Adaptive chaos parallel clonal selection algorithm for objective optimization in WTA application

Abstract

This paper presents a novel objective optimization approach based on clonal selection algorithm (CSA) to solve the problems of weapon-target assignment (WTA) in warship formation antiaircraft application. The proposed CSA, namely adaptive chaos parallel clonal selection algorithm (ACPCSA), combines the benefits of chaos theory and parallel population classification to realize the population initialization and population update. In this algorithm, Chaos regeneration and Chaos disturbance are creatively employed to design population initialization operator and hyper-mutation operator. And parallel population classification is adopted to design parallel mechanism for all sub-populations, which can keep the population diversity according to affinity. Besides, CSA is improved by adaptive clonal proliferation operator, antibody inhibition operator and antibody circulation supplement operator, where operators can improve global optimization ability and local searching ability. Finally, typical scenario is performed and compared by implementation of other algorithms. Simulation results show that the proposed ACPCSA has good optimization performance in terms of search accuracy and convergence flexibility, which can provide an effective way to solve WTA in warship formation antiaircraft application.