The cascade optimization algorithm: A new distributed approach for the stochastic optimization of engineering applications

Kokossis, AC; Linke, P; Yang, S

dc.contributor.author	Kokossis, AC	en
dc.contributor.author	Linke, P	en
dc.contributor.author	Yang, S	en
dc.date.accessioned	2014-03-01T01:37:16Z
dc.date.available	2014-03-01T01:37:16Z
dc.date.issued	2011	en
dc.identifier.issn	0888-5885	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/21490
dc.subject.classification	Engineering, Chemical	en
dc.subject.other	Cost-effective means	en
dc.subject.other	Distributed approaches	en
dc.subject.other	Distributed Computing	en
dc.subject.other	Engineering applications	en
dc.subject.other	Management techniques	en
dc.subject.other	Optimization algorithms	en
dc.subject.other	Parallel and distributed computing	en
dc.subject.other	Stochastic optimization approach	en
dc.subject.other	Stochastic optimizations	en
dc.subject.other	Algorithms	en
dc.subject.other	Knowledge management	en
dc.subject.other	Lakes	en
dc.subject.other	Markov processes	en
dc.subject.other	Stochastic systems	en
dc.subject.other	Optimization	en
dc.title	The cascade optimization algorithm: A new distributed approach for the stochastic optimization of engineering applications	en
heal.type	journalArticle	en
heal.identifier.primary	10.1021/ie1014603	en
heal.identifier.secondary	http://dx.doi.org/10.1021/ie1014603	en
heal.language	English	en
heal.publicationDate	2011	en
heal.abstract	This paper introduces a new stochastic optimization approach in the form of a cascade optimization algorithm. The algorithm incorporates concepts from Markov processes while eliminating the inherent sequential nature that is a major obstacle preventing the exploitation of advances in distributed computing infrastructures. This method introduces partitions and pools to store intermediate solutions and corresponding objectives. A Markov process increases the population of partitions and pools. The population is distributed periodically, following an external certainty. With the use of partitions and pools, multiple Markov processes can be launched simultaneously for different partitions and pools. The cascade optimization algorithm holds a potential in two different fronts. One aims at its deployment in parallel and distributed computing environments. Through storage of solutions in the pools, the algorithm further offers cost-effective means to analyze intermediate solutions, visualize progress, and integrate optimization with data and/or knowledge management techniques without additional burden to the process. © 2011 American Chemical Society.	en
heal.publisher	AMER CHEMICAL SOC	en
heal.journalName	Industrial and Engineering Chemistry Research	en
dc.identifier.doi	10.1021/ie1014603	en
dc.identifier.isi	ISI:000289764000035	en
dc.identifier.volume	50	en
dc.identifier.issue	9	en
dc.identifier.spage	5266	en
dc.identifier.epage	5278	en