Max-min ant system applied in economic load dispatch

Vlachos, A; Moue, A

dc.contributor.author	Vlachos, A	en
dc.contributor.author	Moue, A	en
dc.date.accessioned	2014-03-01T01:54:27Z
dc.date.available	2014-03-01T01:54:27Z
dc.date.issued	2005	en
dc.identifier.issn	11092750	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/27394
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-22944439740&partnerID=40&md5=7975f77a672e5c9b0eae54b74e512b9a	en
dc.subject	Ant colony optimization	en
dc.subject	Economic load dispatch	en
dc.subject	Max-Min ant system	en
dc.subject.other	Electric load dispatching	en
dc.subject.other	Evolutionary algorithms	en
dc.subject.other	Mathematical models	en
dc.subject.other	Operations research	en
dc.subject.other	Ant colony optimization	en
dc.subject.other	Economic load dispatch	en
dc.subject.other	Max-Min Ant System	en
dc.subject.other	Optimization	en
dc.title	Max-min ant system applied in economic load dispatch	en
heal.type	journalArticle	en
heal.publicationDate	2005	en
heal.abstract	This paper presents a new approach to Economic Load Dispatch (ELD) problems using the Max-Min Ant System Optimization. Historically, traditional optimizations techniques have been used, such as linear and non-linear programming, but within the past decade the focus has shifted on the use of Evolutionary Algorithms, for example Genetic Algorithms, Simulated Annealing and more recently Ant Colony Optimization (ACO). In this paper we introduce the Max-Min Ant System based version of the Ant System. This algorithm encourages local searching around the best solution found in each iteration. To show its efficiency and effectiveness, the proposed Max-Min Ant System is applied to sample ELD problems composed of 4 generators. Comparison to conventional genetic algorithms is presented.	en
heal.journalName	WSEAS Transactions on Computers	en
dc.identifier.volume	4	en
dc.identifier.issue	7	en
dc.identifier.spage	711	en
dc.identifier.epage	717	en