A decomposition-based many-objective ant colony optimization algorithm with adaptive reference points

Abstract

The discrete many-objective problem (MaOP) is challenging in practice. Improving the convergence speed and making the nondominated solutions close to the Pareto front (PF) are vital issues in the optimization of the discrete MaOP. This pper proposed a modified decomposition-based many objective ant colony optimization (ACO) algorithm and employs an adaptive reference point mechanism that chooses the ideal point or nadir point as the reference point according to the distribution of the candidate solutions. This mechanism is utilized to improve the selection operator, accelerate the convergence speed and enhance the optimization ability. A comparative experiment is conducted on traveling salesman problems (TSPs) constrained by two, five, and ten objectives, which are built using test cases from the TSPLIB. The experimental results indicate that the inverted generational distance (IGD) indicator of the nondominated solution of the proposed algorithm has a rapid convergence speed and that the proposed algorithm achieves competitive performance regarding its optimization quality.