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Two Sub-swarms Particle Swarm Optimization Algorithm
2005This paper proposes a two sub-warms particle swarm optimization algorithm (TSPSO) and its iteration equations. The new algorithm assumes that particles are divided into two sub-swarms. The two sub-swarms have different move directions. One sub-swarm moves toward the global best position. Another moves in the opposite direction.
Guochu Chen, Jinshou Yu
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Center Particle Swarm Optimization Algorithm
2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), 2019The linear decreasing weight particle swarm optimization algorithm (LDWPSO) is mentioned in the concept of a center particle, and then puts forward center particle swarm optimization algorithm (PSO). The linear decreasing weight particle swarm optimization algorithm, unlike other general center particle, particle velocity center is not clear, and is ...
Yang Xiaojing, Jiao Qingju, Liu Xinke
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Quantum Particle Swarm Optimization Algorithm
Applied Mechanics and Materials, 2011Based on the problem of traditional particle swarm optimization (PSO) easily trapping into local optima, quantum theory is introduced into PSO to strengthen particles’ diversities and avoid the premature convergence effectively. Experimental results show that this method proposed by this paper has stronger optimal ability and better global searching ...
Yu Fa Xu +3 more
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Particle Swarm Optimization System Algorithm
2007Particle Swarm Optimization algorithm (PSO) is a new evolutionary computation method, which has been successfully applied to many fields. However it also has problem of premature convergence and slow search speed. To deal with those problems we make some improvements on traditional PSO to make its search velocity quickly.
Manjun Cai +3 more
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Adaptive particle swarm optimization algorithm
Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788), 2004The particle swarm optimization (PSO) has exhibited good performance on optimization. However, the parameters, which greatly influence the algorithm stability and performance, are selected depending on experience of designer. The selection of parameters needs to consider both the convergence and avoiding premature convergence.
null Tao Cai +2 more
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Three Sub-Swarm Discrete Particle Swarm Optimization Algorithm
2006 IEEE International Conference on Information Acquisition, 2006Three sub-swarm discrete particle swarm optimization algorithm (THSDPSO) is proposed. The new algorithm assumes that all particles are divided into three sub- swarms. One sub-swarm flies toward the global best position. The second sub-swarm flies in the opposite direction. The last sub-swarm flies randomly around the global best position.
Yufa Xu, Guochu Chen, Jinshou Yu
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Crossed Particle Swarm Optimization Algorithm
2006The particle swarm optimization (PSO) algorithm presents a new way for finding optimal solutions of complex optimization problems. In this paper a modified particle swarm optimization algorithm is presented. We modify the PSO algorithm in some aspects.
Teng-Bo Chen +3 more
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An improved particle swarm optimization algorithm
2013 Ninth International Conference on Natural Computation (ICNC), 2013Particles can remember some information in an optimization process. They learn by themselves and from other particles, so the next generation can inherit much information from their parents and finally find optimal solutions. But particles are also faced with two problems of stagnating in a local but not global optimum.
Huafen Yang +5 more
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An Improved Particle Swarm Optimization Algorithm
Applied Mechanics and Materials, 2012Based on the analyzing inertia weight of the standard particle swarm optimization (PSO) algorithm, an improved PSO algorithm is presented. Convergence condition of PSO is obtained through solving and analyzing the differential equation. By the experiments of four Benchmark function, the results show the performance of S-PSO improved more clearly than ...
Chang Yuan Jiang +3 more
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An amelioration Particle Swarm Optimization algorithm
2010 Sixth International Conference on Natural Computation, 2010a new amelioration Particle Swarm Optimization (SARPSO) based on simulated annealing (SA), asynchronously changed learning genes (ACLG) and roulette strategy was proposed because the classical Particle Swarm Optimization (PSO) algorithm was easily plunged into local minimums.
null Huayong +3 more
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