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A Hybrid Algorithm Based on Particle Swarm Optimization and Ant Colony Optimization Algorithm

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Smart Computing and Communication (SmartCom 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10135))

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Abstract

Particle swarm optimization (PSO) and Ant Colony Optimization (ACO) are two important methods of stochastic global optimization. PSO has fast global search capability with fast initial speed. But when it is close to the optimal solution, its convergence speed is slow and easy to fall into the local optimal solution. ACO can converge to the optimal path through the accumulation and update of the information with the distributed parallel global search ability. But it has slow solving speed for the lack of initial pheromone at the beginning. In this paper, the hybrid algorithm is proposed in order to use the advantages of both of the two algorithm. PSO is first used to search the global solution. When it maybe fall in local one, ACO is used to complete the search for the optimal solution according to the specific conditions. The experimental results show that the hybrid algorithm has achieved the design target with fast and accurate search.

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References

  1. Amudhavel, J., Kumar, K.P., Monica, A., Bhuvaneshwari, B., Jaiganesh, S., Kumar, S.S.: A hybrid ACO-PSO based clustering protocol in VANET. In: The 2015 International Conference on Advanced Research in Computer Science Engineering & Technology. ACM Press, New York (2015). Articles 25

    Google Scholar 

  2. Lam, H.T., Nicolaevna, P.N., Quan, N.T.M.: A heuristic particle swarm optimization. In: The 9th Annual Conference on Genetic and Evolutionary Computation, p. 174. ACM Press, New York (2007)

    Google Scholar 

  3. Snyman, J.A., Kok, S.: A strongly interacting dynamic particle swarm optimizational method. In: The 9th Annual Conference on Genetic and Evolutionary Computation, p. 183. ACM Press, New York (2007)

    Google Scholar 

  4. Wu, C., Zhang, C., Wang, C.: Topology optimization of structures using ant colony optimization. In: The First ACM/SIGEVO Summit on Genetic and Evolutionary Computation, pp. 601–608. ACM Press, New York (2009)

    Google Scholar 

  5. Chen, Y., Wong, M.L.: Optimizing stacking ensemble by an ant colony optimization approach. In: The 13th Annual Conference Companion on Genetic and Evolutionary Computation, pp. 7–8. ACM Press, New York (2011)

    Google Scholar 

  6. Al-Rifaie, M.M., Bishop, M.J., Blackwell, T.: An investigation into the merger of stochastic diffusion search and particle swarm optimization. In: The 13th Annual Conference on Genetic and Evolutionary Computation, pp. 37–44. ACM Press, New York (2011)

    Google Scholar 

  7. Khosla, A.: Particle swarm optimization for fuzzy models. In: The 9th Annual Conference Companion on Genetic and Evolutionary Computation, pp. 3283–3296. ACM Press, New York (2007)

    Google Scholar 

  8. Sinnott-Armstrong, N.A., Greene, C.S., Moore, J.H.: Fast genome-wide epistasis analysis using ant colony optimization for multifactor dimensionality reduction analysis on graphics processing units. In: The 12th Annual Conference on Genetic and Evolutionary Computation, pp. 215–216. ACM Press, New York (2010)

    Google Scholar 

  9. Cao, S., Qin, Y., Liu, J., Lu, R.: An ACO-Based user community preference clustering system for customized content service in broadband new media platforms. In: The 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, pp. 591–595. IEEE Press, Washington, DC (2008)

    Google Scholar 

  10. Rajini, A., David, V.K.: Swarm optimization and Flexible Neural Tree for microarray data classification. In: The Second International Conference on Computational Science, Engineering and Information Technology, pp. 261–268. ACM Press, New York (2012)

    Google Scholar 

  11. Chen, S., Montgomery, J.: A simple strategy to maintain diversity and reduce crowding in particle swarm optimization. In: The 13th Annual Conference Companion on Genetic and Evolutionary Computation, pp. 811–812. ACM Press, New York (2011)

    Google Scholar 

  12. Ugolotti, R., Cagnoni, S.: Automatic tuning of standard PSO versions. In: The Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 1501–1502. ACM Press, New York (2015)

    Google Scholar 

  13. Abdelbar, A.M.: Is there a computational advantage to representing evaporation rate in ant colony optimization as a gaussian random variable? In: The 14th Annual Conference on Genetic and Evolutionary Computation, pp. 1–8. ACM Press, New York (2012)

    Google Scholar 

  14. Chira, C., Pintea,C.M., Crisan, G.C., Dumitrescu, D.: Solving the linear ordering problem using ant models. In: The 11th Annual Conference on Genetic and Evolutionary Computation, pp. 1803–1804. ACM Press, New York (2009)

    Google Scholar 

  15. Hemmatiana, H., Fereidoona, A., Sadollahb, A., Bahreininejad, A.: Optimization of laminate stacking sequence for minimizing weight and cost using elitist ant system optimization. Adv. Eng. Softw. 57, 8–18 (2013)

    Article  Google Scholar 

  16. Wang, G., Gong, W., Kastner, R.: Instruction scheduling using MAX-MIN ant system optimization. In: The 15th ACM Great Lakes symposium on VLSI, pp. 44–49. ACM Press, New York (2005)

    Google Scholar 

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Correspondence to Wei Hu .

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Lu, J., Hu, W., Wang, Y., Li, L., Ke, P., Zhang, K. (2017). A Hybrid Algorithm Based on Particle Swarm Optimization and Ant Colony Optimization Algorithm. In: Qiu, M. (eds) Smart Computing and Communication. SmartCom 2016. Lecture Notes in Computer Science(), vol 10135. Springer, Cham. https://doi.org/10.1007/978-3-319-52015-5_3

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  • DOI: https://doi.org/10.1007/978-3-319-52015-5_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-52014-8

  • Online ISBN: 978-3-319-52015-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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