An ILP formulation and genetic algorithm for the Maximum Degree-Bounded Connected Subgraph problem

Bogdanović, Milena

doi:10.1016/j.camwa.2010.02.021

Cited by 2 publications

(3 citation statements)

References 15 publications

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“…In [3] is introduced Integer Linear Programming (ILP) formulation with exponential number of constraints, consequently, the standard ILP solvers were able to solve only very small instances. Additionally, for solving larger instances one metaheuristic approach based on genetic algorithm was proposed in [3,4]. It is based on binary representation and used fine-grained tournament selection, onepoint crossover, simple mutation with frozen genes and caching technique.…”

Section: Related Workmentioning

confidence: 99%

“…The first integer linear programming (ILP) formulation was proposed in [3]. There are great benefits for representing discrete optimization problems as integer linear programming models [14,3] or their generalization [20]. Since it has exponential number of constraints, its ability to solve MDBCSP instances is limited, so it is unable to solve even the instance presented in Example 1.…”

Section: An Existing Ilp Formulationmentioning

confidence: 99%

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A new mixed integer linear programming formulation for the maximum degree bounded connected subgraph problem

Maksimović

2016

Publ Inst Math (Belgr)

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Section: Related Workmentioning

confidence: 99%

Section: An Existing Ilp Formulationmentioning

confidence: 99%

A new mixed integer linear programming formulation for the maximum degree bounded connected subgraph problem

Maksimović

2016

Publ Inst Math (Belgr)

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“…ILP formulation of the model for finding the maximum degree of a connected subgraph constraints is given below, part of the paper [4].…”

Section: Considering An Undirected Graphmentioning

confidence: 99%

Solving the MDBCS Problem Using the Metaheuric–Genetic Algorithm

Bogdanović¹

2011

IJACSA

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Abstract-The problems degree-limited graph of nodes considering the weight of the vertex or weight of the edges, with the aim to find the optimal weighted graph in terms of certain restrictions on the degree of the vertices in the subgraph. This class of combinatorial problems was extensively studied because of the implementation and application in network design, connection of networks and routing algorithms. It is likely that solution of MDBCS problem will find its place and application in these areas. The paper is given an ILP model to solve the problem MDBCS, as well as the genetic algorithm, which calculates a good enough solution for the input graph with a greater number of nodes. An important feature of the heuristic algorithms is that can approximate, but still good enough to solve the problems of exponential complexity. However, it should solve the problem heuristic algorithms may not lead to a satisfactory solution, and that for some of the problems, heuristic algorithms give relatively poor results. This is particularly true of problems for which no exact polynomial algorithm complexity. Also, heuristic algorithms are not the same, because some parts of heuristic algorithms differ depending on the situation and problems in which they are used. These parts are usually the objective function (transformation), and their definition significantly affects the efficiency of the algorithm. By mode of action, genetic algorithms are among the methods directed random search space solutions are looking for a global optimum.

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An ILP formulation and genetic algorithm for the Maximum Degree-Bounded Connected Subgraph problem

Cited by 2 publications

References 15 publications

A new mixed integer linear programming formulation for the maximum degree bounded connected subgraph problem

A new mixed integer linear programming formulation for the maximum degree bounded connected subgraph problem

Solving the MDBCS Problem Using the Metaheuric–Genetic Algorithm

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