Quantifying the Impact of Boundary Constraint Handling Methods on Differential Evolution
This work addresses the overlooked impact of boundary constraint handling on metaheuristic performance, providing practical insights for researchers and practitioners in optimization.
The study systematically benchmarks 28 Differential Evolution variants paired with 13 boundary constraint handling methods on the BBOB/COCO problem set, finding that the choice of BCHMs significantly affects performance and the number of infeasible solutions, leading to general selection guidelines.
Constraint handling is one of the most influential aspects of applying metaheuristics to real-world applications, which can hamper the search progress if treated improperly. In this work, we focus on a particular case - the box constraints, for which many boundary constraint handling methods (BCHMs) have been proposed. We call for the necessity of studying the impact of BCHMs on metaheuristics' performance and behavior, which receives seemingly little attention in the field. We target quantifying such impacts through systematic benchmarking by investigating 28 major variants of Differential Evolution (DE) taken from the modular DE framework (by combining different mutation and crossover operators) and $13$ commonly applied BCHMs, resulting in $28 \times 13 = 364$ algorithm instances after pairing DE variants with BCHMs. After executing the algorithm instances on the well-known BBOB/COCO problem set, we analyze the best-reached objective function value (performance-wise) and the percentage of repaired solutions (behavioral) using statistical ranking methods for each combination of mutation, crossover, and BBOB function group. Our results clearly show that the choice of BCHMs substantially affects the empirical performance as well as the number of generated infeasible solutions, which allows us to provide general guidelines for selecting an appropriate BCHM for a given scenario.