关键词: 粒子群算法, 二元约束满足问题, 惯性权重, 适应度

Abstract: A Self-Adaptive Particle Swarm algorithm（SAPSO）is proposed.There exist two states for each particle in the SAPSO algorithm and a metric to measure a particle’s activity is defined which is used to choose which state it would reside.In order to ba-lance a particle’s exploration and exploitation capability for different evolving phases，a self-adjusted inertia weight varies dynamically with each particle’s evolution degree and the current swarm evolution degree is introduced into SAPSO algorithm.It uses the self-adaptive selection to select values from domains instead of random selection.This strategy searches in the promising solution space for global solution when the particles exploit the search space.It tests the hybrid algorithm（SAPSO） with random constraint satisfaction problems.The experimental results show that the hybrid particle swarm algorithm（SAPSO） can converge to the global solution faster.The efficiency of algorithm is increased by 2 times and average iteration times are reduced to a half of the former.

Key words: Particle Swarm algorithm（PSO）, binary constraint satisfaction problem, inertia weight, fitness