Opposition-based Learning Cooking Algorithm (OLCA) for solving global optimization and engineering problems

Abstract

This study introduces a new human-based meta-heuristic algorithm, the Learning Cooking Algorithm (LCA), based on the opposition-based learning (OBL) strategy, namely the Opposition-based Learning Cooking Algorithm (OLCA). The proposed OLCA algorithm consists of four stages: the first stage, where the OBL strategy is implemented to improve the initial population; the second stage, where children learn from their respective mothers; the third stage, where children and mothers learn from chefs; and the fourth stage, where OBL is applied again to update the population. The proposed OLCA has been examined over 23 test functions, and the OLCA outcomes are equated with several popular and top-performing optimization algorithms. The statistical outcomes, such as the average (Ave), standard deviation (Std), Wilcoxon rank-sum test, and [Formula: see text]-test, reveal that the outcomes of OLCA may effectively address optimization problems by maintaining a proper balance between exploitation and exploration. Furthermore, the proposed OLCA has been employed to solve three real-world engineering problems, such as the tension/compression spring problem, the gear train problem, and the three-bar truss problem. The results demonstrate the OLCA’s superiority and capability over other algorithms in solving engineering problems.