A multi-subgroup competition parallel immune evolutionary programming

Premature convergence is the fatal shortcoming of traditional immune evolutionary programming. Based on the analysis of premature convergence of traditional immune evolutionary programming, a novel Multi-subgroup Competition Immune Parallel Evolutionary programming is proposed. In the algorithm, evolutions of subgroups are parallelly performed with the different mutation strategies. The mutation capability of an individual is determined by the competition among subgroups. The individual, together with information, is exchanged while subgroup is reorganized. The mutation operators with different size are adopted to improve the local search ability while the super mutation operator is used to maintain the population diversity. Thus, population evolution can be actualized by concurrently operating. The simulations based on benchmarks confirm that this algorithm is better than classical evolutionary programming algorithm in the aspects of global optimization, convergence speed and the robustness.