A line up evolutionary algorithm for solving nonlinear constrained optimization problems

Sarimveis, H; Nikolakopoulos, A

dc.contributor.author	Sarimveis, H	en
dc.contributor.author	Nikolakopoulos, A	en
dc.date.accessioned	2014-03-01T01:21:44Z
dc.date.available	2014-03-01T01:21:44Z
dc.date.issued	2005	en
dc.identifier.issn	0305-0548	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/16342
dc.subject	Constrained optimization	en
dc.subject	Evolutionary algorithms	en
dc.subject	Nonlinear programming	en
dc.subject	Penalty adaptation	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Engineering, Industrial	en
dc.subject.classification	Operations Research & Management Science	en
dc.subject.other	Benchmarking	en
dc.subject.other	Evolutionary algorithms	en
dc.subject.other	Functions	en
dc.subject.other	Mathematical operators	en
dc.subject.other	Nonlinear programming	en
dc.subject.other	Optimization	en
dc.subject.other	Constrained optimization	en
dc.subject.other	Line-up differential evolution (LUDE)	en
dc.subject.other	Nonlinear programming problems (NLP)	en
dc.subject.other	Penalty adaptation	en
dc.subject.other	Problem solving	en
dc.title	A line up evolutionary algorithm for solving nonlinear constrained optimization problems	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.cor.2003.11.015	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.cor.2003.11.015	en
heal.language	English	en
heal.publicationDate	2005	en
heal.abstract	In this work a complete framework is presented for solving nonlinear constrained optimization problems. based on the line-up differential evolution (LUDE) algorithm which is proposed for solving unconstrained problems. Linear and/or nonlinear constraints are handled by embodying them in an augmented Lagrangian function, where the penalty parameters and multipliers are adapted as the execution of the algorithm proceeds. The LUDE algorithm maintains a population of solutions.. which is continuously improved as it thrives From generation to generation. In each generation the solutions are lined up according to the corresponding objective function values. The position's in the line are very important.. since they determine to What extent the crossover and the mutation operators are applied to each particular solution. The efficiency of the proposed methodoloy is illustrated by solving numerous unconstrained and constrained optimization problems and comparing it With other optimization techniques that can be found in the literature. (C) 2003 Elsevier Ltd. All rights reserved.	en
heal.publisher	PERGAMON-ELSEVIER SCIENCE LTD	en
heal.journalName	Computers and Operations Research	en
dc.identifier.doi	10.1016/j.cor.2003.11.015	en
dc.identifier.isi	ISI:000225907500007	en
dc.identifier.volume	32	en
dc.identifier.issue	6	en
dc.identifier.spage	1499	en
dc.identifier.epage	1514	en