Handling Constraints using Multiobjective Optimization Concepts


In this paper, we propose a new constraint-handling technique for evolutionary algorithms which we call inverted-shrinkable PAES (IS-PAES). This approach combines the use of multiobjective optimization concepts with a mechanism that focuses the search effort onto specific areas of the feasible region by shrinking the constrained search space. IS-PAES also uses an adaptive grid to store the solutions found, but has a more efficient memory-management scheme than its ancestor (the Pareto archived evolution strategy for multiobjective optimization). The proposed approach is validated using several examples taken from the standard evolutionary and engineering optimization literature. Comparisons are provided with respect to the stochastic ranking method (one of the most competitive constraint-handling approaches used with evolutionary algorithms currently available) and with respect to other four multiobjective-based constraint-handling techniques.