Q-learning whale optimization algorithm for test suite generation with constraints support

Hassan, Ali Abdullah; Abdullah, Salwani; Zamli, Kamal Z.; Razali, Rozilawati

doi:10.1007/s00521-023-09000-2

Neural Comput & Applic

2023

DOI: 10.1007/s00521-023-09000-2

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Q-learning whale optimization algorithm for test suite generation with constraints support

Ali Abdullah Hassan,

Salwani Abdullah,

Kamal Z. Zamli

et al.

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2024

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Cited by 2 publications

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Advancements in Q‐learning meta‐heuristic optimization algorithms: A survey

Yang,

Gao,

Ding

et al. 2024

WIREs Data Min & Knowl

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This paper reviews the integration of Q‐learning with meta‐heuristic algorithms (QLMA) over the last 20 years, highlighting its success in solving complex optimization problems. We focus on key aspects of QLMA, including parameter adaptation, operator selection, and balancing global exploration with local exploitation. QLMA has become a leading solution in industries like energy, power systems, and engineering, addressing a range of mathematical challenges. Looking forward, we suggest further exploration of meta‐heuristic integration, transfer learning strategies, and techniques to reduce state space.This article is categorized under: Technologies > Computational Intelligence Technologies > Artificial Intelligence

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Advancements in Q‐learning meta‐heuristic optimization algorithms: A survey

Yang,

Gao,

Ding

et al. 2024

WIREs Data Min & Knowl

View full text Add to dashboard Cite

show abstract

Q-learning based vegetation evolution for numerical optimization and wireless sensor network coverage optimization

Zhong,

Peng,

et al. 2024

Alexandria Engineering Journal

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Q-learning whale optimization algorithm for test suite generation with constraints support

Cited by 2 publications

References 31 publications

Advancements in Q‐learning meta‐heuristic optimization algorithms: A survey

Advancements in Q‐learning meta‐heuristic optimization algorithms: A survey

Q-learning based vegetation evolution for numerical optimization and wireless sensor network coverage optimization

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