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<p>This study investigates the issues present in the Walrus Optimization Algorithm (WOA) and proposes an improved algorithm called the Adaptive Walrus Optimization Algorithm (AWaOA). The enhancements include incorporating Logistic chaotic population initialization to boost exploration capability, adopting an adaptive Levy flight strategy to improve convergence accuracy, utilizing the crossover strategy from the Adaptive Differential Evolution algorithm to elevate overall population quality, and introducing a Cauchy mutation strategy to mitigate the risk of getting trapped in local optima. To evaluate the efficacy of this algorithm, experiments were conducted on the CEC 2017 and CEC 2020 benchmark functions, achieving rankings of 22, 23, and 22 for the 30, 50, and 100-dimensional cases in CEC 2017, respectively, and securing five best performances in CEC 2020. The results demonstrate that AWaOA outperforms WOA and other algorithms, with Wilcoxon rank-sum test results confirming its superior performance in terms of solution quality, convergence speed, and stability. Additionally, this algorithm has been applied to tackle four constrained engineering optimization design challenges as well as a path planning problem for unmanned aerial vehicles (UAVs). Experimental results consistently show that AWaOA surpasses other benchmarked algorithms, indicating its outstanding overall performance and scalability, and highlighting its significant potential in practical engineering optimization problems.</p>

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Adaptive walrus optimization algorithm for unmanned aerial vehicle path planning and engineering optimization problems

  • 期刊论文

  • 发表日期：

    2025年8月30日

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  • Zonghui Li,
  • Bowei Zheng,
  • Youfa Fu,
  • Xiaoming Liu

摘要

This study investigates the issues present in the Walrus Optimization Algorithm (WOA) and proposes an improved algorithm called the Adaptive Walrus Optimization Algorithm (AWaOA). The enhancements include incorporating Logistic chaotic population initialization to boost exploration capability, adopting an adaptive Levy flight strategy to improve convergence accuracy, utilizing the crossover strategy from the Adaptive Differential Evolution algorithm to elevate overall population quality, and introducing a Cauchy mutation strategy to mitigate the risk of getting trapped in local optima. To evaluate the efficacy of this algorithm, experiments were conducted on the CEC 2017 and CEC 2020 benchmark functions, achieving rankings of 22, 23, and 22 for the 30, 50, and 100-dimensional cases in CEC 2017, respectively, and securing five best performances in CEC 2020. The results demonstrate that AWaOA outperforms WOA and other algorithms, with Wilcoxon rank-sum test results confirming its superior performance in terms of solution quality, convergence speed, and stability. Additionally, this algorithm has been applied to tackle four constrained engineering optimization design challenges as well as a path planning problem for unmanned aerial vehicles (UAVs). Experimental results consistently show that AWaOA surpasses other benchmarked algorithms, indicating its outstanding overall performance and scalability, and highlighting its significant potential in practical engineering optimization problems.