Information technologies A Multi-objective Ant Colony Optimization Algorithm Based on Elitist Selection Strategy

Abstract Multi-objective optimization problem is a kind of common optimization problem in science and engineering. This paper explores the improvement strategy of multi-objective ant colony algorithm and proposes an Elitist Multi-objective Ant Colony Optimization (EMOACO). This method proposes to improve ant colony fitness based on Pareto non-dominated set, performs local search on every individual generated in the ant colony algorithm and accelerates the parallel search of multiple objectives by adopting elite selection strategy in order to increase its search rate. The experimental result shows that the algorithm of this paper is effective and that it makes some improvements in global optimization capacity and population diversity compared with the basic multi-objective ant colony algorithm, it can quickly converge to Pareto optimal solution and provide a reliable basis for the decision making