Scholarlink

<p>To address the challenges of long paths and high energy consumption in global path planning within marine obstacle environments, this paper proposes the Sine-Cosine Intelligence Algorithm with Cauchy Opposition-Based Learning (SCIA). First, a Sine and Power Map (SPM) initialization method is introduced to improve population diversity and ensure a more uniform distribution of individuals. Second, a hierarchically selected Cauchy opposition-based learning strategy is designed to prevent premature convergence and enhance the search capability. Third, the stochastic features of the sine-cosine mechanism are fused with Particle Swarm Optimization (PSO), resulting in faster and more stable convergence than the traditional SCA. Simulation results show that SCIA effectively avoids obstacles in a three-dimensional ocean environment while reducing path length by 12.49%, time cost by 21.72%, and energy consumption by 15.41%.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Cauchy opposition-based learning of sine-cosine intelligence algorithm for AUV global path planning

  • 期刊论文

  • 发表日期：

    2025年11月21日

查看全文
  • Zhihua Liu,
  • Jiaqi Liu,
  • Wei Tian,
  • Jingyu Huo,
  • Jiaxing Chen

摘要

To address the challenges of long paths and high energy consumption in global path planning within marine obstacle environments, this paper proposes the Sine-Cosine Intelligence Algorithm with Cauchy Opposition-Based Learning (SCIA). First, a Sine and Power Map (SPM) initialization method is introduced to improve population diversity and ensure a more uniform distribution of individuals. Second, a hierarchically selected Cauchy opposition-based learning strategy is designed to prevent premature convergence and enhance the search capability. Third, the stochastic features of the sine-cosine mechanism are fused with Particle Swarm Optimization (PSO), resulting in faster and more stable convergence than the traditional SCA. Simulation results show that SCIA effectively avoids obstacles in a three-dimensional ocean environment while reducing path length by 12.49%, time cost by 21.72%, and energy consumption by 15.41%.