MIP neighborhood synthesis through semantic feature extraction and automatic algorithm configuration

•We propose an Automatic Neighborhood Design algorithm.•The procedure relies on the extraction of semantic features from a MIP model.•The algorithm is assessed on four well-known combinatorial optimization problems. The definition of a “good” neighborhood structure on the solution space is a key ste...

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Bibliographic Details
Published in:Computers & operations research Vol. 83; pp. 106 - 119
Main Authors: Adamo, Tommaso, Ghiani, Gianpaolo, Grieco, Antonio, Guerriero, Emanuela, Manni, Emanuele
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 01.07.2017
Pergamon Press Inc
Subjects:
Combinatorial analysis
Experimentation
Feature extraction
Integer programming
Linear programming
Mixed integer
Mixed Integer Programming
Neighborhood search
Neighborhoods
Operations research
Semantic features
Semantics
Solution space
Neighborhood search
Semantic features
Mixed Integer Programming
ISSN:0305-0548, 1873-765X, 0305-0548
Online Access:Get full text
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Summary:•We propose an Automatic Neighborhood Design algorithm.•The procedure relies on the extraction of semantic features from a MIP model.•The algorithm is assessed on four well-known combinatorial optimization problems. The definition of a “good” neighborhood structure on the solution space is a key step when designing several types of heuristics for Mixed Integer Programming (MIP). Typically, in order to achieve efficiency in the search, the neighborhood structures need to be tailored not only to the specific problem but also to the peculiar distribution of the instances to be solved (reference instance population). Nowadays, this is done by human experts through a time-consuming process comprising: (a) problem analysis, (b) literature scouting and (c) experimentation. In this paper, we illustrate an Automatic Neighborhood Design algorithm that mimics steps (a) and (c). Firstly, the procedure extracts some semantic features from a MIP compact model. Secondly, these features are used to derive automatically some neighborhood design mechanisms. Finally, the “proper mix” of such mechanisms is sought through an automatic configuration phase performed on a training set representative of the reference instance population. When assessed on four well-known combinatorial optimization problems, our automatically-generated neighborhoods outperform state-of-the-art model-based neighborhoods with respect to both scalability and solution quality.
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ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2017.01.021