Enhanced migrating birds optimization algorithm for optimization problems in different domains

Abstract

AbstractMigrating birds optimization algorithm is a promising metaheuristic algorithm recently introduced to the optimization community. In this study, we propose a superior version of the migrating birds optimization algorithm by hybridizing it with the simulated annealing algorithm which is one of the most popular metaheuristics. The new algorithm, called MBOx, is compared with the original migrating birds optimization and four well-known metaheuristics, including the simulated annealing, differential evolution, genetic algorithm and recently proposed harris hawks optimization algorithm. The extensive experiments are conducted on problem instances from both discrete and continuous domains; feature selection problem, obstacle neutralization problem, quadratic assignment problem and continuous functions. On problems from discrete domain, MBOx outperforms the original MBO and others by up to 20.99%. On the continuous functions, it is observed that MBOx does not lead the competition but takes the second position. As a result, MBOx provides a significant performance improvement and therefore, it is a promising solver for computational optimization problems.