Construction of the comprehensive multi-layer graph model of the search spaces associated with the combinatorial optimization problems

2017 ◽  
Author(s):  
Artem Potebnia
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problems ◽  
Graph Model ◽  
Search Spaces ◽  
Combinatorial Optimization Problems

GUIDING CONSTRUCTIVE SEARCH WITH STATISTICAL INSTANCE-BASE LEARNING

2002 ◽  
Vol 11 (02) ◽  
pp. 247-266 ◽  
Author(s):  
ORESTIS TELELIS ◽  
PANAGIOTIS STAMATOPOULOS
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problems ◽  
Statistical Information ◽  
Combinatorial Problems ◽  
Set Partitioning ◽  
Greedy Heuristics ◽  
High Quality ◽  
Search Spaces ◽  
Combinatorial Optimization Problems ◽  
Real World Applications

Several real world applications involve solving combinatorial optimization problems. Commonly, existing heuristic approaches are designed to address specific difficulties of the underlying problem and are applicable only within its framework. We suspect, however, that search spaces of combinatorial problems are rich in intuitive statistical and numerical information, which could be exploited heuristically in a generic manner, towards achievement of optimized solutions. Our work presents such a heuristic methodology, which can be adequately configured for several types of optimization problems. Experimental results are discussed, concerning two widely used problem models, namely the Set Partitioning and the Kanpsack problems. It is shown that, by gathering statistical information upon previously found solutions to the problems, the heuristic is able to incrementally adapt its behaviour and reach high quality solutions, exceeding the ones obtained by commonly used greedy heuristics.

Sign in / Sign up

Export Citation Format

Share Document