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dc.contributor.authorAlabas-Uslu, Cigdem
dc.contributor.authorDengiz, Berna
dc.date.accessioned2019-12-17T07:12:04Z
dc.date.available2019-12-17T07:12:04Z
dc.date.issued2014
dc.identifier.issn1875-6891
dc.identifier.urihttps://www.atlantis-press.com/journals/ijcis/25868534
dc.identifier.urihttp://hdl.handle.net/11727/4455
dc.description.abstractThis paper introduces a new self-tuning mechanism to the local search heuristic for solving of combinatorial optimization problems. Parameter tuning of heuristics makes them difficult to apply, as parameter tuning itself is an optimization problem. For this purpose, a modified local search algorithm free from parameter tuning, called Self-Adaptive Local Search (SALS), is proposed for obtaining qualified solutions to combinatorial problems within reasonable amount of computer times. SALS is applied to several combinatorial optimization problems, namely, classical vehicle routing, permutation flow-shop scheduling, quadratic assignment, and topological design of networks. It is observed that self-adaptive structure of SALS provides implementation simplicity and flexibility to the considered combinatorial optimization problems. Detailed computational studies confirm the performance of SALS on the suit of test problems for each considered problem type especially in terms of solution quality.en_US
dc.language.isoengen_US
dc.relation.isversionof10.1080/18756891.2014.966992en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMetaheuristicsen_US
dc.subjectCombinatorial optimizationen_US
dc.subjectParameter tuningen_US
dc.subjectAdaptive parameteren_US
dc.titleA Self-Adaptive Heuristic Algorithm for Combinatorial Optimization Problemsen_US
dc.typearticleen_US
dc.relation.journalINTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMSen_US
dc.identifier.volume7en_US
dc.identifier.issue5en_US
dc.identifier.startpage827en_US
dc.identifier.endpage852en_US
dc.identifier.wos000343408700002


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