Performance study of Multi-Objective Artificial Bee Colony (MOABC) Algorithm by Numerical Problems Benchmark

Multi-objective artificial bee colony (MOABC) is a meta-heuristic which belongs to the field of swarm intelligence techniques. The basic idea is to imitate the intelligent foraging behavior of honeybee swarms in order to solve multi-objective optimization problems. In this study, we present a novel version of the MOABC algorithm whose performances are evaluated through a benchmark of five multi-objective test functions. The simulation based technique is used to generate the non-dominated set of points of the Pareto fronts. A Comparison with the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is shown. The Generational Distance (GD) and Spacing (Sp) are used as performance metrics.

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