一种“自我”感知的高维混沌群体智能算法

为避免早熟收敛和提升粒子在高维空间的搜索能力,文章提出了一种"自我"感知的高维混沌群体智能算法。首先,采用pBest和gBest混沌双扰动来增强粒子的搜索能力;其次,提出一种"自我"感知策略来帮助种群避免早熟收敛;最后,将三种不同微粒群优化(Particle Swarm Optimization,PSO)算法在旅行推销员问题(Traveling Salesman Problem,TSP)上进行了对比实验。实验结果显示"自我"感知的高维混沌群体智能算法简单、有效可行,值得推荐。

A Self-Perception High-Dimensional Chaotic Particle Swarm Algorithm

To avoid the premature convergence and enhance the search capability of the high-dimensional space, a novelself-perception high-dimensional chaotic particle swarm algorithm was presented. Firstly, a double perturbation of pBestand gBest was used to enhance the searching capability of particles. Secondly, self-perception approach was proposed tohelp the particle swarm to avoid the premature convergence. Lastly, three discrete PSO variants were tested on the travelingsalesman problem (TSP). Experimental results show that the self-perception high-dimensional chaotic particle swarmalgorithm is simple, effective and promoting in a high-dimensional space.