AutoPSO: A Meta-Framework for Automated Particle Swarm Optimization

Automated PSO Configuration Framework via Deep Neural Networks and Function Landscape Descriptors

Particle Swarm Optimization (PSO) is a popular gradient-free meta-heuristic, but its many variants pose significant selection and tuning challenges for non-specialists. We introduce Automated Deep-learning-based Particle Swarm Optimization (AD-PSO), a framework that highly automates PSO configuration and execution. AD-PSO features a modular design, incorporating diverse PSO variants as interchangeable components. The core methodology involves training a hard-constrained Conditional Variational Autoencoder (CVAE) to map problem landscape features—obtained via Latin Hypercube Sampling (LHS)—to a near-optimal PSO configuration. This mapping is established a priori through meta-optimization. The user only specifies the maximum number of evaluations; AD-PSO then autonomously samples the function, computes its features, and uses the trained CVAE to generate and execute a tailored configuration. Experimental results demonstrate AD-PSO's strong adaptability. In 297 comparisons on the CEC2022 benchmark against 9 state-of-the-art competitors, AD-PSO won or tied in 239 cases (an 80.5% win/draw rate).

An Adaptive PSO–GA Hybrid Optimization with Diversity-Aware and Stagnation-Resistant Mathematical Framework for SVR

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.

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

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.

RLDMGO: a reinforcement learning-driven hybrid framework for complex optimization problems

A Comparative Study of Metaheuristics on Raspberry Pi 5: Results to Guide Optimization on Constrained Edge Devices

 This study investigates the efficiency of five metaheuristic algorithms, namely Differential Evolution (DE), Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Harmony Search (HS), and Particle Swarm Optimization (PSO), when deployed on a Raspberry Pi 5 edge device. The evaluation focuses on both optimization quality and computational cost, using four standard benchmark functions that represent a range of landscape characteristics: Sphere, Rosenbrock, Rastrigin, and Ackley. Each function is tested at dimensions 10, 30, and 50 to probe scalability. In addition to objective values, the experiments collect per-iteration processor usage and memory (RAM) to provide a practical view of runtime overhead under constrained resources. Among the five candidates, GWO consistently delivers the fastest or near-fastest convergence while keeping variability tight. Its trajectories show smooth descent across functions and dimensions, paired with comparatively modest CPU and RAM footprints. PSO typically ranks second in speed with stable dynamics, though brief CPU spikes often appear at early iterations as swarms synchronize. DE demonstrates resilience on rugged functions but generally requires more iterations to close the final gap. GA and HS can reach competitive objective values on some settings, yet they display wider dispersion and higher overhead at larger dimensions, which reduces their suitability for small devices. Overall, the evidence indicates that GWO is the most efficient choice for edge deployment on Raspberry Pi 5, striking a favorable balance between convergence speed, stability, and resource usage. PSO is a strong alternative when slightly higher processor activity is acceptable. These findings support the adoption of lightweight, variance-stable metaheuristics for edge optimization workloads where CPU and memory budgets are tight.

Adaptive Hybrid Particle Swarm Optimization with Gradient Descent

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.