On the Advantages of Searching Infeasible Regions in Constrained Evolutionary-based Multi-Objective Engineering Optimization

Abstract Solving a multiple-criteria optimization problem with severe constraints remains a significant issue in multi-objective evolutionary algorithms (MOEA). The problem primarily stems from the need for a suitable constraint-handling technique for an MOEA. One potential approach is to balance the search in both feasible and infeasible regions to find the Pareto front efficiently. The justification for such a strategy is that the infeasible region also provides valuable information for the MOEA, especially in problems with a small percentage of feasibility areas. To that end, this paper investigates the potential of the infeasibility-driven principle based on multiple constraint ranking-based techniques to solve a multi-objective problem with a large number of constraints. By analyzing the results from intensive experiments on a set of test problems, including the realistic multi-objective car structure design and actuator design problem, it is shown that there is a significant improvement gained in terms of convergence and diversity by utilizing the generalized version of the multiple constraint ranking techniques.