Version 1
: Received: 14 September 2024 / Approved: 16 September 2024 / Online: 16 September 2024 (10:53:31 CEST)
How to cite:
Luo, W.; Wu, H.; Peng, J. Improvement of Electric Fish Optimization Algorithm for Standstill Label Combined with Levy Flight Strategy. Preprints2024, 2024091219. https://doi.org/10.20944/preprints202409.1219.v1
Luo, W.; Wu, H.; Peng, J. Improvement of Electric Fish Optimization Algorithm for Standstill Label Combined with Levy Flight Strategy. Preprints 2024, 2024091219. https://doi.org/10.20944/preprints202409.1219.v1
Luo, W.; Wu, H.; Peng, J. Improvement of Electric Fish Optimization Algorithm for Standstill Label Combined with Levy Flight Strategy. Preprints2024, 2024091219. https://doi.org/10.20944/preprints202409.1219.v1
APA Style
Luo, W., Wu, H., & Peng, J. (2024). Improvement of Electric Fish Optimization Algorithm for Standstill Label Combined with Levy Flight Strategy. Preprints. https://doi.org/10.20944/preprints202409.1219.v1
Chicago/Turabian Style
Luo, W., Hailong Wu and Jiegang Peng. 2024 "Improvement of Electric Fish Optimization Algorithm for Standstill Label Combined with Levy Flight Strategy" Preprints. https://doi.org/10.20944/preprints202409.1219.v1
Abstract
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 optimum, and issues related to passive electric field localization stagnation. To address these challenges, this study introduces an 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 optimum 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.
Keywords
Electric Fish Optimization Algorithm; Meta-heuristic Algorithm; Levy Flight; Standstill Label; Local Optimum
Subject
Computer Science and Mathematics, Data Structures, Algorithms and Complexity
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
