A decomposition-based many-objective ant colony optimization algorithm with adaptive solution construction and selection approaches

The ant colony optimization algorithm (ACO) had an exceptional performance in solving discrete optimization problems because of its design in solution construction and search strategy. However, the study of ACO in discrete many-objective optimization remains insufficient. This paper proposes a decomposition-based ACO for discrete many-objective optimization. The proposed algorithm utilizes a reinforcement learning-based adaptive pheromone updating strategy that enhances the solution construction phase's searching ability in the high dimensional objective space. Furthermore, an adaptive selection strategy is adopted to improve its convergence performance using different reference points. And A comparative experimental study is conducted on many objective benchmark test cases. The experimental results indicate that the proposed algorithm achieves competitive performance on optimization quality.