Improvement of Electric Fish Optimization Algorithm for Standstill Label Combined with Levy Flight Strategy

Area of Science:

• Computational Intelligence
• Meta-heuristic Optimization
• Nature-Inspired Algorithms

Background:

• The Electric Fish Optimization (EFO) algorithm, inspired by electric fish, offers robustness and global search capabilities.
• However, EFO faces challenges in complex environments, including premature convergence and local optima stagnation.
• Passive electric field localization issues also hinder its effectiveness.

Purpose of the Study:

• To introduce an enhanced meta-heuristic algorithm, the Adaptive Electric Fish Optimization Algorithm Based on Standstill Label and Level Flight (SLLF-EFO).
• To address the limitations of the standard EFO algorithm in complex optimization scenarios.
• To improve the algorithm's adaptability, search speed, and optimization accuracy.

Main Methods:

• Hybridization of EFO with the Golden Sine Algorithm and good point set theory.
• Implementation of a variable-step-size Levy flight strategy to resolve electric field localization stagnation.
• Integration of a standstill label strategy to prevent convergence to local optima.

Main Results:

• The proposed SLLF-EFO algorithm demonstrated superior performance on benchmark functions in complex settings.
• Significant improvements in search speed and optimization accuracy were observed compared to the standard EFO.
• The enhanced algorithm showed increased robustness and reliability.

Conclusions:

• The SLLF-EFO framework effectively overcomes the limitations of the traditional EFO algorithm.
• This hybrid approach offers enhanced adaptability for complex optimization problems.
• The study provides valuable insights for future applications of advanced meta-heuristic algorithms.