Solving Expensive Multimodal Optimization Problem by a Decomposition Differential Evolution Algorithm
| Autor: | Weifeng Gao, Maoguo Gong, Zhifang Wei, Gary G. Yen |
|---|---|
| Rok vydání: | 2023 |
| Předmět: |
Mathematical optimization
education.field_of_study Optimization problem Computer science business.industry Population Computer Science Applications Human-Computer Interaction Surrogate model Control and Systems Engineering Differential evolution Radial basis function Local search (optimization) Electrical and Electronic Engineering business Cluster analysis education Global optimization Software Information Systems |
| Zdroj: | IEEE Transactions on Cybernetics. 53:2236-2246 |
| ISSN: | 2168-2275 2168-2267 |
| DOI: | 10.1109/tcyb.2021.3113575 |
| Popis: | An expensive multimodal optimization problem (EMMOP) is that the computation of the objective function is time consuming and it has multiple global optima. This article proposes a decomposition differential evolution (DE) based on the radial basis function (RBF) for EMMOPs, called D/REM. It mainly consists of two phases: the promising subregions detection (PSD) and the local search phase (LSP). In PSD, a population update strategy is designed and the mean-shift clustering is employed to predict the promising subregions of EMMOP. In LSP, a local RBF surrogate model is constructed for each promising subregion and each local RBF surrogate model tracks a global optimum of EMMOP. In this way, an EMMOP is decomposed into many expensive global optimization subproblems. To handle these subproblems, a popular DE variant, JADE, acts as the search engine to deal with these subproblems. A large number of numerical experiments unambiguously validate that D/REM can solve EMMOPs effectively and efficiently. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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