Evaluation of the exIWO Algorithm Based On the Traveling Salesman Problem
Authors: Daniel Kostrzewa, Henryk Josiński
Abstract:
The expanded Invasive Weed Optimization algorithm (exIWO) is an optimization metaheuristic modelled on the original IWO version created by the researchers from the University of Tehran. The authors of the present paper have extended the exIWO algorithm introducing a set of both deterministic and non-deterministic strategies of individuals’ selection. The goal of the project was to evaluate the exIWO by testing its usefulness for solving some test instances of the traveling salesman problem (TSP) taken from the TSPLIB collection which allows comparing the experimental results with optimal values.
Keywords: Expanded Invasive Weed Optimization algorithm (exIWO), Traveling Salesman Problem (TSP), heuristic approach, inversion operator.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1094667
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2256References:
[1] R. Mehrabian, C. Lucas, "A novel numerical optimization algorithm inspired from weed colonization”, Ecological Informatics, Vol. 1, Issue 4, 2006.
[2] A. R. Mallahzadeh, H. Oraizi, Z. Davoodi-Rad, "Application of the Invasive Weed Optimi¬za¬tion Technique for Antenna Configurations”, Progress in Electromagnetics Research, 2008.
[3] M. Sahraei-Ardakani, M. Roshanaei, A. Rahimi-Kian, C. Lucas, "A Study of Electricity Market Dynamics Using Invasive Weed Colonization Optimization”, IEEE Symposium on Computational Intelligence and Games, 2008.
[4] H. Sepehri Rad, C. Lucas, "A Recommender System based on Invasive Weed Optimization Algorithm”, IEEE Congress on Evolutionary Computation, Singapore, 2007.
[5] D. Kostrzewa, H. Josiński, "Verification of the Search Space Exploration Strategy Based on the Solutions of the Join Ordering Problem”, Advances in Intelligent and Soft Computing, Springer, 2011.
[6] H. Josiński, D. Kostrzewa, A. Michalczuk, A. Świtoński, "The Expanded Invasive Weed Optimization Metaheuristic for Solving Continuous and Discrete Optimization Problems”, The Scientific World Journal, Vol. 2014, Article ID 831691, 14 pages, doi:10.1155/2014/831691, Hindawi Publishing Corporation, 2014.
[7] G. Reinelt, "TSPLIB – traveling salesman problem library”, ORSA Journal on Computing 3(4), 1991.
[8] Z. Michalewicz, D. B. Fogel, How to Solve It: Modern Heuristics. Springer, 2004.
[9] G. Tao, Z. Michalewicz, "Inver-over Operator for the TSP”, Lecture Notes In Computer Science, Vol. 1498, pp. 803-812, Springer, 1998.
[10] O. Kramer, Self-Adaptive Heuristics for Evolutionary Computation. Springer, 2008.
[11] TSP library Ruprecht-Karls-Universität, Heidelberg, Germany, www.iwr.uni-heidelberg.de/groups/comopt/software/TSPLIB95/tsp.