A Hybrid DE-PSO Algorithm for Constrained Optimization Problems with Distinct Constraint Handling

Abstract

Constrained optimization problems (COPs) are widely encountered in real-world applications and remain challenging to solve. Accordingly, developing efficient solution methods for COPs continues to be an important research topic. This paper proposes a novel hybrid DE-PSO algorithm, termed Dual-Population DEPSO (DP-DEPSO), to address COPs. DP-DEPSO is built upon two key ideas. First, a dual-population scheme is adopted in which Differential Evolution (DE) and Particle Swarm Optimization (PSO) evolve in parallel. Second, population-distinct constraint-handling schemes are introduced, where the DE group employs Deb's feasibility rule to preserve and explore high-quality feasible solutions, while the PSO group utilizes Deb's rule and an ε constraint handling method to exploit promising regions. Through complementary search behaviors of the dual population, DP-DEPSO achieves a well-balanced exploration-exploitation search. The proposed method is evaluated on thirteen classical constrained benchmark problems and compared with state-of-the-art evolutionary algorithms and existing DEPSO. Experimental results demonstrate that DP-DEPSO attains superior solution quality and competitive convergence efficiency. Statistical analyses using the Wilcoxon signed-rank test further confirm its significant performance advantage over most comparison methods. These results indicate that DP-DEPSO provides an effective and robust framework for COPs.