Eel and grouper optimizer: a nature-inspired optimization algorithm

This paper proposes a meta-heuristic called Eel and Grouper Optimizer (EGO). The EGO algorithm is inspired by the symbiotic interaction and foraging strategy of eels and groupers in marine ecosystems. The algorithm’s efficacy is demonstrated through rigorous evaluation using nineteen benchmark functions, showcasing its superior performance compared to established meta-heuristic algorithms. The findings and results on the benchmark functions demonstrate that the EGO algorithm outperforms well-known meta-heuristics. This work also considers solving a wide range of real-world practical engineering case studies including tension/compression spring, pressure vessel, piston lever, and car side impact, and the CEC 2020 Real-World Benchmark using EGO to illustrate the practicality of the proposed algorithm when dealing with the challenges of real search spaces with unknown global optima. The results show that the proposed EGO algorithm is a reliable soft computing technique for real-world optimization problems and can efficiently outperform the existing algorithms in the literature.