A novel immune evolutionary algorithm incorporating chaos optimization

Abstract

Making use of the ergodicity and internal randomness of chaos iterations, a novel immune evolutionary algorithm based on the chaos optimization algorithm and immune evolutionary algorithm is presented to improve the convergence performance of the immune evolutionary algorithm. The novel algorithm integrates advantages of the immune evolutionary algorithm and chaos optimization algorithm. Chaos variables are loaded into the variable colony of the immune algorithm in the immune evolutionary algorithm, tiny disturbance is introduced into the memory colony, and the disturbance amplitude is gradually adjusted based on the characteristic of chaos search. The experimental results indicate that the new immune evolutionary algorithm improves the convergence performance and search efficiency of the immune evolutionary algorithm.