This paper introduces a new meta-heuristic optimization algorithm called Eel and Grouper Optimizer (EGO), inspired by the symbiotic hunting strategies of eels and groupers in marine ecosystems. The EGO algorithm is designed to effectively explore and exploit the solution space, avoiding local optima and converging towards global optima. The algorithm's performance is evaluated using 19 benchmark functions, demonstrating superior results compared to established meta-heuristics. Additionally, the EGO algorithm is applied to real-world engineering problems, including tension/compression springs, pressure vessels, piston levers, and car side impact simulations, further validating its practicality and efficiency. The paper includes a literature review of meta-heuristic approaches, highlighting the differences between single solution-based and population-based algorithms, and discusses the strengths and weaknesses of each category. The EGO algorithm's unique strengths in optimization and adaptability make it a promising technique for solving complex optimization problems.This paper introduces a new meta-heuristic optimization algorithm called Eel and Grouper Optimizer (EGO), inspired by the symbiotic hunting strategies of eels and groupers in marine ecosystems. The EGO algorithm is designed to effectively explore and exploit the solution space, avoiding local optima and converging towards global optima. The algorithm's performance is evaluated using 19 benchmark functions, demonstrating superior results compared to established meta-heuristics. Additionally, the EGO algorithm is applied to real-world engineering problems, including tension/compression springs, pressure vessels, piston levers, and car side impact simulations, further validating its practicality and efficiency. The paper includes a literature review of meta-heuristic approaches, highlighting the differences between single solution-based and population-based algorithms, and discusses the strengths and weaknesses of each category. The EGO algorithm's unique strengths in optimization and adaptability make it a promising technique for solving complex optimization problems.