Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 51, Issue 3, Pages 1390-1402Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2020.2967553
Keywords
Optimization; Iron; Particle swarm optimization; Sociology; Statistics; Education; Search problems; Computationally expensive problems; multiswarm optimization; particle swarm optimization (PSO); surrogate model; teaching-learning-based optimization (TLBO)
Categories
Funding
- National Natural Science Foundation for Distinguished Young Scholars of China [51825502]
- 111 Project [B16019]
- Program for HUST Academic Frontier Youth Team [2017QYTD04]
Ask authors/readers for more resources
This article presents a surrogate-assisted multiswarm optimization algorithm for high-dimensional computationally expensive problems. The proposed algorithm consists of two swarms that enhance exploration and convergence using teaching-learning-based optimization and particle swarm optimization techniques. Additional strategies such as dynamic swarm size adjustment, coordinate systems, and prescreening criterion contribute to the algorithm's superior performance in comparison to three state-of-the-art algorithms.
This article presents a surrogate-assisted multiswarm optimization (SAMSO) algorithm for high-dimensional computationally expensive problems. The proposed algorithm includes two swarms: the first one uses the learner phase of teaching-learning-based optimization (TLBO) to enhance exploration and the second one uses the particle swarm optimization (PSO) for faster convergence. These two swarms can learn from each other. A dynamic swarm size adjustment scheme is proposed to control the evolutionary progress. Two coordinate systems are used to generate promising positions for the PSO in order to further enhance its search efficiency on different function landscapes. Moreover, a novel prescreening criterion is proposed to select promising individuals for exact function evaluations. Several commonly used benchmark functions with their dimensions varying from 30 to 200 are adopted to evaluate the proposed algorithm. The experimental results demonstrate the superiority of the proposed algorithm over three state-of-the-art algorithms.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available