Dynamic Niches-Based Hybrid Breeding Optimization Algorithm for Solving Multi-Modal Optimization Problem

Ting Cai, Ziteng Qiao, Zhiwei Ye (), Hu Pan, Mingwei Wang, Wen Zhou, Qiyi He, Peng Zhang and Wanfang Bai
Additional contact information
Ting Cai: School of Computer Science, Hubei University of Technology, Wuhan 430068, China
Ziteng Qiao: School of Computer Science, Hubei University of Technology, Wuhan 430068, China
Zhiwei Ye: School of Computer Science, Hubei University of Technology, Wuhan 430068, China
Hu Pan: School of Computer Science, Hubei University of Technology, Wuhan 430068, China
Mingwei Wang: School of Computer Science, Hubei University of Technology, Wuhan 430068, China
Wen Zhou: School of Computer Science, Hubei University of Technology, Wuhan 430068, China
Qiyi He: School of Computer Science, Hubei University of Technology, Wuhan 430068, China
Peng Zhang: Wuhan Fiberhome Technical Services Co., Ltd., Wuhan 430205, China
Wanfang Bai: Xining Big Data Service Administration, Xining 810000, China

Mathematics, 2024, vol. 12, issue 17, 1-24

Abstract: Some problems exist in classical optimization algorithms to solve multi-modal optimization problems and other complex systems. A Dynamic Niches-based Improved Hybrid Breeding Optimization (DNIHBO) algorithm is proposed to address the multi-modal optimization problem in the paper. By dynamically adjusting the niche scale, it effectively addresses the issue of niche parameter sensitivity. The structure of the algorithm includes three distinct groups: maintainer, restorer, and sterile lines for updating operations. However, the maintainer individuals often stagnate, leading to the risk of the local optima. To overcome this, neighborhood search and elite mutation strategies are incorporated, enhancing the balance between exploration and exploitation. To further improve individual utilization within niches, a niche restart strategy is introduced, ensuring sustained population diversity. The efficacy of DNIHBO is validated through simulations on 16 multi-modal test functions, followed by comparative analyses with various multi-modal optimization algorithms. The results convincingly demonstrate that DNIHBO not only effectively locates multiple global optima but also consistently outperforms other algorithms on test functions. These findings underscore the superiority of DNIHBO as a high-performing solution for multi-modal optimization.

Keywords: multi-modal optimization; hybrid breeding optimization algorithm; dynamic niche; neighborhood search; elite mutation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/17/2779/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/17/2779/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:17:p:2779-:d:1473933

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().