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Optimizing Metaheuristic Algorithms via Reinforcement Learning-Driven Parameter Adaptation for Enhanced Global Search Capabilities

Authors: Pradeep Upadhyay

Published: 2025-05-02 10:34:01.810

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Abstract

Metaheuristic algorithms, celebrated for their efficacy in solving complex optimization problems, often rely on manually tuned parameters. The performance of these algorithms is highly sensitive to these parameters, and suboptimal settings can lead to premature convergence or inefficient exploration of the search space. This paper introduces a novel framework for dynamically adapting metaheuristic algorithm parameters using reinforcement learning (RL). Specifically, we employ Q-learning to train an agent that learns to adjust parameters of the Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) during the optimization process. The state space is defined by the current search progress and solution quality, while the action space consists of discrete parameter adjustments. The reward function is designed to incentivize exploration and exploitation based on the algorithm's performance. We evaluate the proposed framework on a suite of benchmark optimization problems, demonstrating significant improvements in solution quality, convergence speed, and robustness compared to static parameter settings and other adaptive approaches. The results indicate that RL-driven parameter adaptation offers a promising avenue for enhancing the global search capabilities of metaheuristic algorithms.

Keywords

Metaheuristic Algorithms, Reinforcement Learning, Parameter Adaptation, Global Optimization, Q-Learning, Particle Swarm Optimization, Genetic Algorithms, Exploration-Exploitation Balance, Artificial Intelligence, Swarm Intelligence.

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How to Cite

Pradeep Upadhyay, (2025-05-02 10:34:01.810). Optimizing Metaheuristic Algorithms via Reinforcement Learning-Driven Parameter Adaptation for Enhanced Global Search Capabilities. JANOLI International Journal of Artificial Intelligence and its Applications, Volume EOCMPeqBj5R9ZDur0Rlk, Issue 3.

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