A new multi-objective particle swarm optimisation algorithm based on R2 indicator selection mechanism

ABSTRACT

In recent years, Pareto-based selection mechanism has been successfully applied in dealing with complex multi-objective optimisation problems (MOPs), while indicators-based have been explored to apply in solving this problems. Therefore, a new multi-objective particle swarm optimisation algorithm based on R2 indicator selection mechanism (R2SMMOPSO) is presented in this paper. In the proposed algorithm, R2 indicator is designed as a selection mechanism for ensuring convergence and distribution of the algorithm simultaneously. In addition, an improved cosine-adjusted inertia weight balances the ability of algorithm exploitation and exploration effectively. Besides, Gaussian mutation strategy is designed to prevent particles from falling into the local optimum when the particle does not satisfy the condition of the position update formula, polynomial mutation is applied in the external archive to increase the diversity of elite solutions. The performance of the proposed algorithm is validated and compared with some state-of-the-art algorithms on a number of test problems. Experimental studies demonstrate that the proposed algorithm shows very competitive performance when dealing with complex MOPs.

KEYWORDS:

• Multi-objective
• optimisation
• particle swarm
• indicators-based
• R2 indicator
• selection mechanisms

Acknowledgments

The authors would like to appreciate the editor and reviewers for their helpful comments and suggestions to improve the quality of this paper.

Disclosure statement

The authors declare that there is no conflict of interests regarding the publication of this paper.

Additional information

Funding

This work was supported by the Natural Science Foundation of Hebei Province [grant number F2016203249].

Notes on contributors

Lixin Wei

Lixin Wei received the B.E. degree in manufacturing and automation, and the M.E. and Ph.D. degrees in control theory and control engineering from Yanshan University in 2000, 2002 and 2005, respectively. He is currently a Professor of control theory and control engineering with Yanshan University. His current research interests include system optimisation theory and application, metallurgical integrated automation and intelligent control theory and application.

Xin Li

Xin Li is currently pursuing the M.Eng. degree in control theory and control engineering with Yanshan University, China. Her current research interests include system optimisation theory and applications.

Rui Fan

Rui Fan is currently pursuing the Ph.D. degree in control theory and control engineering with Yanshan University, China. Her current research interests include system optimisation theory and applications.