Cited By
View all- Guan J(2016)Differential evolution with a dimensional mutation strategy for global optimization2016 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2016.7744142(2799-2804)Online publication date: Jul-2016
A dimensional-level adaptive differential evolutionary algorithm for continuous optimization
Authors: 
Ming Yang, Jing Guan, Zhihua Cai, Changhe LiAuthors Info & Claims
Pages 123 - 124
Abstract
In differential evolution (DE), the optimal value of the control parameters are problem-dependent. Many improved DE algorithms have been proposed with the aim of improving the exploration ability by adaptively adjusting the values of F. In those algorithms, although the value of F is adaptive at the individual level or at the population level, the value is the same for all dimensions of each individual. Individuals are close to the global optimum at some dimensions, but they may be far away from the global optimum at other dimensions. This indicated that different values of F may be needed for different dimensions. This paper proposed an adaptive scheme for the parameter F at the dimensional level. The scheme was incorporated into the jDE algorithm and tested on a set of 25 scalable benchmark functions. The results showed that the scheme improved the performance of the jDE algorithm, particularly in comparisons with several other peer algorithms on high-dimensional functions.
References
[1]
J. Brest, S. Greiner, B. Boskovic, M. Mernik, and V. Zumer. Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE Transactions on Evolutionary Computation, 10(6):646--657, 2006.
[2]
V. Feoktistov and S. Janaqi. Generalization of the strategies in differential evolution. In Proceedings of the 18th International Parallel and Distributed Processing Symposium, pages 165--170, April 2004.
[3]
E. Mezura-Montes, J. Velázquez-Reyes, and C. A. Coello Coello. A comparative study of differential evolution variants for global optimization. In Proceedings of the 8th annual conference on Genetic and evolutionary computation, GECCO2006, pages 485--492, New York, NY, USA, 2006. ACM.
[4]
R. Storn and K. Price. Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4):341--359, December 1997.
Index Terms
- A dimensional-level adaptive differential evolutionary algorithm for continuous optimization
Recommendations
Population size reduction for the differential evolution algorithm
This paper studies the efficiency of a recently defined population-based direct global optimization method called Differential Evolution with self-adaptive control parameters. The original version uses fixed population size but a method for gradually ...
An improved adaptive differential evolution algorithm with population adaptation
GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computationIn differential evolution (DE), there are many adaptive algorithms proposed for parameters adaptation. However, they mainly aim at tuning the amplification factor F and crossover probability CR. When the population diversity is at a low level or the ...
Enhanced opposition-based differential evolution for solving high-dimensional continuous optimization problems
Special Issue on scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problemsThis paper presents a novel algorithm based on generalized opposition-based learning (GOBL) to improve the performance of differential evolution (DE) to solve high-dimensional optimization problems efficiently. The proposed approach, namely GODE, ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
July 2014
1524 pages
Copyright © 2014 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 12 July 2014
Check for updates
Author Tags
Qualifiers
- Poster
Funding Sources
- National Natural Science Foundation of China
- Fundamental Research Funds for the Central Universities
Conference
GECCO '14
Sponsor:
Acceptance Rates
GECCO Comp '14 Paper Acceptance Rate 180 of 544 submissions, 33%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 67Total Downloads
- Downloads (Last 12 months)3
- Downloads (Last 6 weeks)0
Reflects downloads up to 03 Oct 2024
Other Metrics
Citations
Cited By
View all- Guan J(2016)Differential evolution with a dimensional mutation strategy for global optimization2016 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2016.7744142(2799-2804)Online publication date: Jul-2016
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in