Immune Quantum Evolutionary Algorithm Based on Chaotic Searching Technique for Global Optimization

Abstract

A novel immune quantum evolutionary algorithm based on chaotic searching for global optimization (CRIQEA) is proposed. Firstly, by niching methods population is divided into subpopulations automatically. Secondly, by using immune and catastrophe operator each subpopulation can obtain optimal solutions. Because of the quantum evolutionary algorithm with intrinsic parallelism it can maintain quite nicely the population diversity than the classical evolutionary algorithm; because of the immune operator and real representation for the chromosome it can accelerate the convergence speed. The chaotic searching technique for improving the performance of CRIQEA has been described; catastrophe operator based on chaotic dynamic systems is capable of escaping from local optima. Simulation results demonstrate the superiority of CRIQEA in this paper.