Jul 2026· 2026 6th International Conference on Electrical, Computer and Energy Technologies (ICECET)· pp. 1-7· 0 citations· 18 references
Abstract
Support Vector Regression (SVR) is a reliable and generalizable model for non-linear regression, but its performance depends strongly on selecting proper hyperparameters such as the penalty parameter $C$, kernel width $\gamma$, and the $\epsilon$-insensitive loss. Because the SVR objective is highly nonconvex with a multimodal error surface, manual tuning and grid search become inefficient. Metaheuristic optimizers therefore offer a more practical alternative through population-based search. Although Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) have both shown promise, each has clear limitations: PSO converges quickly but often stagnates in local optima due to loss of diversity, whereas GA preserves diversity but converges slowly because of excessive random exploration. To address these complementary weaknesses, this work introduces a mathematically formulated hybrid framework called Adaptive PSO-GA (A-PSO-GA). Experiments on the Housing, Concrete, and Abalone benchmarks show that A-PSO-GA consistently outperforms PSO-only, GA-only, and non-adaptive hybrid baselines in RMSE, MAE, MAPE, and $R^{2}$. It also achieves faster and more stable convergence across multiple seeds, demonstrating improved prediction accuracy, robustness, and optimization reliability. These results confirm that mathematically justified adaptive mechanisms are important for effective evolutionary hyperparameter tuning of SVR.
Gradient injection helps Particle Swarm Optimization (PSO) only when the swarm has identified a basin with smooth local structure, not universally. We propose Adaptive Hybrid PSO (AHPSO), which uses a sigmoid function on swarm diversity to automatically modulate gradient influence: near-zero during exploration, near-maximum during exploitation, with no manual phase-switching. Under budget-normalized comparison (PSO given equivalent total function evaluations), PSO wins 52.5% of 40 configurations versus AHPSO's 20% (p = 7.0e-5, Friedman). AHPSO retains advantage specifically on problems with smooth local basins (F8, F24-F27) where directed descent outperforms undirected sampling even at equal cost. Under iteration-matched comparison across 29 functions (42 configurations, 14,700 runs), AHPSO-Adadelta ranks first of 9 methods including CMA-ES (p = 9.75e-4). The contribution is a principled characterization of when gradient injection provides value in swarm-based search, not a claim of universal superiority.
Results show that integrating local search significantly enhances performance, while a principled method for setting hybrid parameters ensures robustness and reproducibility, highlighting the potential of combining mathematical programming techniques with evolutionary algorithms for high-dimensional many-objective optimization problems.
Regina C. L. C. de Sousa, Dênis E. C. Vargas, Elizabeth F. Wanner et al.· Journal of Heuristics· 0 citations
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.
Dieyan Liang, Jun-Hang Du, Jun Zhou et al.· 2026 3rd World Conference on...· 0 citations
Comprehensive experiments on numerical benchmarks and neuroevolution robotic control tasks demonstrate that AutoPSO consistently discovers novel PSO variants that significantly outperform strong baselines and confirm that AutoPSO achieves increasing performance gains with larger swarm sizes.
Xin-Meng Yu, Jiaxin Gao, Jianguo Zhang et al.· IEEE Transactions on Evoluti...· 0 citations
Many Particle Swarm Optimization (PSO) variants often fail to approach the global optimum due to complex fitness landscapes and/or the structural rigidity of these variants. We developed a novel framework named PSO-SEM, centered on macro-evolutionary population management. PSO-SEM introduces three landscape-driven operators: Split (Topological Fission), Extinction (Density-driven Recycling), and Migration (Knowledge Transfer). These operators autonomously regulate the lifecycle of sub-swarms to intensify search in high-potential areas by identifying promising basins and recycling computational resources from stagnant regions. Experimental results show that PSO-SEM achieves a top-tier ranking and demonstrates significant competitiveness against 14 state-of-the-art algorithms. Behavioral monitoring and ablation studies confirm PSO-SEM's ability to maintain autonomous exploration/exploitation balance through landscape-based computational resource re-allocation. Our findings verify that PSO-SEM is an interpretable architecture that meets the diverse requirements of black-box optimization.
This study introduces a novel constrained multi-objective evolutionary algorithm, termed DPCME, which employs two interacting populations that exchange information, enabling effective global exploration and reducing the risk of convergence to local optima.