A Many-Objective Evolutionary Algorithm Based on Indicator and Decomposition

In the field of many-objective evolutionary optimization algorithms (MaOEAs), how to maintain balance between convergence and diversity has been a significant research problem. With increase number objectives, mutually nondominated solutions increases rapidly, multi-objective algorithms, based on Pareto-dominated relations, become invalid because loss selection pressure in environmental selection. order solve this problem, indicator-based have proposed; however, they are not good enough at maintaining diversity. Decomposition-based methods achieved promising performance keeping paper, we propose MaOEA indicator decomposition (IDEA) keep simultaneously. Moreover, decomposition-based do work well irregular PFs. To tackle paper develops reference-points adjustment method learning population. Experimental studies several well-known benchmark problems show that IDEA is very effective compared ten state-of-the-art algorithms.