期刊
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
卷 25, 期 4, 页码 769-778出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2021.3066606
关键词
Statistics; Sociology; Optimization; Search problems; Computational modeling; Linear programming; Buildings; Expensive constrained optimization; multiple local surrogates; multiple penalty functions
资金
- National Natural Science Foundation of China [61876163]
The proposed evolutionary algorithm, MPMLS, efficiently tackles expensive constrained optimization problems by optimizing multiple subproblems and constructing multiple local surrogates, leading to better performance compared to other state-of-the-art algorithms.
This article proposes an evolutionary algorithm using multiple penalties and multiple local surrogates (MPMLS) for expensive constrained optimization. In each generation, MPMLS defines and optimizes a number of subproblems. Each subproblem penalizes the constraints in the original problem using a different penalty coefficient and has its own search subregion. A local surrogate is built for optimizing each subproblem. Two major advantages of MPMLS are: 1) it can maintain good population diversity so that the search can approach the optimal solution of the original problem from different directions and 2) it only needs to build local surrogates so that the computational overhead of the model building can be reduced. Numerical experiments demonstrate that our proposed algorithm performs much better than some other state-of-the-art evolutionary algorithms.
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