Using concepts of fuzzy mathematics for formalization and solution of combinatorial optimization problems

Сергиенко, И. В.; Kaspshitskaya, M. F.

doi:10.1007/bf02366930

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General Classification Of Co Methods1

Subclasses Of Solvable Problems1

Introduction1

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2009

2014

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Cited by 6 publications

(6 citation statements)

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“…The first three classes have been used for a long time and detailed in the literature [7,10,11,[14][15][16][17]. Evolutionary methods constitute a wide class of algorithms that employ principles of biological evolution to solve optimization problems [18,19].…”

Section: General Classification Of Co Methodsmentioning

confidence: 99%

Classification of applied methods of combinatorial optimization

2009

Self Cite

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The paper reviews most popular approaches to the development of applied methods of combinatorial optimization. A number of characteristics and criteria are proposed that underlie the classification of approximate algorithms. The classification continues the previous investigations in combinatorial optimization and allows determining key components of computational schemes used in constructing efficient hybrid metaheuristics.

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Section: General Classification Of Co Methodsmentioning

confidence: 99%

Classification of applied methods of combinatorial optimization

2009

Self Cite

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“…Almost simultaneously, Demidenko [22] found the global minimum for the matrix specified by condition (2). He proved that for the traveling salesman problem where the input information is specified by a matrix with condition (2), the objective function achieved the minimum value for a pyramidal tour (the permutation 1 3 5 7 8 6 4 2 , , , , , , , , K ) [25,26].…”

Section: Subclasses Of Solvable Problemsmentioning

confidence: 99%

Subclasses of solvable problems from classes of combinatorial optimization problems

Timofeeva

2009

Cybern Syst Anal

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“…[1][2][3][4]. Depending on the complexity of problems and objectives, it can be interpreted as a solution of certain extremum problem of searching the maximum or minimum value of a given parameter to be optimized.…”

Section: Introductionmentioning

confidence: 99%