哈夫曼编码的协同粒子群优化算法

Cooperative Particle Swarm Optimization Algorithm Based on Huffman Coding

针对粒子群优化(PSO)算法在优化问题过程中易陷入局部最优的问题,提出一种基于哈夫曼编码的协同粒子群优化(HC-PSO)算法。采用哈夫曼编码将种群划分成2个子种群并对2个子种群进行独立优化,同时,2子种群之间协同完成搜索种群的全局最优解。采用6个标准测试函数来测试算法性能。实验结果表明,该算法可以有效地避免种群陷入局部最优,具有较好的优化性能和稳定性,收敛精度得到了显著的提高。

Aiming at particle swarm optimization （ PSO） algorithm easy to fall into local optimal problems in optimizing a popula-tion, a new particle swarm optimization on Huffman Coding （ HC-PSO） algorithm was put forward. Using Huffman Coding, one population will be divided into two sub populations, and each subpopulation will be optimized independently. At the same time, the two subpopulations cooperatively complete searching the global optimum solution. Through six standard test functions, the ex-perimental results show that the algorithm can effectively avoid the population falling into local optimum, is of better optimization performance and stability, and convergence accuracy is significantly improved.