An adaptive evolutionary algorithm with coordinated selection strategies for many-objective optimization

Striking a balance between convergence and diversity matters considerably for evolutionary algorithms in solving many-objective optimization problems. The performance of these algorithms depends on their capability of obtaining a set of uniformly distributed solutions as close to the Pareto optimal front as possible. However, most existing evolutionary algorithms encounter challenges in solving many-objective optimization problems. Thus, in this paper, an adaptive many-objective evolutionary algorithm with coordinated selection strategies, labeled ACS-MOEA, is proposed to balance the convergence and diversity. The coordinated selection strategies include three selection strategies, i.e., the selection based on shifted-dominated distance, the selection based on objective vector angle, and the selection based on Non-Euclidean geometry distance. The first is used in the mating selection process to select high-quality parents for the generation of good offspring. Both the second and the third selection strategies are employed in the environmental selection process to delete poor solutions one by one for preserving the elitist solutions of the next generation. The performance of ACS-MOEA is verified by comparing it with six state-of-the-art algorithms on several well-known benchmark test suites with up to 10 objectives. Experimental results have fully demonstrated the competitiveness of ACS-MOEA in balancing convergence and diversity. Moreover, the proposed ACS-MOEA has also been verified to be effective in solving constrained many-objective optimization problems.