UDE: Differential Evolution with Uniform Design

Differential evolution (DE) is significantly faster and robust for solving numerical optimization problem and is more likely to find true global optimum of functions. It has solved many real-world optimization problems. However, DE has sometimes been shown slow convergence and low accuracy of soluti...

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Vydané v:2010 3rd International Symposium on Parallel Architectures, Algorithms and Programming s. 239 - 246
Hlavní autori: Lei Peng, Yuanzhen Wang, Guangming Dai
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.12.2010
Predmet:
Accuracy
Algorithm design and analysis
Benchmark testing
Chaos
Convergence
Design methodology
differential evolution
global optimization
Optimization
orthogonal design method
uniform design method
ISBN:1424494826, 9781424494828
ISSN:2168-3034
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Abstract Differential evolution (DE) is significantly faster and robust for solving numerical optimization problem and is more likely to find true global optimum of functions. It has solved many real-world optimization problems. However, DE has sometimes been shown slow convergence and low accuracy of solutions when the solution space is hard to explore. Population initialization is very important to the performance of differential evolution. A good initialization method can help in finding better solutions and improving convergence rate. In this paper, a uniform-differential evolution algorithm (UDE) is proposed. It incorporates uniform design initialization method into differential evolution to accelerate its convergence speed and improve the stability. UDE is compared with other two algorithms of standard differential evolution (SDE) and orthogonal differential evolution (ODE). Experiments have been conducted on 23 benchmark problems of diverse complexities. The results indicate that our approach has the stronger ability and higher calculation accuracy to find better solutions than other two algorithms.
AbstractList Differential evolution (DE) is significantly faster and robust for solving numerical optimization problem and is more likely to find true global optimum of functions. It has solved many real-world optimization problems. However, DE has sometimes been shown slow convergence and low accuracy of solutions when the solution space is hard to explore. Population initialization is very important to the performance of differential evolution. A good initialization method can help in finding better solutions and improving convergence rate. In this paper, a uniform-differential evolution algorithm (UDE) is proposed. It incorporates uniform design initialization method into differential evolution to accelerate its convergence speed and improve the stability. UDE is compared with other two algorithms of standard differential evolution (SDE) and orthogonal differential evolution (ODE). Experiments have been conducted on 23 benchmark problems of diverse complexities. The results indicate that our approach has the stronger ability and higher calculation accuracy to find better solutions than other two algorithms.
Author Guangming Dai
Yuanzhen Wang
Lei Peng
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  surname: Guangming Dai
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  email: gmdai@cug.edu.cn
  organization: Sch. of Comput., China Univ. of Geosci., Wuhan, China
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Snippet Differential evolution (DE) is significantly faster and robust for solving numerical optimization problem and is more likely to find true global optimum of...
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SubjectTerms Accuracy
Algorithm design and analysis
Benchmark testing
Chaos
Convergence
Design methodology
differential evolution
global optimization
Optimization
orthogonal design method
uniform design method
Title UDE: Differential Evolution with Uniform Design
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