国家自然基金面上项目(62176050); 中央高校基本科研业务费专项资金(2572017EB02); 东北林业大学双一流科研启动基金(411112438); 哈尔滨市科技局创新人才基金(2017RAXXJ018)
针对粒子群算法无法有效兼顾开采与勘探的问题, 提出一种基于密度峰值的依维度重置多种群粒子群算法. 首先采用密度峰值聚类中相对距离的思想并结合适应度值将种群分为两个子种群: 顶层群和底层群. 之后为顶层群设计专注于开采的学习策略而为底层群设计倾向于勘探的学习策略, 以均衡种群的勘探与开采. 最后依维度将陷入局部最优的粒子与全局最优粒子交叉重置, 在有效避免早熟收敛的同时也显著减少了无效计算次数. 将提出的算法与其他改进的优化算法在基础优化问题与CEC2017测试集上进行实验对比, 实验结果均值的统计检验证明了提出算法的改进具有统计学显著性.
In order to solve the dilemma that particle swarm optimization algorithm (PSO) cannot well balance the exploration and exploitation, a density peak based multi subpopulation particle swarm optimization algorithm is proposed with dimensionally reset strategy (DPMPSO). In the proposed DPMPSO, the idea of relative distance originated from density peak clustering is firstly adopted and then it is combined with the fitness value of particles to divide the whole swarm into two subpopulations: the top subpopulation and the bottom subpopulation. Secondly, the learning strategy is designed, focusing on local search for the top subpopulation and the learning strategy paying more attention to global search for the bottom subpopulation, which can well balance the exploration and exploitation. Finally, particles that fall into local optima will be reset by crossover with the global optima dimensionally, which can not only effectively avoid premature but also significantly reduce invalid iteration. The experiment results on 10 benchmark problems and CEC2017 optimization problems demonstrate that DPMPSO performs better than some representative PSOs and other optimization algorithms with significant difference.