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.