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小世界模型的个体决策微粒群算法

     

摘要

Particle swam optimization (PSO) is a swarm intelligence optimization algorithm which simulates the animal behavior. Due to particles (individuals) survival and feeding in different environments, which accumulate different experience. Thus different individuals in feeding or other behaviors will make a different decision. However this decision mechanism cannot reflect in stand PSO. Particles make decision will take into account information around itself. Therefore this paper improves PSO by introduction individual decision mechanism and neighborhood of small-word modal. At the same time, using Lyapunov stability theory to analysis the improved algorithms stability and give the corresponding parameters selected methods. Particles are attracted by the ideal location around them and the best location of swarm in the improved PSO, which changes the malpractice of traditional PSO only attracted by the best location of swarm. Using several test functions to simulation, results show that the algorithm has a better performance compare the other improved PSO.%微粒群算法是一种模拟动物行为的群智能优化算法.由于微粒(个体)在不同环境中生存与觅食,积累了不同的经验,因此不同个体在觅食或者其他行为申会做出不同的决策,但是这种决策机制在标准微粒群算法申并没有体现出来.微粒在决策时会考虑周围其它粒子的信息,因此本文通过引入个体决策机制与小世界模型的邻城结构来改进微粒群算法,同时利用李雅普诺夫稳定性理论对改进的算法进行稳定性分析,并给出相应的参数选择方式.在改进的微粒群算法申,微粒被周围理想微粒的位置和群体最优位置所吸引,改变了传统微粒群算法只被群体最优位置吸引的弊端.对常用的几个测试函数进行仿真,与其它两种改进的微粒群算法相比,结果表明该算法有更好的性能.

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