article

Population size reduction for the differential evolution algorithm

Authors:

Mirjam Sepesy MaučecAuthors Info & Claims

Applied Intelligence, Volume 29, Issue 3

Pages 228 - 247

https://doi.org/10.1007/s10489-007-0091-x

Published: 01 December 2008 Publication History

Abstract

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 reducing population size is proposed in this paper. It improves the efficiency and robustness of the algorithm and can be applied to any variant of a Differential Evolution algorithm. The proposed modification is tested on commonly used benchmark problems for unconstrained optimization and compared with other optimization methods such as Evolutionary Algorithms and Evolution Strategies.

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Cited By

Hadikhani PLai DOng WNadimi-Shahraki M(2024)Automatic deep sparse clustering with a dynamic population-based evolutionary algorithm using reinforcement learning and transfer learningImage and Vision Computing10.1016/j.imavis.2024.105258151:COnline publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.imavis.2024.105258
Mitran MKononova ACaraffini FZaharie DSilva SPaquete L(2023)Patterns of Convergence and Bound Constraint Violation in Differential Evolution on SBOX-COST Benchmarking SuiteProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3596410(2337-2345)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583133.3596410
Vermetten DCaraffini FKononova ABäck TSilva SPaquete L(2023)Modular Differential EvolutionProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590417(864-872)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590417
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Population size reduction for the differential evolution algorithm

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Published In

cover image Applied Intelligence

Applied Intelligence Volume 29, Issue 3

December 2008

134 pages

ISSN:0924-669X

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Copyright © Copyright © 2008 Springer Science+Business Media, LLC.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 December 2008

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Cited By

Hadikhani PLai DOng WNadimi-Shahraki M(2024)Automatic deep sparse clustering with a dynamic population-based evolutionary algorithm using reinforcement learning and transfer learningImage and Vision Computing10.1016/j.imavis.2024.105258151:COnline publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.imavis.2024.105258
Mitran MKononova ACaraffini FZaharie DSilva SPaquete L(2023)Patterns of Convergence and Bound Constraint Violation in Differential Evolution on SBOX-COST Benchmarking SuiteProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3596410(2337-2345)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583133.3596410
Vermetten DCaraffini FKononova ABäck TSilva SPaquete L(2023)Modular Differential EvolutionProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590417(864-872)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590417
Ahmed RRangaiah GMahadzir SMirjalili SHassan MKamel S(2023)Memory, evolutionary operator, and local search based improved Grey Wolf Optimizer with linear population size reduction techniqueKnowledge-Based Systems10.1016/j.knosys.2023.110297264:COnline publication date: 15-Mar-2023
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Song ZMeng Z(2023)Differential Evolution with wavelet basis function based parameter control and dimensional interchange for diversity enhancementApplied Soft Computing10.1016/j.asoc.2023.110492144:COnline publication date: 1-Sep-2023
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Buakum DWisittipanich W(2022)Selective Strategy Differential Evolution for Stochastic Internal Task Scheduling Problem in Cross-Docking TerminalsComputational Intelligence and Neuroscience10.1155/2022/13984482022Online publication date: 1-Jan-2022
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Chen SBolufé-Röhler AMontgomery JTamayo-Vera DHendtlass T(2022)Measuring the Effects of Increasing Dimensionality on Fitness-Based Selection and Failed Exploration2022 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC55065.2022.9870409(1-8)Online publication date: 18-Jul-2022
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Kitamura TFukunaga A(2022)Differential Evolution with an Unbounded Population2022 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC55065.2022.9870363(1-8)Online publication date: 18-Jul-2022
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