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Enhancing Learning Efficiency of Brain Storm Optimization via Orthogonal Learning Design.
- Source :
- IEEE Transactions on Systems, Man & Cybernetics. Systems; Nov2021, Vol. 51 Issue 11, Part 1, p6723-6742, 20p
- Publication Year :
- 2021
-
Abstract
- In brain storm optimization (BSO), the convergent operation utilizes a clustering strategy to group the population into multiple clusters, and the divergent operation uses this cluster information to generate new individuals. However, this mechanism is inefficient to regulate the exploration and exploitation search. This article first analyzes the main factors that influence the performance of BSO and then proposes an orthogonal learning framework to improve its learning mechanism. In this framework, two orthogonal design (OD) engines (i.e., exploration OD engine and exploitation OD engine) are introduced to discover and utilize useful search experiences for performance improvements. In addition, a pool of auxiliary transmission vectors with different features is maintained and their biases are also balanced by the OD decision mechanism. Finally, the proposed algorithm is verified on a set of benchmarks and is adopted to resolve the quantitative association rule mining problem considering the support, confidence, comprehensibility, and netconf. The experimental results show that the proposed approach is very powerful in optimizing complex functions. It not only outperforms previous versions of the BSO algorithm but also outperforms several famous OD-based algorithms. [ABSTRACT FROM AUTHOR]
- Subjects :
- BRAINSTORMING
ASSOCIATION rule mining
ALGORITHMS
Subjects
Details
- Language :
- English
- ISSN :
- 21682216
- Volume :
- 51
- Issue :
- 11, Part 1
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Systems, Man & Cybernetics. Systems
- Publication Type :
- Academic Journal
- Accession number :
- 153812079
- Full Text :
- https://doi.org/10.1109/TSMC.2020.2963943