An archive-based artificial bee colony optimization algorithm for multi-objective continuous optimization problem

Research on multi-objective optimization (MO) has become one of the hot points of intelligent computation. In this paper, an archive-based multi-objective artificial bee colony optimization algorithm (AMOABC) is proposed, in which an external archive is used to preserve the current obtained non-dominated best solutions, and a novel Pareto local search mechanism is designed and incorporated into the optimization process. To prevent the searching process from being trapped into local minimum, a novel food source generating mechanism is put forward, and different search strategies are designed for bees and local search process. Comprehensive benchmarking and comparison of AMOABC with the some current-related MO algorithms demonstrate its effectiveness.