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<p>A hybrid Differential Evolution with Linear Success History Adaptive Differential Evolution—Ocean Water current optimizer (L-SHADE-OWCO) algorithm is proposed to solve single-objective optimization problems. OWCO excels in exploitation, while L-SHADE offers effective exploration through mutation and success history updates. This hybrid enhances performance, achieving superior results compared to standalone OWCO and other CEC2021 algorithms. The proposed algorithm was evaluated on CEC2021 benchmark functions for scalability, exploration, and convergence across 10 and 20 dimensions. It outperformed existing algorithms, achieving a score of 96.193, ranking 2nd among competitors like DEDMNA, J21, MLS_SHADE, NL_SHADE, and MadDE, and demonstrating significant improvements over OWCO.</p>

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A Hybrid Approach with L-SHADE and OWCO: Towards a Faster Convergence Metaheuristic

  • 期刊论文

  • 发表日期：

    2025年7月22日

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  • Lavika Goel,
  • Siddharth Chand Ramola,
  • Aishwarya Mishra

摘要

A hybrid Differential Evolution with Linear Success History Adaptive Differential Evolution—Ocean Water current optimizer (L-SHADE-OWCO) algorithm is proposed to solve single-objective optimization problems. OWCO excels in exploitation, while L-SHADE offers effective exploration through mutation and success history updates. This hybrid enhances performance, achieving superior results compared to standalone OWCO and other CEC2021 algorithms. The proposed algorithm was evaluated on CEC2021 benchmark functions for scalability, exploration, and convergence across 10 and 20 dimensions. It outperformed existing algorithms, achieving a score of 96.193, ranking 2nd among competitors like DEDMNA, J21, MLS_SHADE, NL_SHADE, and MadDE, and demonstrating significant improvements over OWCO.