Scholarlink

<p>As an emerging swarm intelligence-based optimization technique, the African Vultures Optimization Algorithm (AVOA) has gained significant traction across diverse application domains but suffers from limitations such as insufficient exploration, weak convergence precision, and premature stagnation in complex optimization problems. To alleviate these issues, this study proposes SELEO-AVOA, an enhanced variant that integrates two complementary strategies: Sobol sequence initialization to improve population diversity and global exploration, and an Exponential Local Escaping Operator (ELEO), leveraging an exponential control mechanism to strengthen local exploitation and prevent entrapment in local optima. These enhancements collectively enable a more effective balance between diversification and intensification. The proposed SELEO-AVOA is rigorously evaluated on 23 standard CEC-05 and 12 recent CEC-22 benchmark functions, alongside five constrained engineering design problems, and benchmarked against several state-of-the-art algorithms. Numerical results indicate that SELEO-AVOA successfully attains the global optimum in 96% of CEC-05 functions and 58% of CEC-22 functions. Furthermore, statistical analyses, including the Friedman ranking and Wilcoxon rank-sum tests, demonstrate its superiority, with average Friedman rankings of 1.87 on CEC-05 and 2.83 on CEC-22, confirming enhanced convergence efficiency, robustness, and solution stability. These findings highlight the algorithm’s effectiveness in maintaining a balanced exploration-exploitation trade-off, ensuring high-precision optimization, and demonstrating practical applicability for complex and high-dimensional optimization tasks.</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

An exponential local escaping operator-based african vultures optimization algorithm with low-discrepancy initialization for engineering applications

  • 期刊论文

  • 发表日期：

    2026年7月29日

查看全文
  • Vanisree Chandran,
  • Prabhujit Mohapatra

摘要

As an emerging swarm intelligence-based optimization technique, the African Vultures Optimization Algorithm (AVOA) has gained significant traction across diverse application domains but suffers from limitations such as insufficient exploration, weak convergence precision, and premature stagnation in complex optimization problems. To alleviate these issues, this study proposes SELEO-AVOA, an enhanced variant that integrates two complementary strategies: Sobol sequence initialization to improve population diversity and global exploration, and an Exponential Local Escaping Operator (ELEO), leveraging an exponential control mechanism to strengthen local exploitation and prevent entrapment in local optima. These enhancements collectively enable a more effective balance between diversification and intensification. The proposed SELEO-AVOA is rigorously evaluated on 23 standard CEC-05 and 12 recent CEC-22 benchmark functions, alongside five constrained engineering design problems, and benchmarked against several state-of-the-art algorithms. Numerical results indicate that SELEO-AVOA successfully attains the global optimum in 96% of CEC-05 functions and 58% of CEC-22 functions. Furthermore, statistical analyses, including the Friedman ranking and Wilcoxon rank-sum tests, demonstrate its superiority, with average Friedman rankings of 1.87 on CEC-05 and 2.83 on CEC-22, confirming enhanced convergence efficiency, robustness, and solution stability. These findings highlight the algorithm’s effectiveness in maintaining a balanced exploration-exploitation trade-off, ensuring high-precision optimization, and demonstrating practical applicability for complex and high-dimensional optimization tasks.