Optimization of electric vehicle design problems using improved electric eel foraging optimization algorithm

Abstract

This paper introduces a novel approach, the Modified Electric Eel Foraging Optimization (EELFO) algorithm, which integrates artificial neural networks (ANNs) with metaheuristic algorithms for solving multidisciplinary design problems efficiently. Inspired by the foraging behavior of electric eels, the algorithm incorporates four key phases: interactions, resting, hunting, and migrating. Mathematical formulations for each phase are provided, enabling the algorithm to explore and exploit solution spaces effectively. The algorithm's performance is evaluated on various real-world optimization problems, including weight optimization of engineering components, economic optimization of pressure handling vessels, and cost optimization of welded beams. Comparative analyses demonstrate the superiority of the MEELFO algorithm in achieving optimal solutions with minimal deviations and computational effort compared to existing metaheuristic methods.