期刊
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
卷 23, 期 3, 页码 376-390出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2018.2865931
关键词
Decomposition; evolutionary computation; Gaussian process (GP) regression; multiobjective optimization; reference points generation
资金
- Hong Kong Research Grants Council (RGC) General Research Fund [9042038 (CityU 11205314)]
- ANR/RCC Joint Research Scheme through the Hong Kong RGC
- France National Research Agency [A-CityU101/16]
- Royal Society [IEC/NSFC/170243]
- Chinese National Science Foundation of China [61672443, 61473241]
- UKRI [MR/S017062/1] Funding Source: UKRI
The decomposition-based evolutionary multiobjective optimization (EMO) algorithm has become an increasingly popular choice for a posteriori multiobjective optimization. However, recent studies have shown that their performance strongly depends on the Pareto front (PF) shapes. This can be attributed to the decomposition method, of which the reference points and subproblem formulation settings are not well adaptable to various problem characteristics. In this paper, we develop a learning-to-decompose (LTD) paradigm that adaptively sets the decomposition method by learning the characteristics of the estimated PF. Specifically, it consists of two interdependent parts, i.e., a learning module and an optimization module. Given the current nondominated solutions from the optimization module, the learning module periodically learns an analytical model of the estimated PF. Thereafter, useful information is extracted from the learned model to set the decomposition method for the optimization module: 1) reference points compliant with the PIE shape and 2) subproblem formulations whose contours and search directions are appropriate for the current status. Accordingly, the optimization module, which can be any decomposition-based EMO algorithm in principle, decomposes the multiobjective optimization problem into a number of subproblems and optimizes them simultaneously. To validate our proposed LTD paradigm, we integrate it with two decomposition-based EMO algorithms, and compare them with four state-of-the-art algorithms on a series of benchmark problems with various PF shapes.
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