A Co-Competitive Multiobjective Particle Swarm Optimization Algorithm Based on Dimensionality Reduction

Existing metaheuristic algorithms often struggle to balance diversity, convergence, and stability when solving complex, high-dimensional multiobjective optimization problems due to their sensitivity to parameter settings. To address this issue, we propose CMOPSO/DR, an adaptive co-competitive multiobjective PSO algorithm incorporating dimensionality reduction. First, the denoising adversarial autoencoder (DAAE) network is automatically determined to construct a compressed coordinate system (CCS) to accelerate convergence. Second, simulated binary crossover (SBX) is used to enhance both the diversity and stability of the algorithm. Finally, the co-competitive strategy of coupling CCS and SBX is developed to respond to various optimization demands at different optimization stages adaptively. The experimental results show that CMOPSO/DR performs better than other advanced optimization algorithms on various benchmark and real-world problems, and it achieves efficient acquisition of high-quality optimal solutions, demonstrating its obvious improvements in theory and application.