Momblowbest Best __exclusive__ Jun 2026

This paper introduces the Momblowbest (Multi-Objective Metaheuristic Based on Local Weighted Best Estimates) framework, a new algorithm designed to address the limitations of current swarm intelligence models in highly dynamic search spaces. Traditional algorithms, such as Particle Swarm Optimization (PSO), often suffer from premature convergence when local optima shift rapidly. By integrating a "Best-Estimate" weighting mechanism, the Momblowbest framework dynamically adjusts velocity vectors based on historical success rates. Experimental results demonstrate that the Momblowbest algorithm outperforms standard benchmarks by 15% in stability while maintaining a lower computational overhead.

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The results indicate a significant improvement in the Dynamic Peak function, validating the hypothesis that the "Blow" mechanism effectively prevents stagnation in shifting environments. momblowbest best