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An adaptive differential evolution with combined strategy for global numerical optimization
- Source :
- Soft Computing. 24:6277-6296
- Publication Year :
- 2019
- Publisher :
- Springer Science and Business Media LLC, 2019.
-
Abstract
- Differential evolution (DE) is a simple yet powerful evolutionary algorithm for numerical optimization. However, the performance of DE significantly relies on its mutation operator and control parameters (scaling factor and crossover rate). In this paper, we propose a novel DE variant by introducing a series of combined strategies into DE, called CSDE. Specifically, in CSDE, to obtain a proper balance between global exploration ability and local exploitation ability, we adopt two mutation operators with different characteristics to produce the mutant vector, and provide a mechanism based on their own historical success rate to coordinate the two adopted mutation operators. Moreover, we combine a periodic function based on one modulo operation, an individual-independence macro-control function and an individual-dependence function based on individual’s fitness value information to adaptively produce scaling factor and crossover rate. To verify the effectiveness of the proposed CSDE, comparison experiments contained seven other state-of-the-art DE variants are tested on a suite of 30 benchmark functions and four real-world problems. The simulation results demonstrate that CSDE achieves the best overall performance among the eight DE variants.
- Subjects :
- 0209 industrial biotechnology
Mutation operator
Mathematical optimization
Series (mathematics)
Computer science
Evolutionary algorithm
Computational intelligence
02 engineering and technology
Function (mathematics)
Theoretical Computer Science
020901 industrial engineering & automation
Differential evolution
0202 electrical engineering, electronic engineering, information engineering
Benchmark (computing)
020201 artificial intelligence & image processing
Geometry and Topology
Software
Subjects
Details
- ISSN :
- 14337479 and 14327643
- Volume :
- 24
- Database :
- OpenAIRE
- Journal :
- Soft Computing
- Accession number :
- edsair.doi...........b59547475d8d55649c7a625b2a6ac0d6