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. © 2013 IEEE.
| Original language | English |
|---|---|
| Article number | 8822743 |
| Pages (from-to) | 163105-163118 |
| Number of pages | 14 |
| Journal | IEEE Access |
| Volume | 7 |
| DOIs | |
| Publication status | Published - 2019 |
| Externally published | Yes |
Funding
This work was supported in part by the Natural Science Foundation of China under Grant 61806090 and Grant 61672478, in part by the Program for Guangdong Introducing Innovative and Enterpreneurial Teams under Grant 2017ZT07X386, in part by the Shenzhen Peacock Plan under Grant KQTD2016112514355531, and in part by the Program for University Key Laboratory of Guangdong Province under Grant 2017KSYS008.
Keywords
- divide-and-conquer
- large-scale optimization
- Parallel evolutionary algorithms