关键词: 自适应, 粒子群算法, 动态邻居, 广义学习, 多峰函数

Abstract: As Particle Swarm Optimization (PSO) may easily get trapped in a local optimum, an improved PSO based on adaptive dynamic neighborhood and comprehensive learning named ADPSO was proposed. In ADPSO, the neighbors of each particle were dynamically constructed in terms of the best performing particle among the current particle neighborhood. Then the learning mechanism of each particle was separated into three parts: its own historical best position, the best neighbor and the global best one. At the position of the new particle, a random position around itself was added to increase the probability for the particle to move to that promising region. The test results on benchmark functions show that ADPSO achieves better solutions than other improved PSO, and it is an effective algorithm to solve multi-objective problems.

Key words: adaptive, Particle Swarm Optimization (PSO), dynamic neighbor, comprehensive learning, multimodal function