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A Multi-Swarm Cooperative Perturbed Particle Swarm Optimization
Advanced Materials Research, 2011Combined with a variety of ideas a Multi-swarm cooperative Perturbed Particle Swarm Optimization algorithm (MpPSO) is presented to improve the performance and to reduce the premature convergence of PSO. This algorithm includes the idea of multiple swarms to improve the evolution efficiency by information sharing between populations to avoid falling ...
Xiang Jun Yang +3 more
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A multi-swarm cooperative hybrid particle swarm optimizer
2010 Sixth International Conference on Natural Computation, 2010Cooperative approaches have proved to be very useful in evolutionary computation. This paper a novel multi-swarm cooperative particle swarm optimization (PSO) is proposed. It involves a collection of two sub-swarms that interact by exchanging information to solve a problem.
Ying Li, Jiaxi Liang, Jie Hu
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Multi-swarms Dynamic Convergence Optimization for object tracking
2016 International Joint Conference on Neural Networks (IJCNN), 2016Swarm intelligence has been applied to many research projects in recent years, many scientists are working on developing the full potential of a self-organized and decentralized system to help solving complex problems. In image processing, it also demonstrates fast and accurate in searching solutions for trajectory clustering and precise object ...
Feng Sha +5 more
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Multi-swarm particle swarm optimization for payment scheduling
2017 Seventh International Conference on Information Science and Technology (ICIST), 2017The payment scheduling negotiation problem with multi-mode and resource constraints (MRCPSNP) is a practical extension to the resource-constrained project scheduling problem (RCPSP). It considers the interests of both the client and the contractor, who negotiate with each other to maximize their own benefits.
Xiao-Miao Li +3 more
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Multi-swarm Particle Grid Optimization for Object Tracking
2016In recent years, one of the popular swarm intelligence algorithm Particle Swarm Optimization has demonstrated to have efficient and accurate outcomes for tracking different object movement. But there are still problems of multiple interferences in object tracking need to overcome.
Feng Sha +4 more
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A Novel Learning Multi-Swarm Particle Swarm Optimization
2023Particle swarm optimization (PSO) is one of the metaheuristic optimization methods. Because of its many advantages, it is often used to solve real-world engineering problems. However, in case of complex, multi-dimensional tasks, PSO faces some problems related to premature conver-gence and stagnation in local optima.
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RFID networks planning using a multi-swarm optimizer
2009 Chinese Control and Decision Conference, 2009In this paper, we develop an optimization model for planning the positions of readers in the RFID network based on a novel Multi-swarm Particle Swarm Optimizer called PS2O. The main idea of PS2O is to extend the single population PSO to the interacting multi-swarms model by constructing hierarchical interaction topology and enhanced dynamical update ...
Hanning Chen, Yunlong Zhu, Kunyuan Hu
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Hierarchical multi-swarm cooperative teaching–learning-based optimization for global optimization
Soft Computing, 2016Hierarchical cooperation mechanism, which is inspired by the features of specialization and cooperation in the social organizations, has been successfully used to increase the diversity of the population and avoid premature convergence for solving complex optimization problems.
Feng Zou +3 more
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Multi- Swarm and Multi- Best particle swarm optimization algorithm
2008 7th World Congress on Intelligent Control and Automation, 2008This paper proposes a novel particle swarm optimization algorithm: Multi-Swarm and Multi-Best particle swarm optimization algorithm. The novel algorithm divides initialized particles into several populations randomly. After calculating certain generations respectively, every population is combined into one population and continues to calculate until ...
null Junliang Li, null Xinping Xiao
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Dynamic Multi-Swarm Particle Swarm Optimizer with Local Search
2005 IEEE Congress on Evolutionary Computation, 2005In this paper, the performance of a modified dynamic multi-swarm particle swarm optimizer (DMS-PSO) on the set of benchmark functions provided by CEC2005 is reported. Different from the existing multi-swarm PSOs and local versions of PSO, the swarms are dynamic and the swarms' size is small. The whole population is divided into many small swarms, these
J.J. Liang, P.N. Suganthan
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