融合微粒群的多种群协同进化免疫算法

提出一种融合微粒群的多种群协同免疫优势克隆选择算法(PMCICA).该算法将生态学中的协同进化思想引入人工免疫算法中,各子种群内部通过免疫优势克隆选择操作加快了种群收敛速度;所有子种群共享经过改进微粒群优化的高层优良库,实现了整个种群信息共享与协同进化.针对旅行商问题(TSP)的多个实验结果表明,该算法在收敛速度与最优解等方面均取得了较好的效果.

Multi-population coevolutionary immunodominance clonal selection algorithm combining particle swarm optimization

Multi-population coevolutionary immunodominance clonal selection algorithm combining particle swarm
optimization(PMCICA) is proposed. Enlightened by the knowledge of ecological environment and population competition,
the cooperative evolution in the field of ecology is incorporated into artificial immune system. The convergent speed of
algorithm is enhanced by local optimization immunodominance operating, clonal selection operation within the species.
All subpopulations share one memory which is also used as a leader set consisting of the dominant representatives of each
evolved subpopulation. The high level memory is optimized by using an improved particle swarm optimization(IPSO).
Through those operations, information is shared among populations for co-evolution. The experiments on traveling salesman
problems(TSP) benchmarks show that the proposed algorithm is capable of improving the search performance significantly
in convergent speed and precision.