Constrained Multi-Objective Evolutionary Algorithm with Population Image Sharpening

Various real-world problems can be attributed to constrained multi-objective optimization problems, and many techniques have been proposed to deal with them. However, how to generate a promising offspring population according to the parent population is still a challenge. Given this, a constrained multi-objective evolutionary optimization method with population image sharpening is proposed. In this method, a column-oriented Sobel operator is introduced to participate in the population reproduction, which can be embedded into existing evolutionary algorithms easily. The proposed method is compared with seven state-of-the-art constrained evolutionary algorithms on two test suites and applied to the dispatch optimization problem of integrated coal mine energy systems. Experimental results verify the effectiveness and superiority of the proposed method in solving constrained multi-objective optimization problems.