Journal
APPLIED SOFT COMPUTING
Volume 83, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.asoc.2019.105659
Keywords
Particle swarm optimization; Sequential approximation optimization; Space partition; Expensive problems
Categories
Funding
- National Natural Science Foundation for Distinguished Young Scholars of China [51825502]
- National Natural Science Foundation of China [51805180, 51721092]
- Program for HUST Academic Frontier Youth Team, China [2017QYTD04]
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In this paper, an efficient sequential approximation optimization assisted particle swarm optimization algorithm is proposed for optimization of expensive problems. This algorithm makes a good balance between the search ability of particle swarm optimization and sequential approximation optimization. Specifically, the proposed algorithm uses the optima obtained by sequential approximation optimization in local regions to replace the personal historical best particles and then runs the basic particle swarm optimization procedures. Compared with particle swarm optimization, the proposed algorithm is more efficient because the optima provided by sequential approximation optimization can direct swarm particles to search in a more accurate way. In addition, a space partition strategy is proposed to constraint sequential approximation optimization in local regions. This strategy can enhance the swarm diversity and prevent the preconvergence of the proposed algorithm. In order to validate the proposed algorithm, a lot of numerical benchmark problems are tested. An overall comparison between the proposed algorithm and several other optimization algorithms has been made. Finally, the proposed algorithm is applied to an optimal design of bearings in an all-direction propeller. The results show that the proposed algorithm is efficient and promising for optimization of the expensive problems. (C) 2019 Elsevier B.V. All rights reserved.
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