Ọdịgbo Metaheuristic Optimization Algorithm for Computation of Real-Parameters and Engineering Design Optimization

This paper proposes a new population-based global optimization algorithm, Ọdịgbo Metaheuristic Optimization Algorithm–ỌMOA, for solving complex bounded-constraint/single objective real-parameter problems found in most engineering and scientific applications. It’s inspired by the human socio-cultural informal discipleship learning pattern inherent behavior of Ndịgbo peoples; subject – primary (Nwa-ahịa), mercantile cycle grows to secondary (Mazi) owing intuitive stratagem (dialect - Ịgba) embedded an aged-long cultural model “Ịgba-ọsọ-ahịa” (meaning, strategic marketing skills, practice). The mimics search routine satisfying customer’s need market, built into exploration exploitation applied mathematical model. About 30 classical unconstrained functions are tested, comparing results with that five similar state-of-the-art algorithms. Also, 29 CEC-2017 single real constraint benchmark serious dimensional were simulated compared against winners competition. Validation includes statistical (t-test, p-value) comparison 50 Dimension as ỌMOA demonstrated superior performance. TCS (9.18%), WBP (6.3%), PVDP (601%), RGP (319%), RBP (760%), GTCD (202%), HIMMELBLAU (4%), CDP (88.12%) improvements made on 8 CEC-2020 design former best performances; OMOA is simple implement, replicate applicable across domains. some new, improved optimum was obtained Shubert Schaffer 4 function optimums.