Many-objective cloud manufacturing service selection and scheduling with an evolutionary algorithm based on adaptive environment selection strategy

Cloud manufacturing service selection and scheduling (CMSSS) problem has obtained wide attentions in recent years. However, most existing methods describe this problem as single-, bi-, or tri-objective models. Little work deals with this problem in four or more objectives simultaneously. This paper investigated CMSSS problem in consideration of the interests of users, cloud platform and service providers. An eight-objective CMSSS optimization model is constructed for the problem. Meanwhile, a many-objective evolutionary algorithm with adaptive environment selection (MaOEA-AES) is designed to address the problem. Specifically, diversity-based population partition technology is used to divide the population into multiple subregions to maintain the population diversity, and an adaptive penalty boundary intersection (APBI) distance is designed to select elitist solutions in different stages of evolutionary process. The proposed algorithm is tested on 2 cases with 5 and 8 objectives in CMSSS problems and each of them has sixteen experimental groups with different problem scales. The experiment results show that MaOEA-AES is competitive to resolve the MaO-CMSSS model compared with eight state-of-the-art evolutionary algorithms in convergence and diversity. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper investigates the Cloud Manufacturing Service Selection and Scheduling (CMSSS) problem, constructs an eight-objective optimization model, and designs a many-objective evolutionary algorithm MaOEA-AES to address the issue. By using diversity-based population partition technology and adaptive penalty boundary intersection distance, the algorithm maintains population diversity and selects elitist solutions effectively.