Optimization of energy systems based on Evolutionary and Social metaphors

Dimopoulos, GG; Frangopoulos, CA

dc.contributor.author	Dimopoulos, GG	en
dc.contributor.author	Frangopoulos, CA	en
dc.date.accessioned	2014-03-01T01:28:58Z
dc.date.available	2014-03-01T01:28:58Z
dc.date.issued	2008	en
dc.identifier.issn	0360-5442	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19050
dc.subject	Energy systems	en
dc.subject	Evolutionary programming	en
dc.subject	Particle swarm optimization	en
dc.subject.classification	Thermodynamics	en
dc.subject.classification	Energy & Fuels	en
dc.subject.other	Constraint theory	en
dc.subject.other	Evolutionary algorithms	en
dc.subject.other	Optimization	en
dc.subject.other	Problem solving	en
dc.subject.other	Energy systems	en
dc.subject.other	Particle swarm optimization	en
dc.subject.other	Energy management	en
dc.subject.other	Constraint theory	en
dc.subject.other	Energy management	en
dc.subject.other	Evolutionary algorithms	en
dc.subject.other	Optimization	en
dc.subject.other	Problem solving	en
dc.subject.other	accuracy assessment	en
dc.subject.other	engineering	en
dc.subject.other	genetic algorithm	en
dc.subject.other	methodology	en
dc.subject.other	optimization	en
dc.title	Optimization of energy systems based on Evolutionary and Social metaphors	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.energy.2007.09.002	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.energy.2007.09.002	en
heal.language	English	en
heal.publicationDate	2008	en
heal.abstract	Optimization problems that arise in energy systems design often have several features that hinder the use of many optimization techniques. These optimization problems have non-continuous mixed variable definition domains, are heavily constrained, are multimodal (i.e. have many local optima) and, foremost, the functions used to define the engineering optimization problem are often computationally intensive. Three methods are tested here: (a) a Struggle Genetic Algorithm (StrGA), (b) a Particle Swarm Optimization Algorithm (PSOA), and (c) a PSOA with Struggle Selection (PSOStr). The last is a hybrid of the evolutionary StrGA and the socially inspired PSOA. They are tested in four purely mathematical and three energy systems thermoeconomic optimization problems. All of the methods solved successfully all the problems. The PSOStr, however, outperformed the other methods in terms of both solution accuracy and computational Cost (i.e. function evaluations). (C) 2007 Elsevier Ltd. All rights reserved.	en
heal.publisher	PERGAMON-ELSEVIER SCIENCE LTD	en
heal.journalName	Energy	en
dc.identifier.doi	10.1016/j.energy.2007.09.002	en
dc.identifier.isi	ISI:000253574700009	en
dc.identifier.volume	33	en
dc.identifier.issue	2	en
dc.identifier.spage	171	en
dc.identifier.epage	179	en