Testing the permutation space based geometric differential evolution on the job-shop scheduling problem

Authors:
Antonin Ponsich;Carlos A. Coello Coello
Affiliations:
Universidad Autónoma Metropolitana, Unidad Azcapotzalco, Dpto. de Sistemas, México, D.F.;CINVESTAV-IPN, Departamento de Computación, México, D.F.
Venue:
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Year:
2010

Citing 12
Cited 0

The shifting bottleneck procedure for job shop scheduling

Management Science
Job shop scheduling by simulated annealing

Operations Research
A genetic algorithm for the job shop problem

Computers and Operations Research - Special issue on genetic algorithms
A fast taboo search algorithm for the job shop problem

Management Science
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

Journal of Global Optimization
Parallel GRASP with path-relinking for job shop scheduling

Parallel Computing - Special issue: Parallel computing in numerical optimization
An Advanced Tabu Search Algorithm for the Job Shop Problem

Journal of Scheduling
A very fast TS/SA algorithm for the job shop scheduling problem

Computers and Operations Research
Ant colony optimization combined with taboo search for the job shop scheduling problem

Computers and Operations Research
Geometric differential evolution

Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Solving Permutation Problems with Differential Evolution: An Application to the Jobshop Scheduling Problem

ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
Geometric particle swarm optimisation

EuroGP'07 Proceedings of the 10th European conference on Genetic programming

Quantified Score

Hi-index	0.00

Visualization

Abstract

From within the variety of research that has been devoted to the adaptation of Differential Evolution to the solution of problems dealing with permutation variables, the Geometric Differential Evolution algorithm appears to be a very promising strategy. This approach is based on a geometric interpretation of the evolutionary operators and has been specifically proposed for combinatorial optimization. Such an approach is adopted in this paper, in order to evaluate its efficiency on a challenging class of combinatorial optimization problems: the Job-Shop Scheduling Problem. This algorithm is implemented and tested on a selection of instances normally adopted in the specialized literature. The results obtained by this approach are compared with respect to those generated by a classical DE implementation (using Random Keys encoding for the decision variables). Our computational experiments reveal that, although Geometric Differential Evolution performs (globally) as well as classical DE, it is not really able to significantly improve its performance.