基于局部搜索惯性权重的粒子群优化算法

摘要

The performance of particle swarm optimization is mainly affected by the impact of parameter, especially, the weight of the inertial.So, choosing the right algorithm could balance the global and local search ability.To improve the robustness of the algorithm, determined the weight of inertia by local search method, and the adjustment of learning factor was based on the function value of the current particle.Finally, tested some standard test functions, and verified the superiority of the proposed algorithm.%粒子群优化算法的性能主要受其中参数的影响,尤其是惯性权重的影响,选择合理的ω能够平衡算法的全局和局部搜索能力.根据当前粒子的函数值调整学习因子,利用局部搜索的方法确定惯性权重,提高了算法的鲁棒性能.最后对一些标准测试函数进行验证,实验分析表明该算法具有优越性能.