关键词: 粒子群算法, 加速系数, 非线性

Abstract:

The acceleration coefficients in particle swarm optimization have impact on the cognitive and social learning of particles, which are always the constants in traditional algorithms. According to some research, cognitive and social learning factors are two important parameters associated with the global and local search performance of particle swarm optimization respectively. A larger cognitive coefficient provides better global search ability, and the better local search ability can  be obtained with a larger social coefficient. In order to study the impact of the acceleration coefficients on particle swarm optimization further, three types of dynamic nonlinear acceleration coefficients based on a linear time variation strategy are proposed in the paper, and four benchmark functions are used to test the performance. Simulations show that the performance of nonlinear strategies is better than linear ones, and the best performance can be obtained with the arctangent variation acceleration coefficients.

Key words: particle swarm optimization;acceleration coefficient;nonlinear