Luo Wangzhou, Wu Hailong, Peng Jiegang
School of Automation Engineering, University of Electronic and Science Technology of China, Chengdu 611731, China.
Biomimetics (Basel). 2024 Nov 6;9(11):677. doi: 10.3390/biomimetics9110677.
The Electric Fish Optimization (EFO) algorithm is inspired by the predation behavior and communication of weak electric fish. It is a novel meta-heuristic algorithm that attracts researchers because it has few tunable parameters, high robustness, and strong global search capabilities. Nevertheless, when operating in complex environments, the EFO algorithm encounters several challenges including premature convergence, susceptibility to local optima, and issues related to passive electric field localization stagnation. To address these challenges, this study introduces Adaptive Electric Fish Optimization Algorithm Based on Standstill Label and Level Flight (SLLF-EFO). This hybrid approach incorporates the Golden Sine Algorithm and good point set theory to augment the EFO algorithm's capabilities, employs a variable-step-size Levy flight strategy to efficiently address passive electric field localization stagnation problems, and utilizes a standstill label strategy to mitigate the algorithm's tendency to fall into local optima during the iterative process. By leveraging multiple solutions to optimize the EFO algorithm, this framework enhances its adaptability in complex environments. Experimental results from benchmark functions reveal that the proposed SLLF-EFO algorithm exhibits improved performance in complex settings, demonstrating enhanced search speed and optimization accuracy. This comprehensive optimization not only enhances the robustness and reliability of the EFO algorithm but also provides valuable insights for its future applications.
电鱼优化（EFO）算法的灵感来源于弱电鱼的捕食行为和通信方式。它是一种新颖的元启发式算法，因其可调参数少、鲁棒性高和全局搜索能力强而吸引了研究人员。然而，在复杂环境中运行时，EFO算法面临着一些挑战，包括早熟收敛、易陷入局部最优以及与被动电场定位停滞相关的问题。为了应对这些挑战，本研究引入了基于静止标签和平直飞行的自适应电鱼优化算法（SLLF-EFO）。这种混合方法结合了黄金正弦算法和佳点集理论以增强EFO算法的能力，采用变步长Levy飞行策略有效解决被动电场定位停滞问题，并利用静止标签策略减轻算法在迭代过程中陷入局部最优的倾向。通过利用多种解决方案对EFO算法进行优化，该框架提高了其在复杂环境中的适应性。基准函数的实验结果表明，所提出的SLLF-EFO算法在复杂环境中表现出更好的性能，展示出更高的搜索速度和优化精度。这种全面的优化不仅增强了EFO算法的鲁棒性和可靠性，还为其未来应用提供了有价值的见解。
