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Constructive cooperative coevolution for large-scale global optimisation

E. Glorieux ; B. Svensson ; F. Danielsson ; Bengt Lennartson (Institutionen för elektroteknik, Automation)
Journal of Heuristics (1381-1231). Vol. 23 (2017), 6, p. 449-469.
[Artikel, refereegranskad vetenskaplig]

This paper presents the Constructive Cooperative Coevolutionary () algorithm, applied to continuous large-scale global optimisation problems. The novelty of is that it utilises a multi-start architecture and incorporates the Cooperative Coevolutionary algorithm. The considered optimisation problem is decomposed into subproblems. An embedded optimisation algorithm optimises the subproblems separately while exchanging information to co-adapt the solutions for the subproblems. Further, includes a novel constructive heuristic that generates different feasible solutions for the entire problem and thereby expedites the search. In this work, two different versions of are evaluated on high-dimensional benchmark problems, including the CEC'2013 test suite for large-scale global optimisation. is compared with several state-of-the-art algorithms, which shows that is among the most competitive algorithms. outperforms the other algorithms for most partially separable functions and overlapping functions. This shows that is an effective algorithm for large-scale global optimisation. This paper demonstrates the enhanced performance by using constructive heuristics for generating initial feasible solutions for Cooperative Coevolutionary algorithms in a multi-start framework.

Nyckelord: Evolutionary optimisation, Cooperative coevolution, Algorithm design and analysis, Large-scale optimisation



Denna post skapades 2017-11-21. Senast ändrad 2017-12-13.
CPL Pubid: 253271

 

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Institutioner (Chalmers)

Institutionen för elektroteknik, Automation

Ämnesområden

Datorseende och robotik (autonoma system)

Chalmers infrastruktur