关键词: 降维对称微粒群算法，种群分布嫡，启发式探测，克隆变异

Abstract: A novel Symmetrical PSO algorithm (SymPSO_HD) was proposed to improve the search ability of PSO algorithm. To initialize the cluster effectively, population scatter entropy strategy was introduced. In order to improve the particle's position vector, a special kind of particle characterized by detection was proposed too. And to enhance the partide's learning ability both in local and global domain, we put forward the clone-mutation-selection strategy in neighborhood and the dimensionality reduction symmetrical strategy in global area. Simulation and analysis show that SymPSO_HD algorithm has stable search ability and strong adaptability, and can converge to the global optimum with large probability.

Key words: Dimensionality reduction symmetrical PSO, Population scatter entropy, Heuristic detection, Clone and mutation