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<p>To solve complex engineering problems efficiently, an optimization algorithm must exhibit robust exploration and exploitation capabilities. This paper introduces the Competitive War Strategy Optimizer (CWSO), an enhanced version of the recently developed War Strategy Optimization (WSO) algorithm, inspired by ancient military strategies. CWSO improves the original WSO by incorporating a competition-based learning mechanism, where weaker solutions are refined through learning from stronger counterparts, and a nonlinear weight update mechanism that accelerates convergence and enhances search efficiency. CWSO's performance was rigorously assessed using the CEC-2014 benchmark functions, demonstrating substantial improvements in convergence speed and solution accuracy. It significantly outperformed popular algorithms, including Artificial Ecosystem-based Optimization (AEO), Particle Swarm Optimization (PSO), Comprehensive Learning Particle Swarm Optimizer (CLPSO), Grey Wolf Optimization (GWO), and Harris Hawks Optimization (HHO). In the Friedman ranking, CWSO secured the top spot with an average rank of 2.32, surpassing CLPSO (2.58), AEO (2.83), HHO (4.01), PSO (4.18), and GWO (5.07). To further validate its real-time optimization capabilities, CWSO was applied to the optimal power flow problem in the IEEE-30 bus system, focusing on minimizing fuel costs, reducing emissions, and minimizing power loss. When compared to twenty leading algorithms from the literature, CWSO delivered superior performance in all three optimization objectives.</p>

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Competitive War Strategy Optimizer for global optimization and complex engineering problems

  • 期刊论文

  • 发表日期：

    2025年4月29日

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  • S. L. V. Ayyarao Tummala,
  • G. Indira Kishore

摘要

To solve complex engineering problems efficiently, an optimization algorithm must exhibit robust exploration and exploitation capabilities. This paper introduces the Competitive War Strategy Optimizer (CWSO), an enhanced version of the recently developed War Strategy Optimization (WSO) algorithm, inspired by ancient military strategies. CWSO improves the original WSO by incorporating a competition-based learning mechanism, where weaker solutions are refined through learning from stronger counterparts, and a nonlinear weight update mechanism that accelerates convergence and enhances search efficiency. CWSO's performance was rigorously assessed using the CEC-2014 benchmark functions, demonstrating substantial improvements in convergence speed and solution accuracy. It significantly outperformed popular algorithms, including Artificial Ecosystem-based Optimization (AEO), Particle Swarm Optimization (PSO), Comprehensive Learning Particle Swarm Optimizer (CLPSO), Grey Wolf Optimization (GWO), and Harris Hawks Optimization (HHO). In the Friedman ranking, CWSO secured the top spot with an average rank of 2.32, surpassing CLPSO (2.58), AEO (2.83), HHO (4.01), PSO (4.18), and GWO (5.07). To further validate its real-time optimization capabilities, CWSO was applied to the optimal power flow problem in the IEEE-30 bus system, focusing on minimizing fuel costs, reducing emissions, and minimizing power loss. When compared to twenty leading algorithms from the literature, CWSO delivered superior performance in all three optimization objectives.