New operators for multi‐objective clonal selection algorithms

Abstract

PurposeThe purpose of this paper is to present a new multi‐objective clonal selection algorithm (MCSA) for the solution of electromagnetic optimization problems.Design/methodology/approachThe method performs the somatic hypermutation step using different probability distributions, balancing the local search in the algorithm. Furthermore, it includes a receptor editing operator that implicitly realizes a dynamic search over the landscape.FindingsIn order to illustrate the efficiency of MCSA, its performance is compared with the nondominated sorting genetic algorithm II (NSGA‐II) in some analytical problems and in the well‐known TEAM benchmark Problem 22. Three performance evaluation techniques are used in the comparison, and the effect of each operator of the MCSA in its accomplishment is estimated.Research limitations/implicationsIn the analytical problems, the MCSA enhanced both the extension and uniformity in its solutions, providing better Pareto‐optimal sets than the NSGA‐II. In the Problem 22, the MCSA also outperformed the NSGA‐II. The MCSA was not dominated by the NSGA‐II in the three variables case and clearly presented a better convergence speed in the eight variables problem.Practical implicationsThis paper could be useful for researchers who deal with multi‐objective optimization problems involving high‐computational cost.Originality/valueThe new operators incorporated in the MCSA improved both the extension, uniformity and the convergence speed of the solutions, in terms of the number of function evaluations, representing a robust tool for real‐world optimization problems.