Promising Area Exploration Based on Hybrid Niching: A Metaheuristic Search Framework for Multimodal Optimization

Jing Ting Huang; Tsung Che Chiang

doi:10.1109/IEEM58616.2023.10406801

Promising Area Exploration Based on Hybrid Niching: A Metaheuristic Search Framework for Multimodal Optimization

Jing Ting Huang, Tsung Che Chiang

Department of Computer Science and Information Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Multimodal optimization aims to find multiple optimal or near-optimal solutions in solving a single-objective optimization problem. In this paper we propose a metaheuristic framework, which utilizes several niching methods including speciation, crowding, and clearing to keep population diversity and search multiple areas in the solution space in parallel. It also uses an archive to store inferior solutions to refresh the population to explore promising areas. The performance of the proposed framework is verified by comparing it with four existing algorithms using the CEC2013 benchmark. The results confirm the positive effects of the proposed ideas and show that our framework provides competitive search ability.

Original language	English
Title of host publication	2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	712-716
Number of pages	5
ISBN (Electronic)	9798350323153
DOIs	https://doi.org/10.1109/IEEM58616.2023.10406801
Publication status	Published - 2023
Event	2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023 - Singapore, Singapore Duration: 2023 Dec 18 → 2023 Dec 21

Publication series

Name	2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023

Conference

Conference	2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023
Country/Territory	Singapore
City	Singapore
Period	2023/12/18 → 2023/12/21

Keywords

differential evolution
multimodal optimization
niching

ASJC Scopus subject areas

Artificial Intelligence
Decision Sciences (miscellaneous)
Statistics, Probability and Uncertainty
Industrial and Manufacturing Engineering
Modelling and Simulation
Strategy and Management

Access to Document

10.1109/IEEM58616.2023.10406801

Cite this

Huang, J. T., & Chiang, T. C. (2023). Promising Area Exploration Based on Hybrid Niching: A Metaheuristic Search Framework for Multimodal Optimization. In 2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023 (pp. 712-716). (2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IEEM58616.2023.10406801

Promising Area Exploration Based on Hybrid Niching: A Metaheuristic Search Framework for Multimodal Optimization. / Huang, Jing Ting; Chiang, Tsung Che.
2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 712-716 (2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Huang, JT & Chiang, TC 2023, Promising Area Exploration Based on Hybrid Niching: A Metaheuristic Search Framework for Multimodal Optimization. in 2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023. 2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023, Institute of Electrical and Electronics Engineers Inc., pp. 712-716, 2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023, Singapore, Singapore, 2023/12/18. https://doi.org/10.1109/IEEM58616.2023.10406801

Huang JT, Chiang TC. Promising Area Exploration Based on Hybrid Niching: A Metaheuristic Search Framework for Multimodal Optimization. In 2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 712-716. (2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023). doi: 10.1109/IEEM58616.2023.10406801

Huang, Jing Ting ; Chiang, Tsung Che. / Promising Area Exploration Based on Hybrid Niching : A Metaheuristic Search Framework for Multimodal Optimization. 2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 712-716 (2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023).

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abstract = "Multimodal optimization aims to find multiple optimal or near-optimal solutions in solving a single-objective optimization problem. In this paper we propose a metaheuristic framework, which utilizes several niching methods including speciation, crowding, and clearing to keep population diversity and search multiple areas in the solution space in parallel. It also uses an archive to store inferior solutions to refresh the population to explore promising areas. The performance of the proposed framework is verified by comparing it with four existing algorithms using the CEC2013 benchmark. The results confirm the positive effects of the proposed ideas and show that our framework provides competitive search ability.",

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Promising Area Exploration Based on Hybrid Niching: A Metaheuristic Search Framework for Multimodal Optimization

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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