A New Artificial Glowworm Swarm Optimization Algorithm Based on Chaos Method

Abstract

AbstractArtificial glowworm swarm optimization (GSO) algorithm can effectively capture all local maxima of the multi-modal function, but it exist some shortcomings for searching the global optimal solution, such as the slow convergence speed, easily falling into the local optimum value, the low computational accuracy and success rate of convergence. According to the chaotic motion with randomness, ergodicity and intrinsic regularity, this paper proposes an improved artificial GSO algorithm based on the chaos optimization mechanism, which adopts the chaotic method to locally optimize the better points that are searched by GSO algorithm. Finally, the experimental results based on the six typical functions shows that the improved algorithm has good convergence efficiency, high convergence precision, and better capability of global search and local optimization.KeywordsArtificial glowworm swarm optimizationchaos methodfunction optimization