Using semi-independent variables to enhance optimization search

Gandomi, AH; Deb, K; Averill, RC; Rahnamayan, S; Omidvar, MN

Using semi-independent variables to enhance optimization search

Gandomi, AH Deb, K Averill, RC Rahnamayan, S Omidvar, MN

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Expert Systems with Applications, 2019, 120, pp. 279-297
Issue Date:: 2019-04-15

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Field	Value	Language
dc.contributor.author	Gandomi, AH
dc.contributor.author	Deb, K
dc.contributor.author	Averill, RC
dc.contributor.author	Rahnamayan, S
dc.contributor.author	Omidvar, MN
dc.date.accessioned	2022-07-17T06:10:56Z
dc.date.available	2022-07-17T06:10:56Z
dc.date.issued	2019-04-15
dc.identifier.citation	Expert Systems with Applications, 2019, 120, pp. 279-297
dc.identifier.issn	0957-4174
dc.identifier.issn	1873-6793
dc.identifier.uri	http://hdl.handle.net/10453/158966
dc.description.abstract	In this study, the concept of a semi-independent variable (SIV) problem representation is investigated that embodies a set of expected or desired relationships among the original variables, with the goal of increasing search effectiveness and efficiency. The proposed approach intends to eliminate the generation of infeasible solutions associated with the known relationships among the variables and cutting the search space, thereby potentially improving a search algorithm's convergence rate and narrowing down the search space. However, this advantage does not come for free. The issue is the multiplicity of SIV formulations and their varying degree of complexity, especially with respect to variable interaction. In this paper, we propose the use of automatic variable interaction analysis methods to compare and contrast different SIV formulations. The performance of the proposed approach is demonstrated by implementing it within a number of classical and evolutionary optimization algorithms (namely, interior-point algorithm, simulated annealing, particle swarm optimization, genetic algorithm and differential evolution) in the application to several practical engineering problems. The case study results clearly show that the population-based algorithms can significantly benefit from the proposed SIV formulation resulting in better solutions with fewer function evaluations than in the original approach. The results also indicate that an automatic variable interaction analysis is capable of estimating the difficulty of the resultant SIV formulations prior to any optimization attempt.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Expert Systems with Applications
dc.relation.isbasedon	10.1016/j.eswa.2018.11.031
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Using semi-independent variables to enhance optimization search
dc.type	Journal Article
utslib.citation.volume	120
utslib.for	01 Mathematical Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2022-07-17T06:10:03Z
pubs.publication-status	Published
pubs.volume	120

Abstract:

In this study, the concept of a semi-independent variable (SIV) problem representation is investigated that embodies a set of expected or desired relationships among the original variables, with the goal of increasing search effectiveness and efficiency. The proposed approach intends to eliminate the generation of infeasible solutions associated with the known relationships among the variables and cutting the search space, thereby potentially improving a search algorithm's convergence rate and narrowing down the search space. However, this advantage does not come for free. The issue is the multiplicity of SIV formulations and their varying degree of complexity, especially with respect to variable interaction. In this paper, we propose the use of automatic variable interaction analysis methods to compare and contrast different SIV formulations. The performance of the proposed approach is demonstrated by implementing it within a number of classical and evolutionary optimization algorithms (namely, interior-point algorithm, simulated annealing, particle swarm optimization, genetic algorithm and differential evolution) in the application to several practical engineering problems. The case study results clearly show that the population-based algorithms can significantly benefit from the proposed SIV formulation resulting in better solutions with fewer function evaluations than in the original approach. The results also indicate that an automatic variable interaction analysis is capable of estimating the difficulty of the resultant SIV formulations prior to any optimization attempt.

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http://hdl.handle.net/10453/158966