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arXiv · 1812.02500

A Parallel Divide-and-Conquer based Evolutionary Algorithm for Large-scale Optimization

Peng Yang·Ke Tang·Xin Yao
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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 efficiently. In this paper, we propose a novel Divide-and-Conquer (DC) based EA that can not only produce high-quality solution by solving sub-problems separately, but also highly utilizes the power of parallel computing by solving the sub-problems simultaneously. Existing DC-based EAs that were deemed 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.

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Peng Yang, Ke Tang, Xin Yao. 2018-12-06. A Parallel Divide-and-Conquer based Evolutionary Algorithm for Large-scale Optimization. https://doi.org/10.1109/access.2019.2938765

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