Work

Combinatorial optimization by iterative partial transcription

Year: 1999

Type: article

Abstract: A procedure is presented that considerably improves the performance of local search based heuristic algorithms for combinatorial optimization problems. It increases the average ``gain'' of the individual local searches by merging pairs of solutions: certain parts of either solution are transcribed by the related parts of the respective other solution, corresponding to flipping clusters of a spin glass. This iterative partial transcription acts as a local search in the subspace spanned by the differing components of both solutions. Embedding it in the simple multistart-local-search algorithm and in the thermal-cycling method, we demonstrate its effectiveness for several instances of the traveling salesman problem. The obtained results indicate that, for this task, such approaches are far superior to simulated annealing.

Source: Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics

DOI: 10.1103/physreve.59.4667

Authors: A. Möbius, B. Freisleben, P. Merz, M. Schreiber

Institutions: Leibniz Institute for Solid State and Materials Research, University of Siegen, Chemnitz University of Technology

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Deutsche Forschungsgemeinschaft, Sächsisches Staatsministerium für Wissenschaft und Kunst

OpenAlex ID: W2045762758

FWCI: 2.036

Cites: 32

Cited by: 41

Related to: 10