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| Formato: | Recurso digital |
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Zenodo
2026
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| Acceso en línea: | https://doi.org/10.5281/zenodo.18357887 |
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- <p><span>Swarm optimization algorithms are widely studied due to their lack of gradient information requirement and good adaptability to complex non-convex problems. However, most existing methods still rely on immediate fitness-driven and single global optimum guidance mechanisms, which are prone to premature convergence, rapid loss of search diversity, and excessive preference for unstable solutions in high-dimensional, multimodal, noisy, or dynamic environments. To address these limitations, this paper proposes a novel swarm optimization method—the cognitive yellow-throated marten optimization algorithm. This method no longer treats the optimization process as a simple random search or heuristic iteration, but models it as a dynamic decision-making system driven by counterfactual utility evaluation, cognitive differentiation subgroup cooperation, and game consistency constraints. The algorithm introduces a counterfactual utility function to characterize the difference in potential gains between individual actions and not taking that action, achieves self-organized division of labor in exploration, evaluation, and utilization within the group through a cognitive differentiation mechanism, and extends the optimization objective from a single-point optimum to a set of solutions with robustness and evolutionary stability by constructing a stable and survivable solution set. Theoretical analysis shows that this method has stronger stability and continuous search capability in perturbed environments, providing a new research paradigm for swarm optimization</span>.</p>