Simulated annealing-based immunodominance algorithm for multi-objective optimization problems

Based on the simulated annealing strategy and immunodominance in the artificial immune system, a simulated annealing-based immunodominance algorithm (SAIA) for multi-objective optimization (MOO) is proposed in this paper. In SAIA, all immunodominant antibodies are divided into two classes: the active antibodies and the hibernate antibodies at each temperature. Clonal proliferation and recombination are employed to enhance local search on those active antibodies while the hibernate antibodies have no function, but they could become active during the following temperature. Thus, all antibodies in the search space can be exploited effectively and sufficiently. Simulated annealing-based adaptive hypermutation, population pruning, and simulated annealing selection are proposed in SAIA to evolve and obtain a set of antibodies as the trade-off solutions. Complexity analysis of SAIA is also provided. The performance comparison of SAIA with some state-of-the-art MOO algorithms in solving 14 well-known multi-objective optimization problems (MOPs) including four many objectives test problems and twelve multi-objective 0/1 knapsack problems shows that SAIA is superior in converging to approximate Pareto front with a standout distribution.