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**Harvard**

Glorieux, E., Svensson, B., Danielsson, F. och Lennartson, B. (2015) *Improved Constructive Cooperative Coevolutionary Differential Evolution for Large-Scale Optimisation*.

** BibTeX **

@conference{

Glorieux2015,

author={Glorieux, E. and Svensson, B. and Danielsson, F. and Lennartson, Bengt},

title={Improved Constructive Cooperative Coevolutionary Differential Evolution for Large-Scale Optimisation},

booktitle={2015 Ieee Symposium Series on Computational Intelligence (Ieee Ssci)},

isbn={978-1-4799-7560-0},

pages={1703-1710},

abstract={The Differential Evolution (DE) algorithm is widely used for real-world global optimisation problems in many different domains. To improve DE's performance on large-scale optimisation problems, it has been combined with the Cooperative Coevolution (CCDE) algorithm. CCDE adopts a divide-and-conquer strategy to optimise smaller subcomponents separately instead of tackling the large-scale problem at once. DE then evolves a separate subpopulation for each subcomponent but there is cooperation between the subpopulations to co-adapt the individuals of the subpopulations with each other. The Constructive Cooperative Coevolution ((CDE)-D-3) algorithm, previously proposed by the authors, is an extended version of CCDE that has a better performance on large-scale problems, interestingly also on non-separable problems. This paper proposes a new version, called the Improved Constructive Cooperative Coevolutionary Differential Evolution ((CDE)-D-3i), which removes several limitations with the previous version. A novel element of (CDE)-D-3i is the advanced initialisation of the subpopulations. (CDE)-D-3i initially optimises the subpopulations in a partially co-adaptive fashion. During the initial optimisation of a subpopulation, only a subset of the other subcomponents is considered for the co-adaptation. This subset increases stepwise until all subcomponents are considered. The experimental evaluation of (CDE)-D-3i on 36 high-dimensional benchmark functions (up to 1000 dimensions) shows an improved solution quality on large-scale global optimisation problems compared to CCDE and DE. The greediness of the co-adaptation with (CDE)-D-3i is also investigated in this paper.},

year={2015},

keywords={global optimization, algorithm, spaces, Computer Science },

}

** RefWorks **

RT Conference Proceedings

SR Electronic

ID 241904

A1 Glorieux, E.

A1 Svensson, B.

A1 Danielsson, F.

A1 Lennartson, Bengt

T1 Improved Constructive Cooperative Coevolutionary Differential Evolution for Large-Scale Optimisation

YR 2015

T2 2015 Ieee Symposium Series on Computational Intelligence (Ieee Ssci)

SN 978-1-4799-7560-0

SP 1703

OP 1710

AB The Differential Evolution (DE) algorithm is widely used for real-world global optimisation problems in many different domains. To improve DE's performance on large-scale optimisation problems, it has been combined with the Cooperative Coevolution (CCDE) algorithm. CCDE adopts a divide-and-conquer strategy to optimise smaller subcomponents separately instead of tackling the large-scale problem at once. DE then evolves a separate subpopulation for each subcomponent but there is cooperation between the subpopulations to co-adapt the individuals of the subpopulations with each other. The Constructive Cooperative Coevolution ((CDE)-D-3) algorithm, previously proposed by the authors, is an extended version of CCDE that has a better performance on large-scale problems, interestingly also on non-separable problems. This paper proposes a new version, called the Improved Constructive Cooperative Coevolutionary Differential Evolution ((CDE)-D-3i), which removes several limitations with the previous version. A novel element of (CDE)-D-3i is the advanced initialisation of the subpopulations. (CDE)-D-3i initially optimises the subpopulations in a partially co-adaptive fashion. During the initial optimisation of a subpopulation, only a subset of the other subcomponents is considered for the co-adaptation. This subset increases stepwise until all subcomponents are considered. The experimental evaluation of (CDE)-D-3i on 36 high-dimensional benchmark functions (up to 1000 dimensions) shows an improved solution quality on large-scale global optimisation problems compared to CCDE and DE. The greediness of the co-adaptation with (CDE)-D-3i is also investigated in this paper.

LA eng

DO 10.1109/ssci.2015.239

LK http://dx.doi.org/10.1109/ssci.2015.239

OL 30