Jul 2026· 2026 3rd World Conference on Computer and Information Security (WCCIS)· pp. 5-10· 0 citations· 16 references
Abstract
This paper proposes an adaptive grouping-based hybrid PSO-GA algorithm to address PSO's premature convergence and GA's slow convergence. The algorithm dynamically partitions the population into elite/regular subgroups, applying PSO for local exploitation and GA for global exploration. Key innovations include adaptive grouping, dynamic weight adjustment, bidirectional elite migration, and stagnation-triggered multi-phase optimization. Experiments on 7 benchmarks show superior convergence accuracy, speed, and stability over standalone PSO/GA (especially for multimodal problems). Further validation on highdimensional $(\mathbf{D}=\mathbf{5 0} / \mathbf{1 0 0})$ CEC 2017 benchmarks, constrained engineering designs, and sensitivity analysis confirms its scalability, robustness, and practical applicability.
Traditional particle swarm optimization (PSO) easily falls into premature convergence, while differential evolution (DE) is highly sensitive to fixed control parameters. Existing PSO-DE hybrid frameworks suffer from static population sizes and insufficient cross-population information exchange. This paper proposes PSO-...
Yao-Pei Wang, Yufeng Wang, Ke Liu· Algorithms· 0 citations
Particle swarm optimization (PSO) is a widely used bio-inspired optimization method. However, global guidance can align particle trajectories, reduce population diversity, and lead to premature convergence. To address this issue, we propose GRACE-PSO, a particle swarm optimizer based on group rank assessment and cooper...
This paper proposes a Hierarchical and Stagnation‐Aware Particle Swarm Optimization with Elite‐Guided Crossover (HECO‐PSO). HECO‐PSO dynamically partitions the swarm into multiple parallel layers, enabling independent exploration and periodic information exchange to mitigate premature convergence. Within each layer, el...
Hao-Peng Lei, Jia-Hui Fan, Jihua Ye et al.· Concurrency and Computation· 0 citations
Experimental results demonstrate that the proposed Multi-swarm Collaborative Particle Swarm Optimization with Adaptive Learning Strategy with Adaptive Learning Mechanism exhibits significant advantages in convergence speed, solving accuracy, and comprehensive optimization capabilities compared with several state-of-the...
An enhanced sand cat swarm algorithm (ESC-SCSA) is developed and comprehensively evaluated through sensitivity analysis, ablation studies, convergence analysis, and statistical significance tests, demonstrating its effectiveness and potential generalization capability across both continuous and discrete optimization pr...
wirawati dewi ahmad, Azuraliza Abu Bakar, Mohd Nor Akmal Khalid· Engineering Research Express· 0 citations
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