A Self-Adaptive Heuristic Algorithm for Combinatorial Optimization Problems

DOI

10.1080/18756891.2014.966992How to use a DOI?

Keywords

Metaheuristics, Combinatorial optimization, Parameter tuning, Adaptive parameter

Abstract

This paper introduces a new self-tuning mechanism to the local search heuristic for solving of combinatorial optimization problems. Parameter tuning of heuristics makes them difficult to apply, as parameter tuning itself is an optimization problem. For this purpose, a modified local search algorithm free from parameter tuning, called Self-Adaptive Local Search (SALS), is proposed for obtaining qualified solutions to combinatorial problems within reasonable amount of computer times. SALS is applied to several combinatorial optimization problems, namely, classical vehicle routing, permutation flow-shop scheduling, quadratic assignment, and topological design of networks. It is observed that self-adaptive structure of SALS provides implementation simplicity and flexibility to the considered combinatorial optimization problems. Detailed computational studies confirm the performance of SALS on the suit of test problems for each considered problem type especially in terms of solution quality.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - JOUR
AU  - Cigdem Alabas-Uslu
AU  - Berna Dengiz
PY  - 2014
DA  - 2014/10/01
TI  - A Self-Adaptive Heuristic Algorithm for Combinatorial Optimization Problems
JO  - International Journal of Computational Intelligence Systems
SP  - 827
EP  - 852
VL  - 7
IS  - 5
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2014.966992
DO  - 10.1080/18756891.2014.966992
ID  - Alabas-Uslu2014
ER  -