粒子群优化算法中的分步式策略

为了解决粒子群优化算法(PSO)在处理高维多极值问题时容易陷入局部最优而早熟的问题，提出了分步式学习策略和分步式评价策略。前者让粒子每次升级只向某一个榜样学习，使粒子能在更有潜力的区域搜索；并简化了其升级规则，使粒子的搜索行为更易被控制。后者对粒子的位置矢量逐维进行评价，使粒子向目标最优位置“稳步前进”；并通过对维之间的关系的检测，解决了维不可分解的问题。实验证明，新算法具有很好的收敛速度和抗早熟能力。

Stepwise Strategies in Particle Swarm Optimization

The particle swarm optimization (PSO) may be trapped in local optima and fail to converge to global optima, especially for multimodal and high-dimensional problems. To handle this problem, a stepwise learning strategy and a stepwise evaluation strategy are presented. The former makes each particle learn from only one particle's historical best information in each update progress in order to search in a potential area, and simplifies particles' update rules to easily control their convergence behaviors. The latter enables each particle to be evaluated in dimension-by-dimension order so as to step steadily toward the destination position, and settles non-separable problems by means of detecting relationships between dimensions. Application of the new PSO on several benchmark optimization problems shows a marked improvement in performance over six other recent variants of the PSO.