一种基于双子群的改进粒子群优化算法

An Improved Particle Swarm Optimization Algorithm Based on Two-subpopulation

针对粒子群优化算法易于陷入局部最优解并存在早熟收敛的问题,提出了一种基于双子群的改进粒子群优化算法(TS-IPSO),通过2组搜索方向相反的主、辅子群之间的相互协同,扩大搜索范围,借鉴遗传算法的杂交机制,并采用惯性权值的非线性递减策略,加快算法的收敛速度和提高粒子的搜索能力,降低了算法陷入局部极值的风险.实验结果表明该算法较标准PSO算法提高了全局搜索能力和收敛速度,改善了优化性能.

Particle Swarm Optimization algorithm easily gets stuck at local optimal solution and shows premature convergence. An improved Particle Swarm Optimization algorithm based on two-subpopulation（TS-IPSO） was pro- posed. The search range of the algorithm was extended through main subpopulation particle swarm and assistant sub- population particle swarm, whose search direction was inversed completely. It also adopts the crossbreeding mecha- nism in genetic algorithm, and uses non-linear inertia weight reduction strategy to accelerate the optimization conver- gence and improve the search capabilities of particles, then effectively decrease the risk of trapping into local optima. Experiment results have shown that the TS-IPSO can greatly improve the global convergence ability and enhance the rate of convergence, compared with SPSO.