MOEPO: A novel Multi-objective Emperor Penguin Optimizer for global optimization: Special application in ranking of cloud service providers

Abstract This study introduces the extension of currently developed Emperor Penguin Optimizer (EPO) in terms of multi-objective problems solving capability, which is entitled as Multi-objective Emperor Penguin Optimizer (MOEPO) In this algorithm, a concept of dynamic archive is introduced, which has the feature to cache the non-dominated Pareto optimal solutions. Here, the roulette-wheel approach is utilized to choose the effective archived solutions by simulating the huddling behaviors of emperor penguins. The proposed algorithm is approved by testing it with twenty-four well-known benchmark test functions, and its performance is compared with existing metaheuristic algorithms. The developed algorithm is analyzed on seven constrained problems of engineering to assess its appropriateness for finding solutions of real world problems. After, that it is validated on cloud computing application and compared between competitor approaches. By using the proposed algorithm, improvements in tackling the resource scheduling issue in cloud computing have been established. The outcomes from the empirical analyzes depict that the proposed algorithm is better than other existing algorithms.