Adaptive Particle Swarm Optimizer Combining Hierarchical Learning With Variable Population
提出一种适应性粒子群优化器PSO-HV，通过堆式分层结构组织粒子向更优个体学习，并引入自适应可变种群策略消除冗余粒子，在57个基准函数上验证了其有效性和效率。
Particle swarm optimizer (PSO) is an optimization technique that has been applied to solve various problems. In its variants, hierarchical learning and variable population are two commonly used learning strategies. The former is used to employ more potentially good particles to lead the swarm, which is very effective in the early search phase. However, in the later search phase, such mechanism impedes PSO’s convergence. This work proposes an adaptive particle swarm optimizer combining hierarchical learning with variable population (PSO-HV), in which a heap-based hierarchy is first proposed to organize particles to hierarchically learn from the ones with better fitness in the same and upper levels. The levels of particles are determined and updated according to their current fitness in each iteration. Meanwhile, an adaptive variable population strategy is introduced and eliminates redundant particles based on the population’s evolution state. In this way, the swarm is more explorative upon the hierarchical structure and improves its exploitation capability due to the variable population mechanism. Ten state-of-the-art PSO contenders, including two hierarchical ones and two variable population-based ones, are compared with the proposed method on 57 benchmark functions and the experimental results verify its effectiveness and efficiency.