A hybrid particle swarm algorithm with embedded chaotic search

Abstract

A new hybrid evolutionary-based method combining the particle swarm algorithm and the chaotic search is proposed for optimizing. To achieve high performance in optimizing, the chaotic search mechanism is embedded in the standard particle swarm algorithm adaptively to avoid the stagnancy of population and increase the speed of convergence. This hybrid method makes use of the ergodicity of chaotic search to improve the capability of precise search and keep the balance between the global search and the local search. It has been compared with other methods such as standard particle swarm algorithm, standard genetic algorithm and improved particle swarm algorithm. In comparison, the proposed method shows its superiority in convergence property and robustness. It is validated by the simulation results.