A novel multi-population coevolution strategy for single objective immune optimization algorithm

Abstract

A novel multi-population coevolution strategy for single objective immune optimization algorithm (MCIA) is proposed to solve numerical and engineering optimization problem in real world from the inspiration that how neuro-endocrine system affects T cells and B cells in immune system eliminate the danger. The main idea of MCIA is to promote three populations to coevolution through self-adjusted clone operator, the applied dislocation arithmetic crossover, cloud self-adapting mutation operator and local search operator to produce lymphocyte with high affinity, where several operators have the capability of broadening the elites search space, boosting the global and local search around elites in search space. The MCIA is population B, population T, and assistant population A carrying on parallel evolution in nature, which simulates the immune system more comprehensively and unique in the aspects: clone operator and selected elite elements in the memory population enable the search space be broadened and compressed, and with the help of the cloud model characterized with randomness and stable topotaxis and local search technique, the global and local search is integrated to find the global optima with high population diversity. The performance comparisons of MCIA with three known immune algorithms and three optimization algorithms in optimizing 12 benchmark functions indicate that MCIA is an effective algorithm for solving global optimization problems with high precision, good robustness and low time complexity.