A Modified Particle Swarm Optimization Algorithm Based on Improved Chaos Search Strategy
Xue-Yao Gao, Liquan Sun, Chunxiang Zhang, Shou-ang Yang · 2008
Particle swarm optimization (PSO) algorithm is frequently employed to solve various optimization problems, but it easily gets into the local extremum in later evolution period. An improved chaos search strategy is introduced into PSO algorithm. When particles get into the local extremum, they are activated by chaos search strategy, and chaos search area are controlled in the neighborhood of the current optimal solution by reducing search area of variables, which avoids searching blindly. The new algorithm can not only solve local extremum problem effectively but also enhance the precision of convergence. Experiment results show that the proposed method is better than standard PSO algorithm in both precision and stability.