COARSE-EMOA: An indicator-based evolutionary algorithm for solving equality constrained multi-objective optimization problems

Many real-world applications involve dealing with several conflicting objectives which need to be optimized simultaneously. Moreover, these problems may require the consideration of limitations that restrict their decision variable space. Evolutionary Algorithms (EAs) are capable tackling Multi-objective Optimization Problems (MOPs). However, approaches struggle accurately approximate a feasible solution when considering equality constraints as part problem due inability EAs find and keep solutions exactly at constraint boundaries. Here, we present an indicator-based evolutionary multi-objective optimization algorithm (EMOA) for Equality Constrained MOPs (ECMOPs). In our proposal, adopt artificially constructed reference set closely resembling Pareto front ECMOP calculate Inverted Generational Distance population, is then used density estimator. An empirical study over benchmark each contains least one was performed test capabilities proposed COnstrAined Reference SEt - EMOA (COARSE-EMOA). Our results compared those obtained by six other EMOAs. As will shown, COARSE-EMOA can properly guiding search through use approximates given problem.