Electric eel foraging optimization: A new bio-inspired optimizer for engineering applications

An original swarm-based, bio-inspired metaheuristic algorithm, named electric eel foraging optimization (EEFO) is developed and tested in this work. EEFO draws inspiration from the intelligent group foraging behaviors exhibited by electric eels in nature. The algorithm mathematically models four key foraging behaviors: interaction, resting, hunting, and migration, to provide both exploration and exploitation during the optimization process. In addition, an energy factor is developed to manage the transition from global search to local search and the balance between exploration and exploitation in the search space. EEFO reveals various foraging patterns based on the foraging characteristics of electric eels. In this study, such dynamic patterns and behaviors are mathematically imitated to design an effective global optimizer. The effectiveness of EEFO is verified through a comparison with 12 other algorithms using the 23 test functions, Congress on Evolutionary Computation 2011 (CEC2011) test suite, and Congress on Evolutionary Computation 2017 (CEC2017) test suite. The experimental results reveal that the EEFO algorithm outperforms the other algorithms for 87% of the 23 test functions and 59% of the CEC2011 test suite, as well as for 66%, 52% and 45% of the CEC2017 test suite with 10, 30, and 50 dimensions, respectively. The applicability of EEFO is comprehensively tested with 10 engineering problems and the application of hydropower station sluice opening control under accident tripping conditions. The results demonstrate the superiority and promising prospects of EEFO when solving a wide range of challenging real-world problems. Overall, the proposed algorithm showcases exceptional performance in terms of exploitation, exploration, the ability to balance exploitation and exploration, and avoiding local optima. EEFO exhibits remarkable competitiveness, particularly in optimization problems that involve unimodal characteristics and numerous constraints and variables. The source code of EEFO is publicly available at https://ww2.mathworks.cn/matlabcentral/fileexchange/153461-electric-eel-foraging-optimization-eefo.