Towards Finding an Effective Way of Discrete Problems Solving: The Particle Swarm Optimization, Genetic Algorithm and Linkage Learning Techniques Hybrydization

Bartosz Andrzej Fidrysiak, Michal Przewozniczek

Abstract

Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) are well known optimization tools. PSO advantage is its capability for fast convergence to the promising solutions. On the other hand GAs are able to process schemata thanks to the use of crossover operator. However, both methods have also their drawbacks – PSO may fall into the trap of preconvergence, while GA capability of fast finding locally optimal (or close to optimal) solutions seems low when compared to PSO. Relatively new, important research direction in the field of Evolutionary Algorithms is linkage learning. The linkage learning methods gather the information about possible gene dependencies and use it to improve their effectiveness. Recently, the linkage learning evolutionary methods were shown to be effective tools to solve both: theoretical and practical problems. Therefore, this paper proposes a PSO and GA hybrid, improved by the linkage learning mechanisms, dedicated to solve binary problems. The proposed method tries to combine the GA schema processing ability, linkage information processing and uses fast PSO convergence to quickly improve the quality of already known solutions.

References

  1. Andrade, C., Toso, R., Resende, M., Miyazawa, F., 2015, Biased Random-Key Genetic Algorithms for the Winner Determination Problem in Combinatorial Auctions, In Evolutionary Computation, Vol. 23, No. 2: 279-307.
  2. Baek,H., Ryu, J., Oh, J., Kim T., 2015, Optimal design of multi-storage network for combined sewer overflow management using a diversity-guided, cyclicnetworking particle swarm optimizer - A case study in the Gunja subcatchment area, Korea, In Expert Systems with Applications, Vol. 42, Issue 20, pp. 6966-6975.
  3. Bergh F., 2010, An Analysis of Particle Swarm Optimization. In Computer and Information Science, Vol.3, no. 1, pp.180-184.
  4. Cai, Y, Wang, Y., 2015, Differential evolution with hybrid linkage crossover. In Information Sciences, Vol. 237, pp. 244-287.
  5. Chang, W.D., 2015, A modified particle swarm optimization with multiple subpopulations for multimodal function optimization problems. In Applied Soft Computing, Vol. 33, pp. 170-182.
  6. Chen, Y., Peng. W, Jian M., 2007, Particle Swarm Optimization With Recombination and Dynamic Linkage Discovery, In IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, Vol.37, Issue 6, pp.1460-1470.
  7. Chen, Y., Sastry, K., Goldberg, D.E., 2007b, A Survey of Linkage Learning Techniques in Genetic and Evolutionary Algorithms, In IlliGAL Report No. 2007014, Illinois Genetic Algorithms Laboratory.
  8. Correa, E.S., Shapiro, J.L., 2006, Model Complexity vs. Performance in the Bayesian Optimization Algorithm, In Lecture Notes in Computer Science , Vol. 4193, pp. 998-1007.
  9. Dahzi, W.., Wu, CH., Ip, W.H., Wang, D., Yan, Y., 2008, Parallel multi-population Particle Swarm Optimization Algorithm for the Uncapacitated Facility Location problem using OpenMP. In IEEE Congress on Evolutionary Computation.
  10. Deb, K., Goldberg, D. E., 1992, Sufficient Conditions for Deceptive and Easy Binary Functions, In Annals of Mathematics and Artificial Intelligence, Vol. 10, pp. 385-408.
  11. Devicharan, D., Mohan, C.K., 2004, Particle Swarm Optimization with Adaptive Linkage Learning, In Congress on Evolutionary Computation, Vol.1, pp.530-535.
  12. Eberhart, R. & Kennedy, J., 1995. A New Optimizer Using Particle Swarm Theory. In Proceeding of, 6th International Symposium on Micro Machine and Human Science, pp.530-535.
  13. Fan, K., Yu, T. Lee, J., 2013, Linkage learning by number of function evaluations estimation: practical view of building blocks. In Information Sciences, Vol. 230, Issue 1, pp. 162-182.
  14. Goldberg, D.E., Deb, K., Kargupta H., Harik, G., 1993, Rapid, accurate optimization of difficult problems using fast messy genetic algorithms, In Proceedings of 5th International Conference on Genetic Algorithms.
  15. Kennedy, J. & Eberhart, R., 1997, A Discrete Binary Version of the Particle Swarm Algorithm. In IEEE International Conference on Systems, Man and Cybernetics, Computational Cybernetics and Simulation, Vol.5, pp.4104-4108.
  16. Kennedy, J., Mendes, R., 2006, Neighborhood Topologies in Fully-InformedandBest-Of-Neighborhood ParticleSwarms, In IEEE Transactions on Systems,Man,and Cybernetics, PartC: Applications and Reviews, Vol. 36, Issue 4, pp.515-519.
  17. Khanesar, M. A, Teshnehlab, M. & Shoorehdeli, M.A., 2007, A Novel Binary Particle Swarm Optimization, In Proceedings of the 15th Mediterranean Conference on Control&Automation, pp.1-6.
  18. Kwasnicka, H., Przewozniczek, 2011, M., Multi Population Pattern Searching Algorithm: a new evolutionary method based on the idea of messy Genetic Algorithm, In IEEE Transactions on Evolutionary Computation, Vol. 15 Issue 5, pp.715- 734.
  19. Laumanns, M., Ocenasek, J., 2002, Bayesian Optimization Algorithms for multi-objective optimization, In Lecture Notes in Computer Science , Vol. 2439, pp. 298-307.
  20. Lim, W.H., Isa, N., 2014, Bidirectional teaching and peerlearning particle swarm optimization, In Information Sciences, Vol. 280, pp. 111-134.
  21. Liu, Q., 2015, Order-2 Stability Analysis of Particle Swarm Optimization, In Evolutionary Computation, Vol. 23, No. 2, pp. 187-216.
  22. Lovbjerg, M., Rasmussen, T. K., Krink, T., 2001, Hybrid Particle Swarm Optimiser with Breeding and Subpopulations, In Proceedings of the Genetic and Evolutionary Computation Conference, Vol.24, pp.469-476.
  23. Moubayed, N., Petrovski, A., McCall, J., 2014, D2MOPSO: MOPSO Based on Decomposition and Dominance with Archiving Using Crowding Distance in Objective and Solution Spaces, In Evolutionary Computation, Vol. 22, No. 1, pp. 47-77.
  24. Mu, A.Q., Cao, D.X., Wang, X.H., 2009, A Modified Particle Swarm Optimization Algorithm, In Natural Science, Vol.1, No. 2, pp. 151-155.
  25. Niu, B., Zhu, Y., He, X., Wu, H., 2007, MCPSO: A multiswarm cooperative particle swarm optimizer, In Applied Mathematics and Computation, Vol. 185, pp. 1050-1062.
  26. Pelikan, M., Sastry, K., Butz, M.V., Goldberg, D.E., 2006, Hierarchical BOA on Random Decomposable Problems, In MEDAL Report No. 2006001.
  27. Przewozniczek, M., Goscien, R., Walkowiak, K., Klinkowski, M., 2015, Towards Solving Practical Problems of Large Solution Space Using a Novel Pattern Searching Hybrid Evolutionary Algorithm - An Elastic Optical Network Optimization Case Study, In Expert Systems with Applications, Vol. 42, pp. 7781-7796.
  28. Rani K., Vikas K., 2014, Solving Travelling Salesman Problem Using Genetic Algorithm Based On Heuristic Crossover And Mutation Operator, In International Journal of Research in Engineering & Technology, Vol. 2, Issue 2, pp. 27-34.
  29. Thierens, D., 1999, Scalability problems of simple genetic algorithms, In Evolutionary Computation, Vol. 7, Issue 4, pp. 331-352.
  30. Walkowiak, K., Przewozniczek, M., Pajak, K., 2013, Heuristic Algorithms for Survivable P2P Multicasting, In Applied Artificial Intelligence, Vol. 27, Issue 4, pp. 278-303.
  31. Watson, R.A., Hornby, G.S., Pollack, J.B., 1998, Hierarchical Building-Block Problems for GA Evaluation, In Parallel problem solving from nature , pp. 97-106.
  32. Xu, L., Wang, J., Li, Y, Li, Q., Zhang, X., 2015, Resource allocation algorithm based on hybrid particle swarm optimization for multiuser cognitive OFDM network, In Expert Systems with Applications, Vol. 42, Issue 20, pp. 7186-7194.
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Paper Citation


in Harvard Style

Andrzej Fidrysiak B. and Przewozniczek M. (2015). Towards Finding an Effective Way of Discrete Problems Solving: The Particle Swarm Optimization, Genetic Algorithm and Linkage Learning Techniques Hybrydization . In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA, ISBN 978-989-758-157-1, pages 228-236. DOI: 10.5220/0005596602280236


in Bibtex Style

@conference{ecta15,
author={Bartosz Andrzej Fidrysiak and Michal Przewozniczek},
title={Towards Finding an Effective Way of Discrete Problems Solving: The Particle Swarm Optimization, Genetic Algorithm and Linkage Learning Techniques Hybrydization},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,},
year={2015},
pages={228-236},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005596602280236},
isbn={978-989-758-157-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,
TI - Towards Finding an Effective Way of Discrete Problems Solving: The Particle Swarm Optimization, Genetic Algorithm and Linkage Learning Techniques Hybrydization
SN - 978-989-758-157-1
AU - Andrzej Fidrysiak B.
AU - Przewozniczek M.
PY - 2015
SP - 228
EP - 236
DO - 10.5220/0005596602280236