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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="artificial bee colony"> <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> 2332</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: artificial bee colony</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2332</span> Using Cooperation Approaches at Different Levels of Artificial Bee Colony Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vahid%20Zeighami">Vahid Zeighami</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohsen%20Ghsemi"> Mohsen Ghsemi</a>, <a href="https://publications.waset.org/abstracts/search?q=Reza%20Akbari"> Reza Akbari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, a Multi-Level Artificial Bee Colony (called MLABC) is presented. In MLABC two species are used. The first species employs n colonies in which each of the them optimizes the complete solution vector. The cooperation between these colonies is carried out by exchanging information through a leader colony, which contains a set of elite bees. The second species uses a cooperative approach in which the complete solution vector is divided to k sub-vectors, and each of these sub-vectors is optimized by a a colony. The cooperation between these colonies is carried out by compiling sub-vectors into the complete solution vector. Finally, the cooperation between two species is obtained by exchanging information between them. The proposed algorithm is tested on a set of well known test functions. The results show that MLABC algorithms provide efficiency and robustness to solve numerical functions. <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=cooperative" title=" cooperative"> cooperative</a>, <a href="https://publications.waset.org/abstracts/search?q=multilevel%20cooperation" title=" multilevel cooperation"> multilevel cooperation</a>, <a href="https://publications.waset.org/abstracts/search?q=vector" title=" vector"> vector</a> </p> <a href="https://publications.waset.org/abstracts/15646/using-cooperation-approaches-at-different-levels-of-artificial-bee-colony-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15646.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">446</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">2331</span> Study on the Self-Location Estimate by the Evolutional Triangle Similarity Matching Using Artificial Bee Colony Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuji%20Kageyama">Yuji Kageyama</a>, <a href="https://publications.waset.org/abstracts/search?q=Shin%20Nagata"> Shin Nagata</a>, <a href="https://publications.waset.org/abstracts/search?q=Tatsuya%20Takino"> Tatsuya Takino</a>, <a href="https://publications.waset.org/abstracts/search?q=Izuru%20Nomura"> Izuru Nomura</a>, <a href="https://publications.waset.org/abstracts/search?q=Hiroyuki%20Kamata"> Hiroyuki Kamata</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In previous study, technique to estimate a self-location by using a lunar image is proposed. We consider the improvement of the conventional method in consideration of FPGA implementation in this paper. Specifically, we introduce Artificial Bee Colony algorithm for reduction of search time. In addition, we use fixed point arithmetic to enable high-speed operation on FPGA. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=SLIM" title="SLIM">SLIM</a>, <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=location%20estimate" title=" location estimate"> location estimate</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutional%20triangle%20similarity" title=" evolutional triangle similarity"> evolutional triangle similarity</a> </p> <a href="https://publications.waset.org/abstracts/19303/study-on-the-self-location-estimate-by-the-evolutional-triangle-similarity-matching-using-artificial-bee-colony-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19303.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">518</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">2330</span> Metabolic Pathway Analysis of Microbes using the Artificial Bee Colony Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Serena%20Gomez">Serena Gomez</a>, <a href="https://publications.waset.org/abstracts/search?q=Raeesa%20Tanseen"> Raeesa Tanseen</a>, <a href="https://publications.waset.org/abstracts/search?q=Netra%20Shaligram"> Netra Shaligram</a>, <a href="https://publications.waset.org/abstracts/search?q=Nithin%20Francis"> Nithin Francis</a>, <a href="https://publications.waset.org/abstracts/search?q=Sandesh%20B.%20J."> Sandesh B. J.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The human gut consists of a community of microbes which has a lot of effects on human health disease. Metabolic modeling can help to predict relative populations of stable microbes and their effect on health disease. In order to study and visualize microbes in the human gut, we developed a tool that offers the following modules: Build a tool that can be used to perform Flux Balance Analysis for microbes in the human gut using the Artificial Bee Colony optimization algorithm. Run simulations for an individual microbe in different conditions, such as aerobic and anaerobic and visualize the results of these simulations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=microbes" title="microbes">microbes</a>, <a href="https://publications.waset.org/abstracts/search?q=metabolic%20modeling" title=" metabolic modeling"> metabolic modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=flux%20balance%20analysis" title=" flux balance analysis"> flux balance analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20bee%20colony" title=" artificial bee colony"> artificial bee colony</a> </p> <a href="https://publications.waset.org/abstracts/162178/metabolic-pathway-analysis-of-microbes-using-the-artificial-bee-colony-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162178.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">101</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">2329</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">2328</span> Particle Filter State Estimation Algorithm Based on Improved Artificial Bee Colony Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Guangyuan%20Zhao">Guangyuan Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Nan%20Huang"> Nan Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Xuesong%20Han"> Xuesong Han</a>, <a href="https://publications.waset.org/abstracts/search?q=Xu%20Huang"> Xu Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to solve the problem of sample dilution in the traditional particle filter algorithm and achieve accurate state estimation in a nonlinear system, a particle filter method based on an improved artificial bee colony (ABC) algorithm was proposed. The algorithm simulated the process of bee foraging and optimization and made the high likelihood region of the backward probability of particles moving to improve the rationality of particle distribution. The opposition-based learning (OBL) strategy is introduced to optimize the initial population of the artificial bee colony algorithm. The convergence factor is introduced into the neighborhood search strategy to limit the search range and improve the convergence speed. Finally, the crossover and mutation operations of the genetic algorithm are introduced into the search mechanism of the following bee, which makes the algorithm jump out of the local extreme value quickly and continue to search the global extreme value to improve its optimization ability. The simulation results show that the improved method can improve the estimation accuracy of particle filters, ensure the diversity of particles, and improve the rationality of particle distribution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=particle%20filter" title="particle filter">particle filter</a>, <a href="https://publications.waset.org/abstracts/search?q=impoverishment" title=" impoverishment"> impoverishment</a>, <a href="https://publications.waset.org/abstracts/search?q=state%20estimation" title=" state estimation"> state estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20bee%20colony%20algorithm" title=" artificial bee colony algorithm"> artificial bee colony algorithm</a> </p> <a href="https://publications.waset.org/abstracts/174985/particle-filter-state-estimation-algorithm-based-on-improved-artificial-bee-colony-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/174985.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">151</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">2327</span> Solving Directional Overcurrent Relay Coordination Problem Using Artificial Bees Colony</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20H.%20Hussain">M. H. Hussain</a>, <a href="https://publications.waset.org/abstracts/search?q=I.%20Musirin"> I. Musirin</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20F.%20Abidin"> A. F. Abidin</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20R.%20A.%20Rahim"> S. R. A. Rahim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the implementation of Artificial Bees Colony (ABC) algorithm in solving Directional OverCurrent Relays (DOCRs) coordination problem for near-end faults occurring in fixed network topology. The coordination optimization of DOCRs is formulated as linear programming (LP) problem. The objective function is introduced to minimize the operating time of the associated relay which depends on the time multiplier setting. The proposed technique is to taken as a technique for comparison purpose in order to highlight its superiority. The proposed algorithms have been tested successfully on 8 bus test system. The simulation results demonstrated that the ABC algorithm which has been proved to have good search ability is capable in dealing with constraint optimization problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20bees%20colony" title="artificial bees colony">artificial bees colony</a>, <a href="https://publications.waset.org/abstracts/search?q=directional%20overcurrent%20relay%20coordination%20problem" title=" directional overcurrent relay coordination problem"> directional overcurrent relay coordination problem</a>, <a href="https://publications.waset.org/abstracts/search?q=relay%20settings" title=" relay settings"> relay settings</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20multiplier%20setting" title=" time multiplier setting"> time multiplier setting</a> </p> <a href="https://publications.waset.org/abstracts/9892/solving-directional-overcurrent-relay-coordination-problem-using-artificial-bees-colony" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9892.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">330</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">2326</span> Improved Artificial Bee Colony Algorithm for Non-Convex Economic Power Dispatch Problem </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Badr%20M.%20Alshammari">Badr M. Alshammari</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Guesmi"> T. Guesmi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study presents a modified version of the artificial bee colony (ABC) algorithm by including a local search technique for solving the non-convex economic power dispatch problem. The local search step is incorporated at the end of each iteration. Total system losses, valve-point loading effects and prohibited operating zones have been incorporated in the problem formulation. Thus, the problem becomes highly nonlinear and with discontinuous objective function. The proposed technique is validated using an IEEE benchmark system with ten thermal units. Simulation results demonstrate that the proposed optimization algorithm has better convergence characteristics in comparison with the original ABC algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=economic%20power%20dispatch" title="economic power dispatch">economic power dispatch</a>, <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=valve-point%20loading%20effects" title=" valve-point loading effects"> valve-point loading effects</a>, <a href="https://publications.waset.org/abstracts/search?q=prohibited%20operating%20zones" title=" prohibited operating zones"> prohibited operating zones</a> </p> <a href="https://publications.waset.org/abstracts/89937/improved-artificial-bee-colony-algorithm-for-non-convex-economic-power-dispatch-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89937.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">2325</span> Comparison of ANFIS Update Methods Using Genetic Algorithm, Particle Swarm Optimization, and Artificial Bee Colony</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Michael%20R.%20Phangtriastu">Michael R. Phangtriastu</a>, <a href="https://publications.waset.org/abstracts/search?q=Herriyandi%20Herriyandi"> Herriyandi Herriyandi</a>, <a href="https://publications.waset.org/abstracts/search?q=Diaz%20D.%20Santika"> Diaz D. Santika</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a comparison of the implementation of metaheuristic algorithms to train the antecedent parameters and consequence parameters in the adaptive network-based fuzzy inference system (ANFIS). The algorithms compared are genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC). The objective of this paper is to benchmark well-known metaheuristic algorithms. The algorithms are applied to several data set with different nature. The combinations of the algorithms' parameters are tested. In all algorithms, a different number of populations are tested. In PSO, combinations of velocity are tested. In ABC, a different number of limit abandonment are tested. Experiments find out that ABC is more reliable than other algorithms, ABC manages to get better mean square error (MSE) than other algorithms in all data set. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ANFIS" title="ANFIS">ANFIS</a>, <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=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=metaheuristic%20algorithm" title=" metaheuristic algorithm"> metaheuristic algorithm</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/68821/comparison-of-anfis-update-methods-using-genetic-algorithm-particle-swarm-optimization-and-artificial-bee-colony" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68821.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">352</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">2324</span> Artificial Bee Colony Based Modified Energy Efficient Predictive Routing in MANET</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Akhil%20Dubey">Akhil Dubey</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajnesh%20Singh"> Rajnesh Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In modern days there occur many rapid modifications in field of ad hoc network. These modifications create many revolutionary changes in the routing. Predictive energy efficient routing is inspired on the bee’s behavior of swarm intelligence. Predictive routing improves the efficiency of routing in the energetic point of view. The main aim of this routing is the minimum energy consumption during communication and maximized intermediate node’s remaining battery power. This routing is based on food searching behavior of bees. There are two types of bees for the exploration phase the scout bees and for the evolution phase forager bees use by this routing. This routing algorithm computes the energy consumption, fitness ratio and goodness of the path. In this paper we review the literature related with predictive routing, presenting modified routing and simulation result of this algorithm comparison with artificial bee colony based routing schemes in MANET and see the results of path fitness and probability of fitness. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mobile%20ad%20hoc%20network" title="mobile ad hoc network">mobile ad hoc network</a>, <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=PEEBR" title=" PEEBR"> PEEBR</a>, <a href="https://publications.waset.org/abstracts/search?q=modified%20predictive%20routing" title=" modified predictive routing"> modified predictive routing</a> </p> <a href="https://publications.waset.org/abstracts/33480/artificial-bee-colony-based-modified-energy-efficient-predictive-routing-in-manet" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33480.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">416</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">2323</span> Design of Digital IIR Filter Using Opposition Learning and Artificial Bee Colony Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=J.%20S.%20Dhillon">J. S. Dhillon</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20K.%20Dhaliwal"> K. K. Dhaliwal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In almost all the digital filtering applications the digital infinite impulse response (IIR) filters are preferred over finite impulse response (FIR) filters because they provide much better performance, less computational cost and have smaller memory requirements for similar magnitude specifications. However, the digital IIR filters are generally multimodal with respect to the filter coefficients and therefore, reliable methods that can provide global optimal solutions are required. The artificial bee colony (ABC) algorithm is one such recently introduced meta-heuristic optimization algorithm. But in some cases it shows insufficiency while searching the solution space resulting in a weak exchange of information and hence is not able to return better solutions. To overcome this deficiency, the opposition based learning strategy is incorporated in ABC and hence a modified version called oppositional artificial bee colony (OABC) algorithm is proposed in this paper. Duplication of members is avoided during the run which also augments the exploration ability. The developed algorithm is then applied for the design of optimal and stable digital IIR filter structure where design of low-pass (LP) and high-pass (HP) filters is carried out. Fuzzy theory is applied to achieve maximize satisfaction of minimum magnitude error and stability constraints. To check the effectiveness of OABC, the results are compared with some well established filter design techniques and it is observed that in most cases OABC returns better or atleast comparable results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=digital%20infinite%20impulse%20response%20filter" title="digital infinite impulse response filter">digital infinite impulse response filter</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20bee%20colony%20optimization" title=" artificial bee colony optimization"> artificial bee colony optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=opposition%20based%20learning" title=" opposition based learning"> opposition based learning</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20filter%20design" title=" digital filter design"> digital filter design</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-parameter%20optimization" title=" multi-parameter optimization"> multi-parameter optimization</a> </p> <a href="https://publications.waset.org/abstracts/33966/design-of-digital-iir-filter-using-opposition-learning-and-artificial-bee-colony-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33966.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">2322</span> A Fuzzy Multiobjective Model for Bed Allocation Optimized by Artificial Bee Colony Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jalal%20Abdulkareem%20Sultan">Jalal Abdulkareem Sultan</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdulhakeem%20Luqman%20Hasan"> Abdulhakeem Luqman Hasan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the development of health care systems competition, hospitals face more and more pressures. Meanwhile, resource allocation has a vital effect on achieving competitive advantages in hospitals. Selecting the appropriate number of beds is one of the most important sections in hospital management. However, in real situation, bed allocation selection is a multiple objective problem about different items with vagueness and randomness of the data. It is very complex. Hence, research about bed allocation problem is relatively scarce under considering multiple departments, nursing hours, and stochastic information about arrival and service of patients. In this paper, we develop a fuzzy multiobjective bed allocation model for overcoming uncertainty and multiple departments. Fuzzy objectives and weights are simultaneously applied to help the managers to select the suitable beds about different departments. The proposed model is solved by using Artificial Bee Colony (ABC), which is a very effective algorithm. The paper describes an application of the model, dealing with a public hospital in Iraq. The results related that fuzzy multi-objective model was presented suitable framework for bed allocation and optimum use. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bed%20allocation%20problem" title="bed allocation problem">bed allocation problem</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20logic" title=" fuzzy logic"> fuzzy logic</a>, <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=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a> </p> <a href="https://publications.waset.org/abstracts/45374/a-fuzzy-multiobjective-model-for-bed-allocation-optimized-by-artificial-bee-colony-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45374.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">324</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">2321</span> Improved Multi-Channel Separation Algorithm for Satellite-Based Automatic Identification System Signals Based on Artificial Bee Colony and Adaptive Moment Estimation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Peng%20Li">Peng Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Luan%20Wang"> Luan Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Haifeng%20Fei"> Haifeng Fei</a>, <a href="https://publications.waset.org/abstracts/search?q=Renhong%20Xie"> Renhong Xie</a>, <a href="https://publications.waset.org/abstracts/search?q=Yibin%20Rui"> Yibin Rui</a>, <a href="https://publications.waset.org/abstracts/search?q=Shanhong%20Guo"> Shanhong Guo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The applications of satellite-based automatic identification system (S-AIS) pave the road for wide-range maritime traffic monitoring and management. But the coverage of satellite’s view includes multiple AIS self-organizing networks, which leads to the collision of AIS signals from different cells. The contribution of this work is to propose an improved multi-channel blind source separation algorithm based on Artificial Bee Colony (ABC) and advanced stochastic optimization to perform separation of the mixed AIS signals. The proposed approach adopts modified ABC algorithm to get an optimized initial separating matrix, which can expedite the initialization bias correction, and utilizes the Adaptive Moment Estimation (Adam) to update the separating matrix by adjusting the learning rate for each parameter dynamically. Simulation results show that the algorithm can speed up convergence and lead to better performance in separation accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=satellite-based%20automatic%20identification%20system" title="satellite-based automatic identification system">satellite-based automatic identification system</a>, <a href="https://publications.waset.org/abstracts/search?q=blind%20source%20separation" title=" blind source separation"> blind source separation</a>, <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=adaptive%20moment%20estimation" title=" adaptive moment estimation"> adaptive moment estimation</a> </p> <a href="https://publications.waset.org/abstracts/86895/improved-multi-channel-separation-algorithm-for-satellite-based-automatic-identification-system-signals-based-on-artificial-bee-colony-and-adaptive-moment-estimation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/86895.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">185</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">2320</span> Artificial Bee Colony Optimization for SNR Maximization through Relay Selection in Underlay Cognitive Radio Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Babar%20Sultan">Babar Sultan</a>, <a href="https://publications.waset.org/abstracts/search?q=Kiran%20Sultan"> Kiran Sultan</a>, <a href="https://publications.waset.org/abstracts/search?q=Waseem%20Khan"> Waseem Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Ijaz%20Mansoor%20Qureshi"> Ijaz Mansoor Qureshi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a novel idea for the performance enhancement of secondary network is proposed for Underlay Cognitive Radio Networks (CRNs). In Underlay CRNs, primary users (PUs) impose strict interference constraints on the secondary users (SUs). The proposed scheme is based on Artificial Bee Colony (ABC) optimization for relay selection and power allocation to handle the highlighted primary challenge of Underlay CRNs. ABC is a simple, population-based optimization algorithm which attains global optimum solution by combining local search methods (Employed and Onlooker Bees) and global search methods (Scout Bees). The proposed two-phase relay selection and power allocation algorithm aims to maximize the signal-to-noise ratio (SNR) at the destination while operating in an underlying mode. The proposed algorithm has less computational complexity and its performance is verified through simulation results for a different number of potential relays, different interference threshold levels and different transmit power thresholds for the selected relays. <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=underlay%20spectrum%20sharing" title=" underlay spectrum sharing"> underlay spectrum sharing</a>, <a href="https://publications.waset.org/abstracts/search?q=cognitive%20radio%20networks" title=" cognitive radio networks"> cognitive radio networks</a>, <a href="https://publications.waset.org/abstracts/search?q=amplify-and-forward" title=" amplify-and-forward"> amplify-and-forward</a> </p> <a href="https://publications.waset.org/abstracts/13364/artificial-bee-colony-optimization-for-snr-maximization-through-relay-selection-in-underlay-cognitive-radio-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13364.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">2319</span> An Automated Optimal Robotic Assembly Sequence Planning Using Artificial Bee Colony Algorithm </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Balamurali%20Gunji">Balamurali Gunji</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20B.%20V.%20L.%20Deepak"> B. B. V. L. Deepak</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20B.%20Biswal"> B. B. Biswal</a>, <a href="https://publications.waset.org/abstracts/search?q=Amrutha%20Rout"> Amrutha Rout</a>, <a href="https://publications.waset.org/abstracts/search?q=Golak%20Bihari%20Mohanta"> Golak Bihari Mohanta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Robots play an important role in the operations like pick and place, assembly, spot welding and much more in manufacturing industries. Out of those, assembly is a very important process in manufacturing, where 20% of manufacturing cost is wholly occupied by the assembly process. To do the assembly task effectively, Assembly Sequences Planning (ASP) is required. ASP is one of the multi-objective non-deterministic optimization problems, achieving the optimal assembly sequence involves huge search space and highly complex in nature. Many researchers have followed different algorithms to solve ASP problem, which they have several limitations like the local optimal solution, huge search space, and execution time is more, complexity in applying the algorithm, etc. By keeping the above limitations in mind, in this paper, a new automated optimal robotic assembly sequence planning using Artificial Bee Colony (ABC) Algorithm is proposed. In this algorithm, automatic extraction of assembly predicates is done using Computer Aided Design (CAD) interface instead of extracting the assembly predicates manually. Due to this, the time of extraction of assembly predicates to obtain the feasible assembly sequence is reduced. The fitness evaluation of the obtained feasible sequence is carried out using ABC algorithm to generate the optimal assembly sequence. The proposed methodology is applied to different industrial products and compared the results with past literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=assembly%20sequence%20planning" title="assembly sequence planning">assembly sequence planning</a>, <a href="https://publications.waset.org/abstracts/search?q=CAD" title=" CAD"> CAD</a>, <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=assembly%20predicates" title=" assembly predicates"> assembly predicates</a> </p> <a href="https://publications.waset.org/abstracts/87239/an-automated-optimal-robotic-assembly-sequence-planning-using-artificial-bee-colony-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/87239.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">237</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">2318</span> Comparative Performance of Artificial Bee Colony Based Algorithms for Wind-Thermal Unit Commitment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20K.%20Singhal">P. K. Singhal</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Naresh"> R. Naresh</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20Sharma"> V. Sharma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the three optimization models, namely New Binary Artificial Bee Colony (NBABC) algorithm, NBABC with Local Search (NBABC-LS), and NBABC with Genetic Crossover (NBABC-GC) for solving the Wind-Thermal Unit Commitment (WTUC) problem. The uncertain nature of the wind power is incorporated using the Weibull probability density function, which is used to calculate the overestimation and underestimation costs associated with the wind power fluctuation. The NBABC algorithm utilizes a mechanism based on the dissimilarity measure between binary strings for generating the binary solutions in WTUC problem. In NBABC algorithm, an intelligent scout bee phase is proposed that replaces the abandoned solution with the global best solution. The local search operator exploits the neighboring region of the current solutions, whereas the integration of genetic crossover with the NBABC algorithm increases the diversity in the search space and thus avoids the problem of local trappings encountered with the NBABC algorithm. These models are then used to decide the units on/off status, whereas the lambda iteration method is used to dispatch the hourly load demand among the committed units. The effectiveness of the proposed models is validated on an IEEE 10-unit thermal system combined with a wind farm over the planning period of 24 hours. <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=economic%20dispatch" title=" economic dispatch"> economic dispatch</a>, <a href="https://publications.waset.org/abstracts/search?q=unit%20commitment" title=" unit commitment"> unit commitment</a>, <a href="https://publications.waset.org/abstracts/search?q=wind%20power" title=" wind power"> wind power</a> </p> <a href="https://publications.waset.org/abstracts/49910/comparative-performance-of-artificial-bee-colony-based-algorithms-for-wind-thermal-unit-commitment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49910.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">375</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">2317</span> Hybrid Artificial Bee Colony and Least Squares Method for Rule-Based Systems Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahcene%20Habbi">Ahcene Habbi</a>, <a href="https://publications.waset.org/abstracts/search?q=Yassine%20Boudouaoui"> Yassine Boudouaoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with the problem of automatic rule generation for fuzzy systems design. The proposed approach is based on hybrid artificial bee colony (ABC) optimization and weighted least squares (LS) method and aims to find the structure and parameters of fuzzy systems simultaneously. More precisely, two ABC based fuzzy modeling strategies are presented and compared. The first strategy uses global optimization to learn fuzzy models, the second one hybridizes ABC and weighted least squares estimate method. The performances of the proposed ABC and ABC-LS fuzzy modeling strategies are evaluated on complex modeling problems and compared to other advanced modeling methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automatic%20design" title="automatic design">automatic design</a>, <a href="https://publications.waset.org/abstracts/search?q=learning" title=" learning"> learning</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20rules" title=" fuzzy rules"> fuzzy rules</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid" title=" hybrid"> hybrid</a>, <a href="https://publications.waset.org/abstracts/search?q=swarm%20optimization" title=" swarm optimization"> swarm optimization</a> </p> <a href="https://publications.waset.org/abstracts/15603/hybrid-artificial-bee-colony-and-least-squares-method-for-rule-based-systems-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15603.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">437</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">2316</span> Community Structure Detection in Networks Based on Bee Colony</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bilal%20Saoud">Bilal Saoud</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose a new method to find the community structure in networks. Our method is based on bee colony and the maximization of modularity to find the community structure. We use a bee colony algorithm to find the first community structure that has a good value of modularity. To improve the community structure, that was found, we merge communities until we get a community structure that has a high value of modularity. We provide a general framework for implementing our approach. We tested our method on computer-generated and real-world networks with a comparison to very known community detection methods. The obtained results show the effectiveness of our proposition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bee%20colony" title="bee colony">bee colony</a>, <a href="https://publications.waset.org/abstracts/search?q=networks" title=" networks"> networks</a>, <a href="https://publications.waset.org/abstracts/search?q=modularity" title=" modularity"> modularity</a>, <a href="https://publications.waset.org/abstracts/search?q=normalized%20mutual%20information" title=" normalized mutual information"> normalized mutual information</a> </p> <a href="https://publications.waset.org/abstracts/93455/community-structure-detection-in-networks-based-on-bee-colony" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/93455.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">406</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">2315</span> Comparative Analysis of Two Different Ant Colony Optimization Algorithm for Solving Travelling Salesman Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sourabh%20Joshi">Sourabh Joshi</a>, <a href="https://publications.waset.org/abstracts/search?q=Tarun%20Sharma"> Tarun Sharma</a>, <a href="https://publications.waset.org/abstracts/search?q=Anurag%20Sharma"> Anurag Sharma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ant Colony Optimization is heuristic Algorithm which has been proven a successful technique applied on number of combinatorial optimization problems. Two variants of Ant Colony Optimization algorithm named Ant System and Max-Min Ant System are implemented in MATLAB to solve travelling Salesman Problem and the results are compared. In, this paper both systems are analyzed by solving the some Travelling Salesman Problem and depict which system solve the problem better in term of cost and time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ant%20Colony%20Optimization" title="Ant Colony Optimization">Ant Colony Optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=Travelling%20Salesman%20Problem" title=" Travelling Salesman Problem"> Travelling Salesman Problem</a>, <a href="https://publications.waset.org/abstracts/search?q=Ant%20System" title=" Ant System"> Ant System</a>, <a href="https://publications.waset.org/abstracts/search?q=Max-Min%20Ant%20System" title=" Max-Min Ant System"> Max-Min Ant System</a> </p> <a href="https://publications.waset.org/abstracts/18457/comparative-analysis-of-two-different-ant-colony-optimization-algorithm-for-solving-travelling-salesman-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18457.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">483</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">2314</span> Hybrid Bee Ant Colony Algorithm for Effective Load Balancing and Job Scheduling in Cloud Computing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thomas%20Yeboah">Thomas Yeboah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cloud Computing is newly paradigm in computing that promises a delivery of computing as a service rather than a product, whereby shared resources, software, and information are provided to computers and other devices as a utility (like the electricity grid) over a network (typically the Internet). As Cloud Computing is a newly style of computing on the internet. It has many merits along with some crucial issues that need to be resolved in order to improve reliability of cloud environment. These issues are related with the load balancing, fault tolerance and different security issues in cloud environment.In this paper the main concern is to develop an effective load balancing algorithm that gives satisfactory performance to both, cloud users and providers. This proposed algorithm (hybrid Bee Ant Colony algorithm) is a combination of two dynamic algorithms: Ant Colony Optimization and Bees Life algorithm. Ant Colony algorithm is used in this hybrid Bee Ant Colony algorithm to solve load balancing issues whiles the Bees Life algorithm is used for optimization of job scheduling in cloud environment. The results of the proposed algorithm shows that the hybrid Bee Ant Colony algorithm outperforms the performances of both Ant Colony algorithm and Bees Life algorithm when evaluated the proposed algorithm performances in terms of Waiting time and Response time on a simulator called CloudSim. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20optimization%20algorithm" title="ant colony optimization algorithm">ant colony optimization algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=bees%20life%20algorithm" title=" bees life algorithm"> bees life algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=scheduling%20algorithm" title=" scheduling algorithm"> scheduling algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=performance" title=" performance"> performance</a>, <a href="https://publications.waset.org/abstracts/search?q=cloud%20computing" title=" cloud computing"> cloud computing</a>, <a href="https://publications.waset.org/abstracts/search?q=load%20balancing" title=" load balancing"> load balancing</a> </p> <a href="https://publications.waset.org/abstracts/27139/hybrid-bee-ant-colony-algorithm-for-effective-load-balancing-and-job-scheduling-in-cloud-computing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27139.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">628</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">2313</span> A Controlled Mathematical Model for Population Dynamics in an Infested Honeybees Colonies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chakib%20Jerry">Chakib Jerry</a>, <a href="https://publications.waset.org/abstracts/search?q=Mounir%20Jerry"> Mounir Jerry</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a mathematical model of infested honey bees colonies is formulated in order to investigate Colony Collapse Disorder in a honeybee colony. CCD, as it is known, is a major problem on honeybee farms because of the massive decline in colony numbers. We introduce to the model a control variable which represents forager protection. We study the controlled model to derive conditions under which the bee colony can fight off epidemic. Secondly we study the problem of minimizing prevention cost under model’s dynamics constraints. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=honey%20bee" title="honey bee">honey bee</a>, <a href="https://publications.waset.org/abstracts/search?q=disease%20transmission%20model" title=" disease transmission model"> disease transmission model</a>, <a href="https://publications.waset.org/abstracts/search?q=disease%20control%20honeybees" title=" disease control honeybees"> disease control honeybees</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20control" title=" optimal control"> optimal control</a> </p> <a href="https://publications.waset.org/abstracts/48127/a-controlled-mathematical-model-for-population-dynamics-in-an-infested-honeybees-colonies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48127.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">425</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">2312</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">2311</span> Comparative Performance Analysis for Selected Behavioral Learning Systems versus Ant Colony System Performance: Neural Network Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hassan%20M.%20H.%20Mustafa">Hassan M. H. Mustafa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This piece of research addresses an interesting comparative analytical study. Which considers two concepts of diverse algorithmic computational intelligence approaches related tightly with Neural and Non-Neural Systems. The first algorithmic intelligent approach concerned with observed obtained practical results after three neural animal systems’ activities. Namely, they are Pavlov’s, and Thorndike’s experimental work. Besides a mouse’s trial during its movement inside figure of eight (8) maze, to reach an optimal solution for reconstruction problem. Conversely, second algorithmic intelligent approach originated from observed activities’ results for Non-Neural Ant Colony System (ACS). These results obtained after reaching an optimal solution while solving Traveling Sales-man Problem (TSP). Interestingly, the effect of increasing number of agents (either neurons or ants) on learning performance shown to be similar for both introduced systems. Finally, performance of both intelligent learning paradigms shown to be in agreement with learning convergence process searching for least mean square error LMS algorithm. While its application for training some Artificial Neural Network (ANN) models. Accordingly, adopted ANN modeling is a relevant and realistic tool to investigate observations and analyze performance for both selected computational intelligence (biological behavioral learning) systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network%20modeling" title="artificial neural network modeling">artificial neural network modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=animal%20learning" title=" animal learning"> animal learning</a>, <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20system" title=" ant colony system"> ant colony system</a>, <a href="https://publications.waset.org/abstracts/search?q=traveling%20salesman%20problem" title=" traveling salesman problem"> traveling salesman problem</a>, <a href="https://publications.waset.org/abstracts/search?q=computational%20biology" title=" computational biology"> computational biology</a> </p> <a href="https://publications.waset.org/abstracts/13952/comparative-performance-analysis-for-selected-behavioral-learning-systems-versus-ant-colony-system-performance-neural-network-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13952.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">470</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">2310</span> Dynamic Construction Site Layout Using Ant Colony Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yassir%20AbdelRazig">Yassir AbdelRazig</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Evolutionary optimization methods such as genetic algorithms have been used extensively for the construction site layout problem. More recently, ant colony optimization algorithms, which are evolutionary methods based on the foraging behavior of ants, have been successfully applied to benchmark combinatorial optimization problems. This paper proposes a formulation of the site layout problem in terms of a sequencing problem that is suitable for solution using an ant colony optimization algorithm. In the construction industry, site layout is a very important planning problem. The objective of site layout is to position temporary facilities both geographically and at the correct time such that the construction work can be performed satisfactorily with minimal costs and improved safety and working environment. During the last decade, evolutionary methods such as genetic algorithms have been used extensively for the construction site layout problem. This paper proposes an ant colony optimization model for construction site layout. A simple case study for a highway project is utilized to illustrate the application of the model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ant%20colony" title="ant colony">ant colony</a>, <a href="https://publications.waset.org/abstracts/search?q=construction%20site%20layout" title=" construction site layout"> construction site layout</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithms" title=" genetic algorithms"> genetic algorithms</a> </p> <a href="https://publications.waset.org/abstracts/28641/dynamic-construction-site-layout-using-ant-colony-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28641.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">382</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">2309</span> Prediction of the Lateral Bearing Capacity of Short Piles in Clayey Soils Using Imperialist Competitive Algorithm-Based Artificial Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Reza%20Dinarvand">Reza Dinarvand</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahdi%20Sadeghian"> Mahdi Sadeghian</a>, <a href="https://publications.waset.org/abstracts/search?q=Somaye%20Sadeghian"> Somaye Sadeghian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Prediction of the ultimate bearing capacity of piles (Qu) is one of the basic issues in geotechnical engineering. So far, several methods have been used to estimate Qu, including the recently developed artificial intelligence methods. In recent years, optimization algorithms have been used to minimize artificial network errors, such as colony algorithms, genetic algorithms, imperialist competitive algorithms, and so on. In the present research, artificial neural networks based on colonial competition algorithm (ANN-ICA) were used, and their results were compared with other methods. The results of laboratory tests of short piles in clayey soils with parameters such as pile diameter, pile buried length, eccentricity of load and undrained shear resistance of soil were used for modeling and evaluation. The results showed that ICA-based artificial neural networks predicted lateral bearing capacity of short piles with a correlation coefficient of 0.9865 for training data and 0.975 for test data. Furthermore, the results of the model indicated the superiority of ICA-based artificial neural networks compared to back-propagation artificial neural networks as well as the Broms and Hansen methods. <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=clayey%20soil" title=" clayey soil"> clayey soil</a>, <a href="https://publications.waset.org/abstracts/search?q=imperialist%20competition%20algorithm" title=" imperialist competition algorithm"> imperialist competition algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=lateral%20bearing%20capacity" title=" lateral bearing capacity"> lateral bearing capacity</a>, <a href="https://publications.waset.org/abstracts/search?q=short%20pile" title=" short pile"> short pile</a> </p> <a href="https://publications.waset.org/abstracts/104881/prediction-of-the-lateral-bearing-capacity-of-short-piles-in-clayey-soils-using-imperialist-competitive-algorithm-based-artificial-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/104881.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">152</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">2308</span> Cluster Based Ant Colony Routing Algorithm for Mobile Ad-Hoc Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alaa%20Eddien%20Abdallah">Alaa Eddien Abdallah</a>, <a href="https://publications.waset.org/abstracts/search?q=Bajes%20Yousef%20Alskarnah"> Bajes Yousef Alskarnah </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ant colony based routing algorithms are known to grantee the packet delivery, but they suffer from the huge overhead of control messages which are needed to discover the route. In this paper we utilize the network nodes positions to group the nodes in connected clusters. We use clusters-heads only on forwarding the route discovery control messages. Our simulations proved that the new algorithm has decreased the overhead dramatically without affecting the delivery rate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ad-hoc%20network" title="ad-hoc network">ad-hoc network</a>, <a href="https://publications.waset.org/abstracts/search?q=MANET" title=" MANET"> MANET</a>, <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20routing" title=" ant colony routing"> ant colony routing</a>, <a href="https://publications.waset.org/abstracts/search?q=position%20based%20routing" title=" position based routing"> position based routing</a> </p> <a href="https://publications.waset.org/abstracts/13698/cluster-based-ant-colony-routing-algorithm-for-mobile-ad-hoc-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13698.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">425</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">2307</span> Bee Colony Optimization Applied to the Bin Packing Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kenza%20Aida%20Amara">Kenza Aida Amara</a>, <a href="https://publications.waset.org/abstracts/search?q=Bachir%20Djebbar"> Bachir Djebbar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We treat the two-dimensional bin packing problem which involves packing a given set of rectangles into a minimum number of larger identical rectangles called bins. This combinatorial problem is NP-hard. We propose a pretreatment for the oriented version of the problem that allows the valorization of the lost areas in the bins and the reduction of the size problem. A heuristic method based on the strategy first-fit adapted to this problem is presented. We present an approach of resolution by bee colony optimization. Computational results express a comparison of the number of bins used with and without pretreatment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bee%20colony%20optimization" title="bee colony optimization">bee colony optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=bin%20packing" title=" bin packing"> bin packing</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic%20algorithm" title=" heuristic algorithm"> heuristic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=pretreatment" title=" pretreatment"> pretreatment</a> </p> <a href="https://publications.waset.org/abstracts/65005/bee-colony-optimization-applied-to-the-bin-packing-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65005.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">633</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">2306</span> Fault Diagnosis of Manufacturing Systems Using AntTreeStoch with Parameter Optimization by ACO</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ouahab%20Kadri">Ouahab Kadri</a>, <a href="https://publications.waset.org/abstracts/search?q=Leila%20Hayet%20Mouss"> Leila Hayet Mouss</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present three diagnostic modules for complex and dynamic systems. These modules are based on three ant colony algorithms, which are AntTreeStoch, Lumer & Faieta and Binary ant colony. We chose these algorithms for their simplicity and their wide application range. However, we cannot use these algorithms in their basement forms as they have several limitations. To use these algorithms in a diagnostic system, we have proposed three variants. We have tested these algorithms on datasets issued from two industrial systems, which are clinkering system and pasteurization system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20algorithms" title="ant colony algorithms">ant colony algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=complex%20and%20dynamic%20systems" title=" complex and dynamic systems"> complex and dynamic systems</a>, <a href="https://publications.waset.org/abstracts/search?q=diagnosis" title=" diagnosis"> diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/42293/fault-diagnosis-of-manufacturing-systems-using-anttreestoch-with-parameter-optimization-by-aco" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42293.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">298</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">2305</span> Internet of Things: Route Search Optimization Applying Ant Colony Algorithm and Theory of Computer Science</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tushar%20Bhardwaj">Tushar Bhardwaj</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Internet of Things (IoT) possesses a dynamic network where the network nodes (mobile devices) are added and removed constantly and randomly, hence the traffic distribution in the network is quite variable and irregular. The basic but very important part in any network is route searching. We have many conventional route searching algorithms like link-state, and distance vector algorithms but they are restricted to the static point to point network topology. In this paper we propose a model that uses the Ant Colony Algorithm for route searching. It is dynamic in nature and has positive feedback mechanism that conforms to the route searching. We have also embedded the concept of Non-Deterministic Finite Automata [NDFA] minimization to reduce the network to increase the performance. Results show that Ant Colony Algorithm gives the shortest path from the source to destination node and NDFA minimization reduces the broadcasting storm effectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=routing" title="routing">routing</a>, <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20algorithm" title=" ant colony algorithm"> ant colony algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=NDFA" title=" NDFA"> NDFA</a>, <a href="https://publications.waset.org/abstracts/search?q=IoT" title=" IoT"> IoT</a> </p> <a href="https://publications.waset.org/abstracts/1965/internet-of-things-route-search-optimization-applying-ant-colony-algorithm-and-theory-of-computer-science" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1965.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">444</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">2304</span> Ant Colony Optimization Control for Multilevel STATCOM</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20T%C3%A9djini">H. Tédjini</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20Meslem"> Y. Meslem</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Guesbaoui"> B. Guesbaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Safa"> A. Safa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Flexible AC Transmission Systems (FACTS) are potentially becoming more flexible and more economical local controllers in the power system; and because of the high MVA ratings, it would be expensive to provide independent, equal, regulated DC voltage sources to power the multilevel converters which are presently proposed for STATCOMs. DC voltage sources can be derived from the DC link capacitances which are charged by the rectified ac power. In this paper a new stronger control combined of nonlinear control based Lyapunov’s theorem and Ant Colony Algorithm (ACA) to maintain stability of multilevel STATCOM and the utility. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Static%20Compensator%20%28STATCOM%29" title="Static Compensator (STATCOM)">Static Compensator (STATCOM)</a>, <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20optimization%20%28ACO%29" title=" ant colony optimization (ACO)"> ant colony optimization (ACO)</a>, <a href="https://publications.waset.org/abstracts/search?q=lyapunov%20control%20theory" title=" lyapunov control theory"> lyapunov control theory</a>, <a href="https://publications.waset.org/abstracts/search?q=Decoupled%20power%20control" title=" Decoupled power control"> Decoupled power control</a>, <a href="https://publications.waset.org/abstracts/search?q=neutral%20point%20clamped%20%28NPC%29" title=" neutral point clamped (NPC)"> neutral point clamped (NPC)</a> </p> <a href="https://publications.waset.org/abstracts/19254/ant-colony-optimization-control-for-multilevel-statcom" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19254.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">556</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">2303</span> An Improved Ant Colony Algorithm for Genome Rearrangements</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Essam%20Al%20Daoud">Essam Al Daoud</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Genome rearrangement is an important area in computational biology and bioinformatics. The basic problem in genome rearrangements is to compute the edit distance, i.e., the minimum number of operations needed to transform one genome into another. Unfortunately, unsigned genome rearrangement problem is NP-hard. In this study an improved ant colony optimization algorithm to approximate the edit distance is proposed. The main idea is to convert the unsigned permutation to signed permutation and evaluate the ants by using Kaplan algorithm. Two new operations are added to the standard ant colony algorithm: Replacing the worst ants by re-sampling the ants from a new probability distribution and applying the crossover operations on the best ants. The proposed algorithm is tested and compared with the improved breakpoint reversal sort algorithm by using three datasets. The results indicate that the proposed algorithm achieves better accuracy ratio than the previous methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20algorithm" title="ant colony algorithm">ant colony algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=edit%20distance" title=" edit distance"> edit distance</a>, <a href="https://publications.waset.org/abstracts/search?q=genome%0D%0Abreakpoint" title=" genome breakpoint"> genome breakpoint</a>, <a href="https://publications.waset.org/abstracts/search?q=genome%20rearrangement" title=" genome rearrangement"> genome rearrangement</a>, <a href="https://publications.waset.org/abstracts/search?q=reversal%20sort" title=" reversal sort"> reversal sort</a> </p> <a href="https://publications.waset.org/abstracts/5601/an-improved-ant-colony-algorithm-for-genome-rearrangements" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5601.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> <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=artificial%20bee%20colony&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=artificial%20bee%20colony&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=artificial%20bee%20colony&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=artificial%20bee%20colony&page=5">5</a></li> <li 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