[1]TAN Xujie,DENG Changshou,WU Zhijian,et al.Cooperative differential evolution in cloud computing for solving large-scale optimization problems[J].CAAI Transactions on Intelligent Systems,2018,13(2):243-253.[doi:10.11992/tis.201706053]
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Cooperative differential evolution in cloud computing for solving large-scale optimization problems

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 13 Number of periods: 2018 2 Page number: 243-253 Column: 学术论文—智能系统 Public date: 2018-04-15
Title:
Cooperative differential evolution in cloud computing for solving large-scale optimization problems
Author(s):
TAN Xujie1;  DENG Changshou1;  WU Zhijian2;  PENG Hu1;  ZHU Queqiao3
1. School of Information Science and Technology, Jiujiang University, Jiujiang 332005, China;
2. State Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, China;
3. People’s Liberation Army of China 93704
Keywords:
differential evolution; large-scale optimization; coevolution; resilient distributed dataset; cloud computing
CLC:
TP301
DOI:
10.11992/tis.201706053
Abstract:
Differential evolution is an efficient algorithm for solving continuous optimization problems. However, its performance deteriorates quickly and the runtime grows exponentially when differential evolution is applied to solve large-scale optimization problems. To overcome this problem, a novel cooperative coevolution differential evolution based on Spark (called SparkDECC) was proposed. The strategy of separate processing is used in SparkDECC. Firstly, the large-scale problem is decomposed into several low-dimensional sub-problems by using the random grouping strategy; then each sub-problem can be tackled in a parallel way by taking advantage of the parallel computation capability of the resilient distributed datasets model in Spark; finally the optimal solution of the entire problem is obtained by using cooperation mechanism. The experimental results on 13 high-dimensional functions show that the new algorithm has good performances of speedup and scalability. The effectiveness and applicability of the proposed algorithm were verified.
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