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A random elite ensemble learning swarm optimizer for high-dimensional optimizationopen access

Authors
Yang, QiangSong, Gong-WeiGao, Xu-DongLu, Zhen-YuJeon, Sang-WoonZhang, Jun
Issue Date
Oct-2023
Publisher
SPRINGER HEIDELBERG
Keywords
Particle swarm optimization; Large-scale optimization; Random elite ensemble learning swarm optimizer; Ensemble learning; Cognitive learning; High-dimensional problems
Citation
COMPLEX & INTELLIGENT SYSTEMS, v.9, no.5, pp 5467 - 5500
Pages
34
Indexed
SCIE
SCOPUS
Journal Title
COMPLEX & INTELLIGENT SYSTEMS
Volume
9
Number
5
Start Page
5467
End Page
5500
URI
https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/112559
DOI
10.1007/s40747-023-00993-w
ISSN
2199-4536
2198-6053
Abstract
High-dimensional optimization problems are increasingly pervasive in real-world applications nowadays and become harder and harder to optimize due to increasingly interacting variables. To tackle such problems effectively, this paper designs a random elite ensemble learning swarm optimizer (REELSO) by taking inspiration from human observational learning theory. First, this optimizer partitions particles in the current swarm into two exclusive groups: the elite group consisting of the top best particles and the non-elite group containing the rest based on their fitness values. Next, it employs particles in the elite group to build random elite neighbors for each particle in the non-elite group to form a positive learning environment for the non-elite particle to observe. Subsequently, the non-elite particle is updated by cognitively learning from the best elite among the neighbors and collectively learning from all elites in the environment. For one thing, each non-elite particle is directed by superior ones, and thus the convergence of the swarm could be guaranteed. For another, the elite learning environment is randomly formed for each non-elite particle, and hence high swarm diversity could be maintained. Finally, this paper further devises a dynamic partition strategy to divide the swarm into the two groups dynamically during the evolution, so that the swarm gradually changes from exploring the immense solution space to exploiting the found optimal areas without serious diversity loss. With the above mechanisms, the devised REELSO is expected to explore the search space and exploit the found optimal areas properly. Abundant experiments on two popularly used high-dimensional benchmark sets prove that the devised optimizer performs competitively with or even significantly outperforms several state-of-the-art approaches designed for high-dimensional optimization.
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ERICA 공학대학 (SCHOOL OF ELECTRICAL ENGINEERING)
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