Improved material generation algorithm by opposition-based learning and laplacian crossover for global optimization and advances in real-world engineering problems

The current study aims to utilize a unique hybrid optimizer called oppositional-based learning and laplacian crossover augmented material generation algorithm (MGA-OBL-LP) to solve engineering design problems. The oppositional-based learning and laplacian crossover approaches are used to address the local optima trap weakness of a recently discovered MGA algorithm that has been added to the fundamental MGA structure. The proposed hybridization strategy aimed to make it easier to improve the exploration-exploitation behavior of the MGA algorithm. The performance of the proposed hybridized algorithm was compared with other notable metaheuristics collected from the literature for four constrained engineering design problems in order to determine whether it would be practical in real-world applications. A comparison analysis is undertaken to confirm the MGA-OBL-LP algorithm's competence in terms of solution quality and stability, and it is discovered to be robust in addressing difficult practical problems.