一种基于双子群的改进粒子群优化算法

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.

An Improved Particle Swarm Optimization Algorithm Based on Two-subpopulation

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 proposed. The search range of the algorithm was extended through main subpopulation particle swarm and assistant subpopulation particle swarm, whose search direction was inversed completely. It also adopts the crossbreeding mechanism in genetic algorithm, and uses non-linear inertia weight reduction strategy to accelerate the optimization convergence 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.