Constrained Optimization by Artificial Bee Colony Framework

Abstract

In this paper, a novel artificial bee colony (ABC) algorithm for constrained optimization problems (COPs), named COABC, is proposed. The proposed approach treats a COP as a bi-objective optimization problem where the first one remains the same objective function itself while the second one is the degree of constraint violations. Then, the whole population is classed into dual subpopulations based on the partition method. The feasibility rule and the $\varepsilon $ constrained method are employed to compare two solutions in two subpopulations, respectively, which can archive a suitable balance between infeasible solutions and feasible solutions. Next, a multistrategy technique which consists of three diverse search strategies is served as the search method on the two subpopulations. This technique plays a major part in balancing between the diversity and the convergence. Finally, the comparison results on a set of benchmark functions denote that COABC performs competitively and effectively when compared with the selected state-of-the-art algorithms.