MOALG: A Metaheuristic Hybrid of Multi-Objective Ant Lion Optimizer and Genetic Algorithm for Solving Design Problems

This study proposes a hybridization of two efficient algorithm’s Multi-objective Ant Lion Optimizer Algorithm (MOALO) which is a multi-objective enhanced version of the Ant Lion Optimizer Algorithm (ALO) and the Genetic Algorithm (GA). MOALO version has been employed to address those problems containing many objectives and an archive has been employed for retaining the non-dominated solutions. The uniqueness of the hybrid is that the operators like mutation and crossover of GA are employed in the archive to update the solutions and later those solutions go through the process of MOALO. A first-time hybrid of these algorithms is employed to solve multi-objective problems. The hybrid algorithm overcomes the limitation of ALO of getting caught in the local optimum and the requirement of more computational effort to converge GA. To evaluate the hybridized algorithm’s performance, a set of constrained, unconstrained test problems and engineering design problems were employed and compared with five well-known computational algorithms-MOALO, Multi-objective Crystal Structure Algorithm (MOCryStAl), Multi-objective Particle Swarm Optimization (MOPSO), Multi-objective Multiverse Optimization Algorithm (MOMVO), Multi-objective Salp Swarm Algorithm (MSSA). The outcomes of five performance metrics are statistically analyzed and the most efficient Pareto fronts comparison has been obtained. The proposed hybrid surpasses MOALO based on the results of hypervolume (HV), Spread, and Spacing. So primary objective of developing this hybrid approach has been achieved successfully. The proposed approach demonstrates superior performance on the test functions, showcasing robust convergence and comprehensive coverage that surpasses other existing algorithms.