Backtracking immune algorithm for continuous multi-objective optimisation

Abstract

In this paper, a new multi-objective immune algorithm (MOIAs) called BIAMO is proposed for tackling continuous multi-objective optimisation problems. It uses the updated archive to sort the non-dominated solutions of the Pareto front as well as the mutation and crossover operators of the backtracking search algorithm (BSA). Experimental results are produced for various benchmark problems and for a variety of engineering design problems. They show that, compared to the recent multi-objective optimisation evolutionary algorithms, the proposed algorithm improves not only the convergence capacity but also preserves the diversity of the population. In this paper, thirteen benchmark and engineering design problems are presented and the obtained results were compared with other well-known optimisers. The obtained results demonstrate that the proposed algorithm requires less number of function evaluations and in most cases gives better results compared to others considered algorithms.