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MLS-JAYA: A multi-learning JAYA algorithm for numerical optimization and efficient engineering design
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DOI：10.1007/s10586-025-05873-1.png)
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Abstract
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To address the persistent challenge of balancing diversity preservation and convergence acceleration in metaheuristic optimization, we introduce MLS-JAYA - an enhanced algorithm incorporating several learning mechanisms. The proposed approach integrates population-based incremental learning to model promising solution distributions, a refined search operator for accelerated convergence, and orthogonal opposition-based learning to prevent premature stagnation. Comprehensive evaluation on the CEC2017 benchmark suite and six mechanical engineering design problems demonstrates MLS-JAYA’s competitive performance. Practical application in wireless sensor network (WSN) node coverage optimization further validates its effectiveness. Comparative analysis against four JAYA variants and other state-of-the-art metaheuristics confirms the algorithm’s superior optimization capabilities across diverse problem domains. The source code of MLS-JAYA is publicly available at https://github.com/denglingyun123/MLS-JAYA .
Keywords:
Enhanced JAYA algorithm
Probability distribution learning
Convergence acceleration
Orthogonal opposition
Engineering optimization
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