MLDGWO: a grey wolf optimizer with momentum, leader adjustment, and differential perturbation for global optimization problems

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

Quantifying the Explore-Exploit Trade-off in Metaheuristics via Normalised Positional Diversity

This study introduces a standardized framework based on a Normalized Positional Diversity Index (D*) to quantify optimizer behaviour and demonstrates that D* is a geometric generalization of existing measures, by replacing stochastic, path-dependent historical maximums with a fixed global upper bound anchored to the search space geometry.

Weather State Ants Optimizer: A Markov-Driven Variable-Structure Metaheuristic

Metaheuristics require sustained global search without sacrificing local refinement, yet many variable-structure methods change operators through one-way iteration schedules. We introduce the Weather State Ants Optimizer (WSAO), in which a discrete-time Markov chain recurrently selects one of three population updates. Sunny, cloudy, and rainy states correspond to global exploration, movement toward nests, and local refinement, respectively. An archive-based mechanism also maintains several spatially separated nests as concurrent search centers. Thirty independent runs compared WSAO with 11 algorithms on 29 CEC2017 and 12 CEC2022 functions. WSAO achieved the lowest Friedman mean rank on both suites, at 2.48 and 2.33. Across five constrained design cases, it joined the leading group by mean objective value on four cases and ranked second on pressure-vessel design. Targeted CEC2022 controls showed that no alternative transition matrix dominated the baseline. Eliminating the trial perturbation worsened every selected function, whereas the contribution of multiple nests depended on the landscape structure. The combined evidence supports recurrent state-controlled search as a competitive framework for continuous numerical and constrained optimization.

SAWHALE: A Surrogate-Assisted Self-Adaptive Whale Optimization Algorithm with Novel Asymmetric Opposition-Based Learning for Expensive Optimization Problems

Expensive optimization problems allow for only a small number of exact objective evaluations, and this is where most metaheuristics lose their value. This paper proposes SAWHALE, a surrogate-assisted and self-adaptive whale optimization framework built around two new asymmetric opposition-based learning operators. EDOFAS schedules entropy-driven oppositional probes at several scales, while OPADAMP perturbs the worst coordinates of promising solutions. Surrogate models price the candidates of a global phase and a local phase, and a logistic rule switches between the phases according to the state of the population. Four experiments examine the framework. A component study over nine whale variants at 500 and 1000 dimensions places EDOFAS first, with mean Friedman ranks of 1.650 and 1.575. On the CEC 2014 suite, the full framework attains the best mean rank against five surrogate-assisted optimizers, 2.233 at 30 dimensions and 2.267 at 50, with more Wilcoxon wins than losses against every one of them. An ablation over seven configurations keeps the complete design first at 1.333 and 2.033. On the CEC 2017 suite, the framework ranks first at 1.767 and 1.600 against six metaheuristics, including three newer whale variants, and the cost of the machinery on smooth unimodal ground is reported openly.

Performance of multi-strategy optimized mayfly optimization algorithm for location selection of university reimbursement form submission terminals

The Mayfly Optimization Algorithm (MOA) is a swarm intelligence algorithm with competitive search capability, but it may suffer from premature convergence and unstable late-stage exploitation. This study proposes a multi-strategy optimized mayfly optimization algorithm (MSMOA) to improve the overall optimization performance of MOA. MSMOA integrates Logistic chaotic initialization, a nonlinear adaptive weighting factor, Lévy-flight perturbation, and a PSO-guided learning mechanism to increase initialization randomness, adjust the search process, introduce long-range stochastic perturbations, and provide additional information-sharing guidance. The algorithm was evaluated on scalable benchmark functions under 30D, 50D, and 100D settings and two fixed-dimensional functions, and was compared with MOA, PSO, GWO, SSA, and DESMA over 30 independent runs. MSMOA achieved the best overall average rank among the compared algorithms and maintained competitive performance across high-dimensional settings. Two-sided Wilcoxon rank-sum tests supported the statistical reliability of the observed performance differences, and ablation experiments suggested that the four components jointly contributed to the overall performance improvement. Finally, a reimbursement-terminal location-selection case provided a preliminary illustration of the applicability of MSMOA to a simplified static facility-location problem.

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.