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Complex System Modeling and Simulation 2021, 1(4): 291-307 https://doi.org/10.23919/CSMS.2021.0023
Open Access | Issue | Published: 31 December 2021

Novel PIO Algorithm with Multiple Selection Strategies for Many-Objective Optimization Problems

Show Author's Information Zhihua Cui1,  Lihong Zhao1,  Youqian Zeng1,  Yeqing Ren2,  Wensheng Zhang3( ),  Xiao-Zhi Gao4
School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China
the School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China
State Key Laboratory of Intelligent Control and Management of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
the School of Computing, University of Eastern Finland, Kuopio, FI-70211, Finland
Keywords:
pigeon-inspired optimization algorithm, many-objective optimization problem, multiple selection strategy, elite individual retention
Cite this article:
Cui Z, Zhao L, Zeng Y, et al. Novel PIO Algorithm with Multiple Selection Strategies for Many-Objective Optimization Problems. Complex System Modeling and Simulation, 2021, 1(4): 291-307. https://doi.org/10.23919/CSMS.2021.0023
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Abstract Full text About this article

With the increase of problem dimensions, most solutions of existing many-objective optimization algorithms are non-dominant. Therefore, the selection of individuals and the retention of elite individuals are important. Existing algorithms cannot provide sufficient solution precision and guarantee the diversity and convergence of solution sets when solving practical many-objective industrial problems. Thus, this work proposes an improved many-objective pigeon-inspired optimization (ImMAPIO) algorithm with multiple selection strategies to solve many-objective optimization problems. Multiple selection strategies integrating hypervolume, knee point, and vector angles are utilized to increase selection pressure to the true Pareto Front. Thus, the accuracy, convergence, and diversity of solutions are improved. ImMAPIO is applied to the DTLZ and WFG test functions with four to fifteen objectives and compared against NSGA-III, GrEA, MOEA/D, RVEA, and many-objective Pigeon-inspired optimization algorithm. Experimental results indicate the superiority of ImMAPIO on these test functions.


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About this article

Novel PIO Algorithm with Multiple Selection Strategies for Many-Objective Optimization Problems

Show Author's information Zhihua Cui1, Lihong Zhao1, Youqian Zeng1, Yeqing Ren2, Wensheng Zhang3( ), Xiao-Zhi Gao4
School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China
the School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China
State Key Laboratory of Intelligent Control and Management of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
the School of Computing, University of Eastern Finland, Kuopio, FI-70211, Finland

Abstract

With the increase of problem dimensions, most solutions of existing many-objective optimization algorithms are non-dominant. Therefore, the selection of individuals and the retention of elite individuals are important. Existing algorithms cannot provide sufficient solution precision and guarantee the diversity and convergence of solution sets when solving practical many-objective industrial problems. Thus, this work proposes an improved many-objective pigeon-inspired optimization (ImMAPIO) algorithm with multiple selection strategies to solve many-objective optimization problems. Multiple selection strategies integrating hypervolume, knee point, and vector angles are utilized to increase selection pressure to the true Pareto Front. Thus, the accuracy, convergence, and diversity of solutions are improved. ImMAPIO is applied to the DTLZ and WFG test functions with four to fifteen objectives and compared against NSGA-III, GrEA, MOEA/D, RVEA, and many-objective Pigeon-inspired optimization algorithm. Experimental results indicate the superiority of ImMAPIO on these test functions.

Keywords: pigeon-inspired optimization algorithm, many-objective optimization problem, multiple selection strategy, elite individual retention

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Publication history

Received: 06 August 2021
Revised: 04 September 2021
Accepted: 13 September 2021
Published: 31 December 2021
Issue date: December 2021

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© The author(s) 2021

Acknowledgements

Acknowledgment

This work was supported by the National Key Research and Development Program of China (No. 2018YFC1604000), the National Natural Science Foundation of China (Nos. 61806138, 61772478, U1636220, 61961160707, and 61976212), the Key R&D Program of Shanxi Province (High Technology) (No. 201903D121119), the Key R&D Program of Shanxi Province (International Cooperation) (No. 201903D421048), and the Key R&D Program (International Science and Technology Cooperation Project) of Shanxi Province, China (No. 201903D421003).

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