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Using Competitive Population Evaluation in a differential evolution algorithm for dynamic environments

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Title: Using Competitive Population Evaluation in a differential evolution algorithm for dynamic environments
Authors: du Plessis, Mathys C. mc.duplessis@nmmu.ac.za, Engelbrecht, Andries P.1 engel@cs.up.ac.za
Source: European Journal of Operational Research. Apr2012, Vol. 218 Issue 1, p7-20. 14p.
Subjects: Algorithms, Population dynamics, Mathematical optimization, Stochastic analysis, Virtual reality, Stochastic differential equations
Abstract: Abstract: This paper proposes two adaptations to DynDE, a differential evolution-based algorithm for solving dynamic optimization problems. The first adapted algorithm, Competitive Population Evaluation (CPE), is a multi-population DE algorithm aimed at locating optima faster in the dynamic environment. This adaptation is based on allowing populations to compete for function evaluations based on their performance. The second adapted algorithm, Reinitialization Midpoint Check (RMC), is aimed at improving the technique used by DynDE to maintain populations on different peaks in the search space. A combination of the CPE and RMC adaptations is investigated. The new adaptations are empirically compared to DynDE using various problem sets. The empirical results show that the adaptations constitute an improvement over DynDE and compares favorably to other approaches in the literature. The general applicability of the adaptations is illustrated by incorporating the combination of CPE and RMC into another Differential Evolution-based algorithm, jDE, which is shown to yield improved results. [Copyright &y& Elsevier]
Copyright of European Journal of Operational Research is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 69953422
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Using Competitive Population Evaluation in a differential evolution algorithm for dynamic environments
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  Data: <searchLink fieldCode="AR" term="%22du+Plessis%2C+Mathys+C%2E%22">du Plessis, Mathys C.</searchLink><i> mc.duplessis@nmmu.ac.za</i><br /><searchLink fieldCode="AR" term="%22Engelbrecht%2C+Andries+P%2E%22">Engelbrecht, Andries P.</searchLink><relatesTo>1</relatesTo><i> engel@cs.up.ac.za</i>
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  Data: <searchLink fieldCode="JN" term="%22European+Journal+of+Operational+Research%22">European Journal of Operational Research</searchLink>. Apr2012, Vol. 218 Issue 1, p7-20. 14p.
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  Data: <searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Population+dynamics%22">Population dynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+analysis%22">Stochastic analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Virtual+reality%22">Virtual reality</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+differential+equations%22">Stochastic differential equations</searchLink>
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  Data: Abstract: This paper proposes two adaptations to DynDE, a differential evolution-based algorithm for solving dynamic optimization problems. The first adapted algorithm, Competitive Population Evaluation (CPE), is a multi-population DE algorithm aimed at locating optima faster in the dynamic environment. This adaptation is based on allowing populations to compete for function evaluations based on their performance. The second adapted algorithm, Reinitialization Midpoint Check (RMC), is aimed at improving the technique used by DynDE to maintain populations on different peaks in the search space. A combination of the CPE and RMC adaptations is investigated. The new adaptations are empirically compared to DynDE using various problem sets. The empirical results show that the adaptations constitute an improvement over DynDE and compares favorably to other approaches in the literature. The general applicability of the adaptations is illustrated by incorporating the combination of CPE and RMC into another Differential Evolution-based algorithm, jDE, which is shown to yield improved results. [Copyright &y& Elsevier]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of European Journal of Operational Research is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Population dynamics
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      – SubjectFull: Mathematical optimization
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      – SubjectFull: Virtual reality
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      – SubjectFull: Stochastic differential equations
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      – TitleFull: Using Competitive Population Evaluation in a differential evolution algorithm for dynamic environments
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              Text: Apr2012
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