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Description: Particle swarm optimization (PSO) is a population based stochastic optimization technique developed by Dr. Eberhart and Dr. Kennedy in 1995, inspired by social behavior of bird flocking or fish schooling. Each particle keeps track of its coordinates in the problem space which are associated with the best solution (fitness) it has achieved so far. (The fitness value is also stored.)
This value is called pbest. Another "best" value that is tracked by the particle swarm optimizer is the best value, obtained so far by any particle in the neighbors of the particle. This location is called lbest. when a particle takes all the population as its topological neighbors, the best value is a global best and is called gbest. Following is the steps of PSO:
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Size: 1024 |
Author: BBB |
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Description: 这个是基于环形拓扑结构的约束微粒群算法，对原来的算法做了改进，只用到了一个邻居节点，效果与基本的微粒群算法大致相当。但是在对多模函数的计算上，优于基本的微粒群算法。这个算法的运行平台是GPUmat,需要的运行环境有：Matlab, GPU, GPUmat, Visual Studio.-This the modified particle swarm optimization algorithm that based on ring toplopy with constrain condition. The lbest of this algorithm only comes itself and its one neighbourhood , the experimental result show its performance as well as simple PSO. However, it is better than simple PSO when it is applied in the multimodal function. The systme environment including: Matlab, GPU, GPUmat, Visual Studio.
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Size: 3072 |
Author: Shiqin Yang |
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Description: 粒子群优化研究工具箱是为了协助解决粒子群优化（PSO）过早收敛问题的论文研究。(Gbest PSO, Lbest PSO, RegPSO, GCPSO, MPSO, OPSO, Cauchy mutation, and hybrid combinations)
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Size: 128000 |
Author: 小茶柚
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