Hybrid Electric Fish Optimization Algorithm: Integrating Stagnation Label and Levy Flight Strategies

The Electric Fish Optimization Algorithm (EFO) is renowned for its robustness and global search prowess, yet it faces challenges like premature convergence and local optimum entrapment in complex, high-dimensional spaces. To tackle these issues, this study introduces an enhanced EFO, dubbed SLLF-EFO, which incorporates the Stagnation Label and Levy Flight strategies. By integrating advanced techniques like Good Point Set Theory and the Golden Sine Algorithm, SLLF-EFO boosts EFO's search capabilities. It employs a variable step size Levy flight to counteract passive electric field positioning stagnation problem and utilizes a stagnation label strategy to escape local optimum. Benchmark function-based experiments demonstrate SLLF-EFO's superior performance over EFO, offering faster search speeds and higher optimization accuracy in complex environments. This enhancement not only bolsters EFO's robustness and reliability but also holds significant implications for its future applications.