[1]GU Qinghua,TANG Hui,LI Xuexian,et al.A multimodal multi-objective optimization algorithm with clustering and niching searching[J].CAAI Transactions on Intelligent Systems,2023,18(5):1127-1141.[doi:10.11992/tis.202204040]
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A multimodal multi-objective optimization algorithm with clustering and niching searching

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 5 Page number: 1127-1141 Column: 学术论文—机器学习 Public date: 2023-09-05
Title:
A multimodal multi-objective optimization algorithm with clustering and niching searching
Author(s):
GU Qinghua1; 2; 3;  TANG Hui1; 2;  LI Xuexian2; 4;  JIANG Song1; 2; 3
1. School of Resources Engineering, Xi’an University of Architecture&Technology, Xi’an 710055, China;
2. Xi’an Key Laboratory for Intelligent Industrial Perception, Calculation and Decision, Xi’an 710055, China;
3. Xi’an Youmai Intelligent Mining Research Institute, Xi’an 710055, China;
4. School of Management, Xi’an University of Architecture&Technology, Xi’an 710055, China
Keywords:
multimodal multi-objective optimization; pigeon-inspired optimization algorithm; clustering strategy; niching searching; non-dominant sorting; elite learning strategy; diversity; map-based testing application
CLC:
TP301
DOI:
10.11992/tis.202204040
Abstract:
In order to address the problems associated with maintaining population diversity and the insufficient number of equivalent Pareto-optimal solutions for multimodal multi-objective optimization, this study proposes a multimodal multi-objective optimization algorithm combining clustering and niche search (CSSMPIO). In the proposed algorithm, a clustering-based special crowding distance method is designed to initialize the population. Additionally, self-organized speciation is introduced to form stable niches, facilitating parallel searching and maintaining equivalent Pareto-optimal solutions. Subsequently, a non-dominated special crowding distance is introduced to realize individual selection and an elite learning strategy, circumventing premature convergence. The algorithm has been simulated using seven other state-of-the-art algorithms on 14 multimodal multi-objective optimization problems and was tested and analyzed using the Wilcoxon rank sum test. The results reveal that the general performance of CSSMPIO is superior to that of the compared algorithms. Finally, the CSSMPIO algorithm is applied to the map-based test problem, which confirms the effectiveness of the algorithm.
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