<p>This work has developed the first extended version of novel nature-inspired electric eel foraging optimization naming multi-objective electric eel foraging optimization to deal with multi-objective optimization problems of real-world applications. The ingenious collective foraging strategies of electric eels serve as an inspiration for multi-objective electric eel foraging optimization algorithm. To enable both exploitation and exploration throughout the process, the algorithm mathematically replicates the four essential foraging behaviors of interaction, hunting, migrating, and resting. Further to manage the transition from local to global search and maintain an equilibrium between exploration and exploitation, another factor, known as the energy factor, is evolved. A unique variety of foraging properties are exhibited by electric eels. Therefore, such adaptive behaviors and patterns are mathematically modeled in this work to produce an efficient global optimizer. To maintain solution diversity and guarantee convergence to the Pareto optimal set, the idea of non-dominated sorting with crowding distance is included. This performance of the proposed algorithm is tested using two Zitzler, Deb, and Thiele benchmark problems. The efficacy is confirmed by a comparison with five other already existing algorithms. The simulation results show that the proposed algorithm provides more diversified solutions than others. Additionally, in order to check its effectiveness for real-world scenarios, a case study of smart agriculture is taken where multi-objective electric eel foraging optimization is further compared with three already existing algorithms. The proposed algorithm performs exceptionally well overall in terms of exploitation, exploration, maintaining equilibrium between exploration and exploitation, and avoiding local optima.</p>