Parameter Tuning for Local-Search-Based Matheuristic Methods

Algorithms that aim to solve optimisation problems by combining heuristics and mathematical programming have attracted researchers’ attention. These methods, also known as matheuristics, have been shown to perform especially well for large, complex optimisation problems that include both integer and...

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Bibliographic Details
Published inComplexity (New York, N.Y.) Vol. 2017; no. 2017; pp. 1 - 15
Main Authors Paredes, Fernando, Johnson, Franklin, Castañeda, Carolina, Lagos, Carolina, Cabrera-Guerrero, Guillermo, Cabrera, Enrique
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Publishing Corporation 01.01.2017
Hindawi
Hindawi Limited
Wiley
Subjects
Algorithms
Artificial intelligence
Bioinformatics
Combinatorial analysis
Computer science
Continuity (mathematics)
Heuristic
Heuristic methods
Integer programming
Logistics
Mathematical analysis
Mathematical programming
Medical research
Mixed integer
Nonlinear programming
Operations research
Optimization
Parameter modification
Parameters
Radiation therapy
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Summary:Algorithms that aim to solve optimisation problems by combining heuristics and mathematical programming have attracted researchers’ attention. These methods, also known as matheuristics, have been shown to perform especially well for large, complex optimisation problems that include both integer and continuous decision variables. One common strategy used by matheuristic methods to solve such optimisation problems is to divide the main optimisation problem into several subproblems. While heuristics are used to seek for promising subproblems, exact methods are used to solve them to optimality. In general, we say that both mixed integer (non)linear programming problems and combinatorial optimisation problems can be addressed using this strategy. Beside the number of parameters researchers need to adjust when using heuristic methods, additional parameters arise when using matheuristic methods. In this paper we focus on one particular parameter, which determines the size of the subproblem. We show how matheuristic performance varies as this parameter is modified. We considered a well-known NP-hard combinatorial optimisation problem, namely, the capacitated facility location problem for our experiments. Based on the obtained results, we discuss the effects of adjusting the size of subproblems that are generated when using matheuristics methods such as the one considered in this paper.
ISSN:1076-2787
1099-0526
DOI:10.1155/2017/1702506