A Modified Cloud Particles Differential Evolution Algorithm for Real-Parameter Optimization
The issue of exploration-exploitation remains one of the most challenging tasks within the framework of evolutionary algorithms. To effectively balance the exploration and exploitation in the search space, this paper proposes a modified cloud particles differential evolution algorithm (MCPDE) for re...
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doaj-e94ed938ed32407990bfd4e7a01b836f2020-11-25T01:21:23ZengMDPI AGAlgorithms1999-48932016-11-01947810.3390/a9040078a9040078A Modified Cloud Particles Differential Evolution Algorithm for Real-Parameter OptimizationWei Li0School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, ChinaThe issue of exploration-exploitation remains one of the most challenging tasks within the framework of evolutionary algorithms. To effectively balance the exploration and exploitation in the search space, this paper proposes a modified cloud particles differential evolution algorithm (MCPDE) for real-parameter optimization. In contrast to the original Cloud Particles Differential Evolution (CPDE) algorithm, firstly, control parameters adaptation strategies are designed according to the quality of the control parameters. Secondly, the inertia factor is introduced to effectively keep a better balance between exploration and exploitation. Accordingly, this is helpful for maintaining the diversity of the population and discouraging premature convergence. In addition, the opposition mechanism and the orthogonal crossover are used to increase the search ability during the evolutionary process. Finally, CEC2013 contest benchmark functions are selected to verify the feasibility and effectiveness of the proposed algorithm. The experimental results show that the proposed MCPDE is an effective method for global optimization problems.http://www.mdpi.com/1999-4893/9/4/78cloud particles differential evolutionexploration-exploitationinertia factorglobal optimization |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Wei Li |
spellingShingle |
Wei Li A Modified Cloud Particles Differential Evolution Algorithm for Real-Parameter Optimization Algorithms cloud particles differential evolution exploration-exploitation inertia factor global optimization |
author_facet |
Wei Li |
author_sort |
Wei Li |
title |
A Modified Cloud Particles Differential Evolution Algorithm for Real-Parameter Optimization |
title_short |
A Modified Cloud Particles Differential Evolution Algorithm for Real-Parameter Optimization |
title_full |
A Modified Cloud Particles Differential Evolution Algorithm for Real-Parameter Optimization |
title_fullStr |
A Modified Cloud Particles Differential Evolution Algorithm for Real-Parameter Optimization |
title_full_unstemmed |
A Modified Cloud Particles Differential Evolution Algorithm for Real-Parameter Optimization |
title_sort |
modified cloud particles differential evolution algorithm for real-parameter optimization |
publisher |
MDPI AG |
series |
Algorithms |
issn |
1999-4893 |
publishDate |
2016-11-01 |
description |
The issue of exploration-exploitation remains one of the most challenging tasks within the framework of evolutionary algorithms. To effectively balance the exploration and exploitation in the search space, this paper proposes a modified cloud particles differential evolution algorithm (MCPDE) for real-parameter optimization. In contrast to the original Cloud Particles Differential Evolution (CPDE) algorithm, firstly, control parameters adaptation strategies are designed according to the quality of the control parameters. Secondly, the inertia factor is introduced to effectively keep a better balance between exploration and exploitation. Accordingly, this is helpful for maintaining the diversity of the population and discouraging premature convergence. In addition, the opposition mechanism and the orthogonal crossover are used to increase the search ability during the evolutionary process. Finally, CEC2013 contest benchmark functions are selected to verify the feasibility and effectiveness of the proposed algorithm. The experimental results show that the proposed MCPDE is an effective method for global optimization problems. |
topic |
cloud particles differential evolution exploration-exploitation inertia factor global optimization |
url |
http://www.mdpi.com/1999-4893/9/4/78 |
work_keys_str_mv |
AT weili amodifiedcloudparticlesdifferentialevolutionalgorithmforrealparameteroptimization AT weili modifiedcloudparticlesdifferentialevolutionalgorithmforrealparameteroptimization |
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1725130566473351168 |