A fitness-assignment method for evolutionary constrained multi-objective optimization

The effectiveness of Constrained Multi-Objective Evolutionary Algorithms (CMOEAs) depends on their ability to explore diverse feasible regions within the problem search space by utilizing information from both feasible and infeasible solutions. While many high-performing CMOEAs have been proposed, they are often too complex due to their underlying multi-stage or multi-population design. To simplify the process, fitness-assignment-based CMOEAs have been proposed that integrate feasibility information into traditional methods from unconstrained multi-objective optimization. However, these approaches are not scalable in terms of performance because it is difficult to design a fitness assignment method that can simultaneously account for constraint violation, convergence, and diversity. Hence, in this paper, we propose an effective single-population fitness assignment-based CMOEA referred to as ISDE+c that can explore different feasible regions in the search space. ISDE+c is a fitness assignment-based algorithm, that is an efficient fusion of constraint violation (c), Shift-based Density Estimation (SDE), and sum of objectives (＋ ). This fusion facilitates the efficient use of information from infeasible solutions and ensures the algorithm can effectively span diverse feasible regions in the search space. The performance of ISDE+c evaluated in terms of Hypervolume and runtime complexity is favorably compared against 9 baseline CMOEAs on 6 different benchmark suites with diverse characteristics. The code of the proposed ISDE+c is publicly available at https://github.com/RammohanMallipeddi/Matlab-Codes-for-cISDE-.