An Adaptive Multi-Objective Particle Swarm Optimization Algorithm Based on Capacity-Driven Dual-Archive

Abstract

Existing multi-objective particle swarm optimisation (MOPSO) algorithms often face bottlenecks when handling complex frontiers, such as gene loss caused by truncation of a single archive and search imbalance resulting from parameter stagnation. To address these challenges, this paper proposes a novel algorithm (CDAMOPSO) based on capacity-driven dual archiving and multi-source feedback coordination. The algorithm innovatively constructs an “overflow-mutation-refill” dual-repository mechanism, which transfers congested solutions overflowing from the main repository to the auxiliary repository for differential activation. This effectively reawakens dormant genes and breaks through population evolutionary stagnation. Concurrently, topological features such as population congestion and dispersion are extracted as feedback signals, and an adaptive closed-loop control law for parameters is designed to achieve dynamic, on-demand allocation of computational resources between global exploration and local exploitation. Furthermore, by introducing a sparse-grid-guided “vanguard-main force” hierarchical strategy, the algorithm utilises asymmetric collaboration among heterogeneous particles to accurately map complex Pareto fronts, including discontinuous and long-tail fronts. Comparative experiments based on 22 standard test sets and 10 mainstream algorithms confirm that CDAMOPSO achieves significant statistical advantages in both convergence accuracy (IGD) and comprehensive coverage (HV), demonstrating outstanding performance in solving complex multi-objective problems.