并行混合免疫遗传算法及其应用

A Parallel Hybrid Immune Genetic Algorithm and Its Application

以并行遗传算法(PGA)为基础,对其早熟、收敛慢等缺陷加以改进,提出一种并行混合免疫遗传算法(PHIGA)。该算法将免疫原理引入到遗传算法中,提高了算法的整体性能。这主要表现在一方面免疫选择可有效地防止早熟,另一方面基于免疫记忆的子群体信息交换策略可加速收敛。算法采用混沌初始化和基于自适应交叉、变异的多种群搜索,与单纯形法的混合可更好地改善其局部搜索性能。文中布局问题的算例验证了该算法的可行性和有效性。

The authors propose a parallel hybrid immune genetic algorithm(PHIGA)based on parallel genetic algorithms (PGA)in order to overcome some defects of them,such as premature convergence and slow convergence rate.The global performance of the algorithm is improved by introducing immunity theory into PGA.This is revealed in the following two aspects.One is that immune selection can prevent the algorithm from premature.The other is that convergence rate can be accelerated by individual migration strategy between subpopulations based on immune memory mechanism.In this algorithm,chaos initialization and multiple subpopulations evolution based on adaptive crossover and mutation are adopted.To be hybridized with simplex the method can further improve local searching performance of the algorithm.An example of layout problems shows that PHIGA is feasible and effective.