An innovative copula-evolutionary strategy for constrained multi-objective optimization

This paper introduces an innovative multi-objective optimization methodology tailored to address constrained optimization challenges. The proposed approach synergistically integrates evolutionary algorithms with copula-based reliability modeling to systematically explore optimal solutions that concurrently optimize multiple objectives while maintaining adherence to probabilistic constraints. The efficacy of the algorithm was rigorously assessed via two distinct target functions, demonstrating its proficiency in identifying a collection of Pareto optimal solutions. Moreover, the document incorporates graphical representations of the fitness landscape, convergence rate, and algorithmic progression, offering valuable insights into the optimization procedure. The results of the proposed approach demonstrate its robustness and applicability in dealing with complex optimization problems characterized by multiple objectives and constraints.