An Immune Multiobjective Optimization with Backtracking Search Algorithm Inspired Recombination

Abstract

We propose a novel hybrid multiobjective (MO) immune algorithm for tackling continuous MO problems. Similarly to the nondominated neighbor immune algorithm (NNIA), it considers the characteristics of OM problems: based on the fitness values, the best individuals from the test population are selected and recombined to guide the rest of the individuals in the population to the Pareto front. But NNIA uses the simulated binary crossover (SBX), which uses the local search method. In our algorithm, the recombination is essentially inspired by the cross used in the backtracking search algorithm (BSA), but the adaptations are found in the immune algorithm. Thus, three variants are designed in this chapter, resulting in new recombination operators. They are evaluated through 10 benchmark tests. For the most advanced proposed variant, which is designed to have global search ability, results show that an improved convergence and a better diversity of the Pareto front are statistically achieved when compared with a basic immune algorithm having no recombination or to NNIA. Finally, the proposed new algorithm is demonstrated to be successful in approximating the Pareto front of the complex 10 bar truss structure MO problem.