demonstrate how various algorithms can be implemented within swarms of autonomous robots to provide continuous inspection within underground pipeline networks. Current methods of fault detection within pipes are costly, time consuming and inefficient. As such, solutions tend toward a more reactive approach, repairing faults, as opposed to proactively seeking leaks and blockages. The paper presents an efficient inspection method, showing that autonomous swarm robotics is a viable way of monitoring underground infrastructure. Tailored adaptations of various Vehicle Routing Problems (VRP) and path-planning algorithms provide a customised inspection procedure for complicated networks of underground pipes. The performance of multiple algorithms is compared to determine their effectiveness and feasibility. Notable inspirations come from ant colonies and <em>stigmergy</em>, graph theory, the k-Chinese Postman Problem ( -CPP) and traffic theory. Unlike most swarm behaviours which rely on fast communication between agents, underground pipe networks are a highly challenging communication environment with extremely limited communication ranges. This is due to the extreme variability in the pipe conditions and relatively high attenuation of acoustic and radio waves with which robots would usually communicate. This paper illustrates how to optimise the inspection process and how to increase the frequency with which the robots pass each other, without compromising the routes they are able to take to cover the whole network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autonomous%20inspection" title="autonomous inspection">autonomous inspection</a>, <a href="https://publications.waset.org/abstracts/search?q=buried%20pipes" title=" buried pipes"> buried pipes</a>, <a href="https://publications.waset.org/abstracts/search?q=stigmergy" title=" stigmergy"> stigmergy</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=vehicle%20routing%20problem" title=" vehicle routing problem"> vehicle routing problem</a> </p> <a href="https://publications.waset.org/abstracts/101625/an-application-of-path-planning-algorithms-for-autonomous-inspection-of-buried-pipes-with-swarm-robots" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/101625.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">166</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">1721</span> Energy Efficient Clustering with Adaptive Particle Swarm Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=KumarShashvat">KumarShashvat</a>, <a href="https://publications.waset.org/abstracts/search?q=ArshpreetKaur"> ArshpreetKaur</a>, <a href="https://publications.waset.org/abstracts/search?q=RajeshKumar"> RajeshKumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Raman%20Chadha"> Raman Chadha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wireless sensor networks have principal characteristic of having restricted energy and with limitation that energy of the nodes cannot be replenished. To increase the lifetime in this scenario WSN route for data transmission is opted such that utilization of energy along the selected route is negligible. For this energy efficient network, dandy infrastructure is needed because it impinges the network lifespan. Clustering is a technique in which nodes are grouped into disjoints and non–overlapping sets. In this technique data is collected at the cluster head. In this paper, Adaptive-PSO algorithm is proposed which forms energy aware clusters by minimizing the cost of locating the cluster head. The main concern is of the suitability of the swarms by adjusting the learning parameters of PSO. Particle Swarm Optimization converges quickly at the beginning stage of the search but during the course of time, it becomes stable and may be trapped in local optima. In suggested network model swarms are given the intelligence of the spiders which makes them capable enough to avoid earlier convergence and also help them to escape from the local optima. Comparison analysis with traditional PSO shows that new algorithm considerably enhances the performance where multi-dimensional functions are taken into consideration. <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=adaptive%20%E2%80%93%20PSO" title=" adaptive – PSO"> adaptive – PSO</a>, <a href="https://publications.waset.org/abstracts/search?q=comparison%20between%20PSO%20and%20A-PSO" title=" comparison between PSO and A-PSO"> comparison between PSO and A-PSO</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20efficient%20clustering" title=" energy efficient clustering"> energy efficient clustering</a> </p> <a href="https://publications.waset.org/abstracts/46415/energy-efficient-clustering-with-adaptive-particle-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46415.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">246</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=swarm%20intelligence&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=swarm%20intelligence&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=swarm%20intelligence&page=4">4</a></li> <li class="page-item"><a class="page-link" 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