Abstract: In response to the issues of EEFO algorithm,such as insufficient global exploration ability,susceptibility to local optima,slow convergence,and performance sensitivity to parameter settings that require careful adjustment and optimization,a multi-strategy improved Electric Eel Foraging Optimization algorithm(IEEFO)is proposed.Firstly,the energy factor strategy is adjusted by introducing a hyperbolic tangent energy factor,which allows the algorithm to incorporate exploratory behavior earlier in the iteration process,enabling rapid discovery of the optimal population and accelerating convergence speed.Secondly,thedistur-bance factor is improved to increase the range of positions where the electric eel can move,which is beneficial for global optimization of the population.Then,a sine cosine strategy is added during the migration phase,which is conducive to local exploration of the algorithm.Finally,after each iteration,a lens imaging reverse learning strategy is incorporated to expand the search space,which helps the algorithm escape from local optima and accelerate convergence to the global optimal solution.The IEEFO is compared with 6 basic algorithms and 4 single-strategy improved Electric Eel Foraging Optimization algorithms,and 13 benchmark functions are used for simulation experiments to evaluate the performance of the IEEFO algorithm.The experimental results show that the IEEFO has faster convergence speed and stronger global optimization ability compared to the aforementioned algorithms,with a significant improvement in overall algorithm performance.Additionally,a mechanical optimization design experiment is conducted to further test and analyze the effectiveness and applicability of the IEEFO.

Key words: Electric eel optimization algorithm, Lens imaging ieverse learning, Energy factor, Perturbation factor, Sine cosine algorithm, Swarm intelligence optimization algorithm