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Analysing the performance of migrating birds optimisation approaches for large scale continuous problems

Lalla-Ruiz, Eduardo; Segredo, Eduardo; Voss, Stefan; Hart, Emma; Paechter, Ben

Authors

Eduardo Lalla-Ruiz

Eduardo Segredo

Stefan Voss



Abstract

We present novel algorithmic schemes for dealing with large scale continuous problems. They are based on the recently proposed population-based meta-heuristics Migrating Birds Optimisation (mbo) and Multi-leader Migrating Birds Optimisation (mmbo), that have shown to be effective for solving combinatorial problems. The main objective of the current paper is twofold. First, we introduce a novel neighbour generating operator based on Differential Evolution (de) that allows to produce new individuals in the continuous decision space starting from those belonging to the current population. Second, we evaluate the performance of mbo and mmbo by incorporating our novel operator to them. Hence, mbo and mmbo are enabled for solving continuous problems. Comparisons are carried out by applying both aforementioned schemes to a set of well-known large scale functions.

Presentation Conference Type Conference Paper (Published)
Conference Name 14th International Conference on Parallel Problem Solving from Nature (PPSN 2016)
Start Date Sep 17, 2016
End Date Sep 21, 2016
Acceptance Date May 30, 2016
Online Publication Date Aug 31, 2016
Publication Date 2016
Deposit Date May 31, 2016
Publicly Available Date Aug 31, 2016
Electronic ISSN 1611-3349
Publisher Springer
Peer Reviewed Peer Reviewed
Pages 134-144
Series Title Lecture Notes in Computer Science
Series Number 9921
Series ISSN 0302-9743
Book Title Parallel Problem Solving from Nature – PPSN XIV
ISBN 978-3-319-45823-6; 978-3-319-45822-9
DOI https://doi.org/10.1007/978-3-319-45823-6_13
Keywords Migrating birds optimization; population-based meta-heuristics; MMBO (Multi-leader Migrating Birds Optimisation); global optimization problems;
Public URL http://researchrepository.napier.ac.uk/id/eprint/10330
Contract Date May 31, 2016

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