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Repeated weighted boosting search for discrete or mixed search space and multiple-objective optimisation

Repeated weighted boosting search for discrete or mixed search space and multiple-objective optimisation
Repeated weighted boosting search for discrete or mixed search space and multiple-objective optimisation
Repeated weighted boosting search (RWBS) optimisation is a guided stochastic search algorithm that is capable of handling the difficult optimisation problems with non-smooth and/or multi-modal cost functions. Compared with other alternatives for global optimisation solvers, such as the genetic algorithms and adaptive simulated annealing, RWBS is easier to implement, has fewer algorithmic parameters to tune and has been shown to provide similar levels of performance on many benchmark problems. In its original form, however, RWBS is only applicable to unconstrained, single-objective problems with continuous search spaces. This contribution begins with an analysis of the performance of the original RWBS algorithm and then proceeds to develop a number of novel extensions to the algorithm which facilitate its application to a more general class of optimisation problems, including those with discrete and mixed search spaces as well as multiple objective functions. The performance of the extended or generalised RWBS algorithms are compared with other standard techniques on a range of benchmark problems, and the results obtained demonstrate that the proposed generalised RWBS algorithms offer excellent performance whilst retaining the benefits of the original RWBS algorithm.
global optimisation, constrained optimisation, discrete and mixed search space, multiple-objective optimisation, pareto-optimality, genetic algorithms, repeated weighted boosting search
1568-4946
2740-2755
Page, Scott F.
b93099f1-3575-4bbd-8e04-a193402e65a5
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Harris, Chris J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
White, Neil M.
c7be4c26-e419-4e5c-9420-09fc02e2ac9c
Page, Scott F.
b93099f1-3575-4bbd-8e04-a193402e65a5
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Harris, Chris J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
White, Neil M.
c7be4c26-e419-4e5c-9420-09fc02e2ac9c

Page, Scott F., Chen, Sheng, Harris, Chris J. and White, Neil M. (2012) Repeated weighted boosting search for discrete or mixed search space and multiple-objective optimisation. Applied Soft Computing, 12 (9), 2740-2755. (doi:10.1016/j.asoc.2012.03.056).

Record type: Article

Abstract

Repeated weighted boosting search (RWBS) optimisation is a guided stochastic search algorithm that is capable of handling the difficult optimisation problems with non-smooth and/or multi-modal cost functions. Compared with other alternatives for global optimisation solvers, such as the genetic algorithms and adaptive simulated annealing, RWBS is easier to implement, has fewer algorithmic parameters to tune and has been shown to provide similar levels of performance on many benchmark problems. In its original form, however, RWBS is only applicable to unconstrained, single-objective problems with continuous search spaces. This contribution begins with an analysis of the performance of the original RWBS algorithm and then proceeds to develop a number of novel extensions to the algorithm which facilitate its application to a more general class of optimisation problems, including those with discrete and mixed search spaces as well as multiple objective functions. The performance of the extended or generalised RWBS algorithms are compared with other standard techniques on a range of benchmark problems, and the results obtained demonstrate that the proposed generalised RWBS algorithms offer excellent performance whilst retaining the benefits of the original RWBS algorithm.

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e-pub ahead of print date: 24 April 2012
Published date: September 2012
Keywords: global optimisation, constrained optimisation, discrete and mixed search space, multiple-objective optimisation, pareto-optimality, genetic algorithms, repeated weighted boosting search
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 340785
URI: http://eprints.soton.ac.uk/id/eprint/340785
ISSN: 1568-4946
PURE UUID: e9fecc08-4a3b-4fe1-801a-13bee97a0cb0
ORCID for Neil M. White: ORCID iD orcid.org/0000-0003-1532-6452

Catalogue record

Date deposited: 04 Jul 2012 09:06
Last modified: 20 Jul 2019 01:24

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Contributors

Author: Scott F. Page
Author: Sheng Chen
Author: Chris J. Harris
Author: Neil M. White ORCID iD

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