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<p>The Spider Wasp Optimization (SWO) algorithm often suffers from premature convergence, slow early-stage progress, and the need for manual tuning of the trade-off rate (TR) when applied to complex problems. To address these limitations, we propose Spider-Wasp Optimizer Combining Sine-Tent-Cosine Mapping with Dynamic Opposition (CSWO), a hybrid multi-strategy optimizer that enhances SWO with several complementary mechanisms. CSWO employs an STC (Sine-Tent-Cosine) chaotic map together with dynamic opposition-based initialization to increase initial population diversity and accelerate early convergence; a <i>t</i>-distribution-based adaptive step-size and mutation scheme to realize stage-aware search scaling and improve escape from local optima; a population-coupled dynamic TR to automatically balance exploration and exploitation during the run; and dual-axis crossover updates plus elite external crossover perturbation to strengthen information exchange and maintain elite diversity. We validate CSWO on 23 classical benchmark functions and the CEC2017 suite, and demonstrate its engineering applicability on seven real-world problems from CEC2011. Comparative experiments against several state-of-the-art algorithms show that CSWO achieves superior solution quality, faster convergence, and improved robustness across diverse problem classes.</p>

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Hybrid multi-strategy optimization framework for engineering design: dynamic opposition-based STC chaos, adaptive step-size mutation, and crossover-renewal

  • 期刊论文

  • 发表日期：

    2026年3月5日

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  • Yongjie Xiong,
  • Guanwu Jiang,
  • Xiaoyi Zhu

摘要

The Spider Wasp Optimization (SWO) algorithm often suffers from premature convergence, slow early-stage progress, and the need for manual tuning of the trade-off rate (TR) when applied to complex problems. To address these limitations, we propose Spider-Wasp Optimizer Combining Sine-Tent-Cosine Mapping with Dynamic Opposition (CSWO), a hybrid multi-strategy optimizer that enhances SWO with several complementary mechanisms. CSWO employs an STC (Sine-Tent-Cosine) chaotic map together with dynamic opposition-based initialization to increase initial population diversity and accelerate early convergence; a t-distribution-based adaptive step-size and mutation scheme to realize stage-aware search scaling and improve escape from local optima; a population-coupled dynamic TR to automatically balance exploration and exploitation during the run; and dual-axis crossover updates plus elite external crossover perturbation to strengthen information exchange and maintain elite diversity. We validate CSWO on 23 classical benchmark functions and the CEC2017 suite, and demonstrate its engineering applicability on seven real-world problems from CEC2011. Comparative experiments against several state-of-the-art algorithms show that CSWO achieves superior solution quality, faster convergence, and improved robustness across diverse problem classes.