Jul 2026· GECCO Companion· pp. 713-716· 0 citations· 6 references
Computer Science
TL;DR
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.
Abstract
A continuous development of metaheuristics necessitates a deeper understanding of internal search mechanisms, specifically the transition between exploration and exploitation, which is often overlooked in favour of final fitness values. This study introduces a standardized framework based on a Normalized Positional Diversity Index (D*) to quantify optimizer behaviour. We demonstrate that D* is a geometric generalization of existing measures, such as the one proposed by Hussain et al. [1], by replacing stochastic, path-dependent historical maximums with a fixed global upper bound anchored to the search space geometry. This transformation eliminates post-hoc bias and provides a scale-independent diagnostic. From D*, additional metrics including Area Under the Curve (AUC) and crossover point (txover) are derived to identify exact phase transitions. We evaluate five optimizers (GWO, DE, PSO, AGOA, and EES) across CEC 2020 benchmarks. Results confirm that D* offers consistent diversity monitoring with lower Coefficients of Variation compared to existing measures. Code and supplementary materials are available at: [https://github.com/saiddddd/D-Star-Metric-Optimization].
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.
Ziadan Qowi, Akhdan Musyaffa Firdaus, Hari Purnama· The eurasia proceedings of s...· 0 citations
Applied to five engineering optimization problems, modified momentum–leader–differential grey wolf optimizer consistently achieves the lowest objective values, while analytically proving its ability to navigate heavily penalized boundaries and satisfy all constraints.
Xin Su, Yichen Liu· Cluster Computing· 0 citations
This paper compares six different strategies identified in a recent survey, categorizing them as either global strategies (GS), which treat the decision vector as a unified type, or decomposition-based strategies (DS), which split the problem into continuous and discrete subproblems.
Thomas Lang, Denis Pallez· GECCO Companion· 0 citations
This paper introduces the bi-objective problem class CLIMB and analyzes the runtime of GSEMO and the widely used NSGA-II on this problem, and proves that GSEMO and NSGA-II-DYN, a version of NSGA-II with dynamic population sizes, can find the Pareto front of CLIMB in expected fitness evaluations.
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.
Global optimization problems often involve nonlinear, multimodal, non-separable, and constrained search landscapes that challenge population-based metaheuristics under limited function-evaluation budgets. This paper proposes the camel foraging optimization algorithm (CFEOA), a mechanism-driven metaheuristic that combines endurance-regulated state control with bounded Grünwald–Letnikov fractional-order displacement memory. In CFEOA, endurance acts as an agent-specific variable regulating the transition between exploration and local refinement, while the bounded fractional-memory term introduces directional persistence from recent accepted displacements. The camel-foraging analogy is used only as an organizing metaphor; the search process is defined through explicit mathematical operators, boundary repair, acceptance rules, and function-evaluation accounting. CFEOA is evaluated using 30 independent runs, fixed random seeds, equal function-evaluation budgets, disabled early stopping in the main benchmark comparisons, full parameter disclosure, and public code availability. The main validation uses the CEC 2022 suite with classical swarm baselines and adaptive DE-family comparators, including NL-SHADE-LBC as an additional recent SOTA reference. Across 18 function–dimension blocks, NL-SHADE-LBC achieves the most favorable aggregate rank, whereas CFEOA retains statistically significant advantages over PSO, GWO, and WOA and shows no significant difference from DE, JADE, SHADE, L-SHADE, and TLBO after Holm correction. Component-wise ablation, parameter sensitivity, convergence, diversity, CPU-time, scalability, and constrained engineering analyses indicate that CFEOA provides a transparent, reproducible, and reliability-oriented search framework with landscape-dependent strengths rather than universal SOTA dominance. The results position CFEOA as a controlled-search alternative for selected complex and constrained optimization settings where repeatability, feasibility, and auditable mechanism design are important.
