Scatter Search vs. Genetic Algorithms

An Experimental Evaluation with Permutation Problems
  • Rafael Martí
  • Manuel Laguna
  • Vicente Campos
Part of the Operations Research/Computer Science Interfaces Series book series (ORCS, volume 30)


The purpose of this work is to compare the performance of a scatter search (SS) implementation and an implementation of a genetic algorithm (GA) in the context of searching for optimal solutions to permutation problems. Scatter search and genetic algorithms are members of the evolutionary computation family. That is, they are both based on maintaining a population of solutions for the purpose of generating new trial solutions. Our computational experiments with four well-known permutation problems reveal that in general a GA with local search outperforms one without it. Using the same problem instances, we observed that our specific scatter search implementation found solutions of a higher average quality earlier during the search than the GA variants.


Scatter Search Genetic Algorithms Combinatorial Optimization Permutation Problems 


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  1. Campos, V.,M. Laguna and R. Martí (1999) “Scatter Search for the Linear Ordering Problem,” Corne, Dorigo and Glover (Eds.) New Ideas in Optimization, McGraw-Hill, UK.Google Scholar
  2. Campos, V., F. Glover, M. Laguna and R. Martí (2001) “An Experimental Evaluation of a Scatter Search for the Linear Ordering Problem,” Journal of Global Optimization, 21:397–414.CrossRefzbMATHGoogle Scholar
  3. Chanas, S. and P. Kobylanski (1996) “A New Heuristic Algorithm Solving the Linear Ordering Problem,” Computational Optimization and Applications, 6:191–205.MathSciNetzbMATHGoogle Scholar
  4. Dueck, G.H. and J. Jeffs (1995) “A Heuristic Bandwidth Reduction Algorithm,” J. of Combinatorial Math. And Comp., 18:97–108.MathSciNetzbMATHGoogle Scholar
  5. Everett, H. (1963) “Generalized Lagrangean Multiplier Method for Solving Problems of Optimal Allocation of Resources,” Operations Research, 11:399–417.zbMATHMathSciNetGoogle Scholar
  6. Glover, F. (1965) “A Multiphase-Dual Algorithm for the Zero-One Integer Programming Problem,” Operations Research, 13:879–919.zbMATHMathSciNetGoogle Scholar
  7. Glover, F. (1977) “Heuristics for Integer Programming Using Surrogate Constraints,” Decision Sciences, 8(7):156–166.Google Scholar
  8. Glover, F. (1994a) “Genetic Algorithms and Scatter Search: Unsuspected Potentials,” Statistics and Computing, 4:131–140.CrossRefGoogle Scholar
  9. Glover, F. (1994b) “Tabu Search for Nonlinear and Parametric Optimization with Links to Genetic Algorithms,” Discrete Applied Mathematics, 49:231–255.CrossRefzbMATHMathSciNetGoogle Scholar
  10. Glover, F. (1995) “Scatter Search and Star-Paths: Beyond the Genetic Metaphor,” OR Spektrum, 17(2–3):125–138.CrossRefzbMATHGoogle Scholar
  11. Glover, F. (1998) “A Template for Scatter Search and Path Relinking,” in Artificial Evolution, Lecture Notes in Computer Science 1363, J.K. Hao, E. Lutton, E. Ronald, M. Schoenauer and D. Snyers (Eds.), Springer, 13–54.Google Scholar
  12. Grötschel, M., M. Jünger and G. Reinelt (1984), “A Cutting Plane Algorithm for the Linear Ordering Problem,” Operations Research, 32(6):1195–1220.MathSciNetCrossRefzbMATHGoogle Scholar
  13. Holland, J.H. (1975) Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, MI.Google Scholar
  14. Laguna, M., J.W. Barnes and F. Glover (1993), “Intelligent Scheduling with Tabu Search: An Application to Jobs With Linear Delay Penalties and Sequence-Dependent Setup Costs and Times,” Journal of Applied Intelligence, 3:159–172.CrossRefGoogle Scholar
  15. Laguna, M. and R. Martí (2000) “Experimental Testing of Advanced Scatter Search Designs for Global Optimization of Multimodal Functions,” Technical Report TR11-2000, Dpto de Estadística e I.O., University of Valencia.Google Scholar
  16. Laguna, M., R. Martí and V. Campos (1999) “Intensification and Diversification with Elite Tabu Search Solutions for the Linear Ordering Problem,” Computers and Operations Research, 26:1217–1230.CrossRefzbMATHGoogle Scholar
  17. Lawler, L., R. Kan and Shmoys (1985) The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization, John Wiley and Sons.Google Scholar
  18. Martí, R., M. Laguna, F. Glover and V. Campos (2001) “Reducing the Bandwidth of a Sparse Matrix with Tabu Search,” European Journal of Operational Research, 135:450–459.CrossRefMathSciNetzbMATHGoogle Scholar
  19. Michalewicz, Z. (1996) Genetic Algorithms + Data Structures = Evolution Programs, 3rd edition, Springer-Verlag, Berlin.zbMATHGoogle Scholar
  20. Reinelt, G. (1985) “The Linear Ordering Problem: Algorithm and Applications,” Research and Exposition in Mathematics, 8, H.H. Hofman and R. Wille (eds.), Heldermann Verlag, Berlin.Google Scholar
  21. Reinelt, G. (1994) “The Traveling Salesman: Computational Solutions for TSP applications,” Lecture Notes in Computer Science, Springer Verlag, Berlin.Google Scholar

Copyright information

© Kluwer Academic Publishers 2005

Authors and Affiliations

  • Rafael Martí
    • 1
  • Manuel Laguna
    • 2
  • Vicente Campos
    • 1
  1. 1.Departamento de Estadística e 1.0., Facultad de MatemdticasUniversitat de ValenciaBurjassot ValenciaSpain
  2. 2.Leads School of BusinessUniversity of ColoradoBoulderUSA

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