Adaptive parallel immune algorithm with orthomutation

Aiming at the slow convergence problem of clonal selection algorithm, this paper proposes an adaptive parallel immune algorithm with orthomutation (APIA). By adopting adaptive parallel search strategy with orthomutation operator in the memory base, the APIA can strength the ability of local directed search and jump out of local optimization. In addition, this proposed algorithm improves hypermutation operator to increase operational efficiency of the algorithm. The simulation experiment results show that the APIA has better optimizing capacity than clonal selection algorithm and traditional generic algorithm, and effectively increases the convergence speed and shortens the search time.