Improving Tabu Search Performance by Means of Automatic Parameter Tuning

Lagos C.; Crawford B.; Soto R.; Cabrera E.; Vega J.; Johnson F.; Paredes F.

dc.contributor.author	Lagos C.
dc.contributor.author	Crawford B.
dc.contributor.author	Soto R.
dc.contributor.author	Cabrera E.
dc.contributor.author	Vega J.
dc.contributor.author	Johnson F.
dc.contributor.author	Paredes F.
dc.date.accessioned	2020-09-02T22:21:11Z
dc.date.available	2020-09-02T22:21:11Z
dc.date.issued	2016
dc.identifier	10.1109/CJECE.2015.2496338
dc.identifier.citation	39, 1, 51-58
dc.identifier.issn	08408688
dc.identifier.uri	https://hdl.handle.net/20.500.12728/5025
dc.description	A common problem when performing (meta)heuristic techniques over complex combinatorial optimization problems is parameter tuning. Finding the right parameter values can lead to significant improvements in terms of the best solution objective value found by the heuristic, heuristic reliability, and heuristic convergence, among others. Unfortunately, this is usually a tedious and complicated task if done manually. Furthermore, parameter values usually depend on the problem that is going to be solved. In this paper, we propose a framework that is based on the genetic programming (GP) technique to fine tune a key parameter of the well-known tabu search (TS) algorithm. Several experiments are performed over a set of small instances of the well-known capacitated facility location problem. The results have shown that adjusting the probability of acceptance of the best neighbor ρ in the TS algorithm using GP leads to an average value of the obtained solution that is closer to the optimal solution than the average value obtained by the simple TS algorithm with an a priori selected value for ρ. More importantly, standard deviation of the algorithm is greatly improved by our approach, which makes it much more reliable if time limitations are present. Finally, we confirm that the value of the parameter ρ largely depends on the problem that is attempted to solve. © 2016 IEEE.
dc.language.iso	en
dc.publisher	IEEE Canada
dc.subject	Automatic parameter tuning
dc.subject	combinatorial optimization
dc.subject	genetic programming (GP)
dc.subject	tabu search (TS).
dc.subject	Algorithms
dc.subject	Combinatorial optimization
dc.subject	Genetic algorithms
dc.subject	Genetic programming
dc.subject	Heuristic methods
dc.subject	Tabu search
dc.subject	Automatic parameter tuning
dc.subject	Capacitated facility location problems
dc.subject	Combinatorial optimization problems
dc.subject	Heuristic techniques
dc.subject	Optimal solutions
dc.subject	Search performance
dc.subject	Standard deviation
dc.subject	Tabu search algorithms
dc.subject	Optimization
dc.title	Improving Tabu Search Performance by Means of Automatic Parameter Tuning
dc.type	Article