基于侦察学习策略的粒子群算法优化

Particle Swarm Optimization Based on Scout Learning Strategy

针对粒子群算法收敛能力不足和易陷入局部最优的问题,提出了一种基于侦察学习策略的新型粒子群算法。算法首先利用拓扑结构构建粒子种群,其次采用联合因子均衡算法的局部搜索能力和全局搜索能力,并通过侦察学习策略改进算法的速度和位置公式进而产生候选解;Wilcoxon秩和检验结果和CEC2017基准函数检测结果表明,新型粒子群算法的收敛能力,最优解精度以及算法稳定性更好,说明算法性能得以提升。

Aiming at the problem of insufficient convergence ability of particle swarm optimization and easy to fall into local optimum,a novel particle swarm optimization based on scout learning strategy is proposed.The algorithm first uses the topology structure to construct the particle population,and then uses the local search ability and global search ability of the joint factor equalization algorithm,and improves the speed and position formula of the algorithm through the scout learning strategy to generate candidate solutions.The results of Wilcoxon rank sum test and CEC2017 benchmark function test show that this novel particle swarm algorithm has better convergence ability,optimal solution precision and algorithm stability,indicating that the performance of the algorithm has been improved.