Differential Evolution with Success Rate-based adaptation CL-SRDE for Constrained Optimization

Constrained numerical optimization problems introduce significant challenges for optimization methods. One of the popular heuristic optimization techniques for such problems is the Differential Evolution algorithm. This study focuses on applying a variant of differential evolution with two populations to the set of benchmark problems from the CEC 2024 Constrained Single Objective Numerical Optimization competition. In the proposed CL-SRDE algorithm the adaptation of the scaling factor is performed based on the success rate, which is the number of replaced individuals during selection divided by population size. The constraints are handled by a modified epsilon-constraint method. The analysis of the experimental results show that the used adaptation scheme achieves better feasibility rates and overall performance, compared to the alternative approaches.