An Artificial Bee Colony Algorithm for Multi-objective Optimization

Abstract

Multi-objective optimization methods are essential to resolve real-world problems. An artificial bee colony algorithm used to the multi-objective optimization problems is presented. In the algorithm, solutions with a smaller number of dominating solutions and a larger crowding distance are first chosen into the next generation, their vicinity is searched with a higher probability and at self-adjective steps, and the opposition-based strategy is applied to the initialization, to speed up the convergence to the Pareto optimal solution set and improve the distribution uniformity of the solutions in the objective space. The simulation results on multi-objective test functions verify the validity of the proposed algorithm.