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- W2971636810 abstract "Large-scale optimization problems that involve thousands of decision variables have extensively arisen from various industrial areas. As a powerful optimization tool for many real-world applications, evolutionary algorithms (EAs) fail to solve the emerging large-scale problems both effectively and computationally efficiently. In this paper, we propose a novel Divide-and-Conquer (DC) based EA that can not only produce high-quality solutions by solving sub-problems separately, but also benefits significantly from the power of parallel computing by solving the sub-problems simultaneously. Existing DC-based EAs that were thought to enjoy the same advantages of the proposed algorithm, are shown to be practically incompatible with the parallel computing scheme, unless some trade-offs are made by compromising the solution quality." @default.
- W2971636810 created "2019-09-12" @default.
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- W2971636810 date "2019-01-01" @default.
- W2971636810 modified "2023-10-17" @default.
- W2971636810 title "A Parallel Divide-and-Conquer-Based Evolutionary Algorithm for Large-Scale Optimization" @default.
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- W2971636810 doi "https://doi.org/10.1109/access.2019.2938765" @default.
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