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Search moves in the local optima networks of permutation spaces: the QAP case

Published:13 July 2019Publication History

ABSTRACT

In this work we analyze, from a qualitative point-of-view, the structure of the connections among the local optima in the fitness landscapes of the Quadratic Assignment Problem (QAP). In particular, we are interested in determining which search moves, intended as pairwise exchanges of permutation items, are beneficial for moving from one optimum to another. Novel algebraic methods are introduced for determining, and measuring the effectiveness, of the exchange moves connecting two given optima. The analysis considers real-like QAP instances whose local optima networks are clustered in communities. The results of the conducted experimentation shows the presence of few preferred search moves that look more effective for moving across intra-community optima, while the same is not so apparent when the optima are taken from different communities.

References

  1. Marco Baioletti, Alfredo Milani, and Valentino Santucci. 2015. Linear Ordering Optimization with a Combinatorial Differential Evolution. In Proc. of 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015. 2135--2140.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Marco Baioletti, Alfredo Milani, and Valentino Santucci. 2016. An Extension of Algebraic Differential Evolution for the Linear Ordering Problem with Cumulative Costs. In Proc. of 14th International Conference on Parallel Problem Solving from Nature - PPSN XIV. 123--133.Google ScholarGoogle ScholarCross RefCross Ref
  3. M. Baioletti, A. Milani, and V. Santucci. 2017. Algebraic Particle Swarm Optimization for the permutations search space. In Proc. of 2017 IEEE Congress on Evolutionary Computation (CEC 2017). 1587--1594.Google ScholarGoogle ScholarCross RefCross Ref
  4. M. Baioletti, A. Milani, and V. Santucci. 2018. Algebraic Crossover Operators for Permutations. In 2018 IEEE Congress on Evolutionary Computation (CEC 2018). 1--8.Google ScholarGoogle Scholar
  5. Marco Baioletti, Alfredo Milani, and Valentino Santucci. 2018. Automatic Algebraic Evolutionary Algorithms. In Proc. of Int. Workshop on Artificial Life and Evolutionary Computation (WIVACE 2017). Springer International Publishing, Cham, 271--283.Google ScholarGoogle ScholarCross RefCross Ref
  6. Marco Baioletti, Alfredo Milani, and Valentino Santucci. 2018. Learning Bayesian Networks with Algebraic Differential Evolution. In Proc. of 15th Int. Conf. on Parallel Problem Solving from Nature - PPSN XV. Springer International Publishing, Cham, 436--448.Google ScholarGoogle ScholarCross RefCross Ref
  7. Marco Baioletti, Alfredo Milani, and Valentino Santucci. 2018. MOEA/DEP: An Algebraic Decomposition-Based Evolutionary Algorithm for the Multiobjective Permutation Flowshop Scheduling Problem. In Proc. of European Conference on Evolutionary Computation in Combinatorial Optimization - EvoCOP 2018. Springer International Publishing, Cham, 132--145.Google ScholarGoogle ScholarCross RefCross Ref
  8. Marco Baioletti and Valentino Santucci. 2017. Fitness Landscape Analysis of the Permutation Flowshop Scheduling Problem with Total Flow Time Criterion. In Computational Science and Its Applications - ICCSA 2017. Springer International Publishing, Cham, 705--716.Google ScholarGoogle ScholarCross RefCross Ref
  9. Albert-László Barabási et al. 2016. Network science. Cambridge university press.Google ScholarGoogle Scholar
  10. Kenneth D Boese, Andrew B Kahng, and Sudhakar Muddu. 1994. A new adaptive multi-start technique for combinatorial global optimizations. Operations Research Letters 16, 2 (1994), 101--113. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Lidia Ceriani and Paolo Verme. 2012. The origins of the Gini index: extracts from Variabilità e Mutabilità (1912) by Corrado Gini. The Journal of Economic Inequality 10, 3 (01 Sep 2012), 421--443.Google ScholarGoogle ScholarCross RefCross Ref
  12. Gabor Csardi and Tamas Nepusz. 2006. The igraph software package for complex network research. Inter Journal Complex Systems (2006), 1695. http://igraph.orgGoogle ScholarGoogle Scholar
  13. Fabio Daolio, Marco Tomassini, Sébastien Vérel, and Gabriela Ochoa. 2011. Communities of minima in local optima networks of combinatorial spaces. Physica A: Statistical Mechanics and its Applications 390, 9 (2011), 1684--1694.Google ScholarGoogle Scholar
  14. J Garnier and L Kallel. 2001. How to detect all maxima of a function. In Theoretical aspects of evolutionary computing. Springer, 343--370. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Sebastian Herrmann, Gabriela Ochoa, and Franz Rothlauf. 2016. Communities of local optima as funnels in fitness landscapes. In Proceedings of the Genetic and Evolutionary Computation Conference 2016. ACM, 325--331. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. S. A. Kauffman. 1993. The Origins of Order. Oxford University Press, New York.Google ScholarGoogle Scholar
  17. J. Knowles and D. Corne. 2003. Instance Generators and Test Suites for the Multiobjective Quadratic Assignment Problem. In Proceedings of the Evolutionary Multi-Criterion Optimization Conference (EMO 2003) (LNCS). Springer, 295--310. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. T. C. Koopmans and M. Beckmann. 1957. Assignment Problems and the Location of Economic Activities. Econometrica 25, 1 (1957), 53--76.Google ScholarGoogle ScholarCross RefCross Ref
  19. Serge Lang. 2002. Algebra. Vol. 211. Springer.Google ScholarGoogle Scholar
  20. Paolo Mengoni, Alfredo Milani, and Yuanxi Li. 2018. Community graph elicitation from students' interactions in virtual learning environments. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 10962 LNCS. 414--425.Google ScholarGoogle Scholar
  21. Gabriela Ochoa, Marco Tomassini, Sebástien Verel, and Christian Darabos. 2008. A Study of NK Landscapes' Basins and Local Optima Networks. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2008. ACM, 555--562. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Gabriela Ochoa and Nadarajen Veerapen. 2016. Deconstructing the big valley search space hypothesis. In Evolutionary Computation in Combinatorial Optimization. Springer, 58--73.Google ScholarGoogle Scholar
  23. G. Ochoa, S. Verel, and M. Tomassini. 2010. First-Improvement vs. Best-Improvement Local Optima Networks of NK Landscapes. In Parallel Problem Solving from Nature - PPSN XI (Lecture Notes in Computer Science), Vol. 6238. Springer, 104--113. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. C.M. Reidys and P.F. Stadler. 2002. Combinatorial landscapes. SIAM review 44, 1 (2002), 3--54. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Valentino Santucci, Marco Baioletti, Gabriele Di Bari, and Alfredo Milani. 2019. A Binary Algebraic Differential Evolution for the MultiDimensional Two-Way Number Partitioning Problem. In Evolutionary Computation in Combinatorial Optimization. Springer International Publishing, Cham, 17--32.Google ScholarGoogle Scholar
  26. Valentino Santucci, Marco Baioletti, and Alfredo Milani. 2014. A Differential Evolution Algorithm for the Permutation Flowshop Scheduling Problem with Total Flow Time Criterion. In Proc. of 13th International Conference on Parallel Problem Solving from Nature - PPSN XIII. Springer, Cham, 161--170.Google ScholarGoogle ScholarCross RefCross Ref
  27. V. Santucci, M. Baioletti, and A. Milani. 2016. Algebraic Differential Evolution Algorithm for the Permutation Flowshop Scheduling Problem With Total Flowtime Criterion. IEEE Transactions on Evolutionary Computation 20, 5 (2016), 682--694.Google ScholarGoogle ScholarCross RefCross Ref
  28. V. Santucci, M. Baioletti, and A. Milani. 2016. Solving permutation flowshop scheduling problems with a discrete differential evolution algorithm. AI Communications 29, 2 (2016), 269--286.Google ScholarGoogle ScholarCross RefCross Ref
  29. Valentino Santucci and Alfredo Milani. 2011. Particle Swarm Optimization in the EDAs Framework. In Soft Computing in Industrial Applications. Springer Berlin Heidelberg, Berlin, Heidelberg, 87--96.Google ScholarGoogle Scholar
  30. Tommaso Schiavinotto and Thomas Stützle. 2007. A review of metrics on permutations for search landscape analysis. Computers & Operations Research 34, 10 (2007), 3143--3153. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. É, D. Taillard. 1995. Comparison of iterative searches for the quadratic assignment problem. Location Science 3, 2 (1995), 87 -- 105.Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Marco Tomassini, Sébastien Verel, and Gabriela Ochoa. 2008. Complex-Network Analysis of combinatorial spaces: The NK landscape case. Phys. Rev. E 78, 6 (2008), 066114.Google ScholarGoogle ScholarCross RefCross Ref
  33. S. Verel, F. Daolio, G. Ochoa, and M. Tomassini. 2012. Local Optima Networks with Escape Edges. In Proceedings of the International Conference on Artificial Evolution, EA-2011 (Lecture Notes in Computer Science), Vol. 7401. Springer, 49--60. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. S. Verel, G. Ochoa, and M. Tomassini. 2011. Local Optima Networks of NK Landscapes with Neutrality. IEEE Transactions on Evolutionary Computation 15, 6(2011), 783--797.Google ScholarGoogle ScholarCross RefCross Ref

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        cover image ACM Conferences
        GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion
        July 2019
        2161 pages
        ISBN:9781450367486
        DOI:10.1145/3319619

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        • Published: 13 July 2019

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