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Dynamic small world particle swarm optimizer for function optimization

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Abstract

Performance of particle swarm optimization technique is highly influenced by the population topology. It determines the way in which particles communicate and share information within a swarm. If path length is too small, it implies that a particle communicates with other particles in its close proximity leading to exploitation. On the contrary, if path length is large then the particle interacts with other remote particles leading to exploration. There needs to be a balance between exploration and exploitation and Small world network fits to this need of ours. In this paper, dynamic small world network has been proposed with the objective to have a balanced trade-off between exploration and exploitation. In order to make learning process dynamic linearly decreasing inertia weight has been employed. Experimental study is performed on a set of 23 test functions using different performance evaluation measures. Results obtained are compared with other state of the art techniques demonstrating the effectiveness of the proposed approach.

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References

  • Alrashidi MR, El-Hawary ME (2006) A survey of particle swarm optimization applications in power system operations. Electr Power Compon Syst 34(12):1349–1357

    Article  Google Scholar 

  • Angeline PJ (1998) Using selection to improve particle swarm optimization. In: IEEE international conference on computational intelligence. IEEE, pp 84–89

  • Bergh FVD (2002) An analysis of particle swarm optimizer. Ph.D. Thesis, Department of Computer Science, University of Petoria, South Africa

  • Blackwell TM, Bentley P (2002) Don’t push me! collision-avoiding swarms. In: IEEE congress on evolutionary computation, vol 2. IEEE, pp 1691–1696

  • Blackwell TM, Branke J (2006) Multiswarms, exclusion, and anti convergence in dynamic environments. IEEE Trans Evol Comput 10(4):459–472

    Article  Google Scholar 

  • Chen YP, Peng WC, Jian MC (2007) Particle swarm optimization with recombination and dynamic linkage discovery. IEEE Trans Syst Man Cybern Part B 37(6):1460–1470

    Article  Google Scholar 

  • Clerc M (1999) The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. In: Proceedings of the congress on evolutionary computation. IEEE Service Center, Piscataway, pp 1951–1957

  • Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multi-dimensional complex space. IEEE Trans Evol Comput 6:58–73

    Article  Google Scholar 

  • Das S, Abraham A, Konar A (2008) Automatic kernel clustering with a multi-elitist particle swarm optimization algorithm. Pattern Recognit Lett 29:688–699

    Article  Google Scholar 

  • Eberhart RC, Shi Y (2004) Guest editorial special issue on particle swarm optimization. IEEE Trans Evol Comput 8(3):201–203

    Article  Google Scholar 

  • Eberhart RC, Shi Y, Kennedy J (2001) Swarm intelligence. Morgan Kaufmann, Los Altos

    Google Scholar 

  • Elsayed S, Sarker R, Essam D (2011) GA with a new multi-parent crossover for solving IEEE-CEC2011 competition problems. In: Congress on evolutionary computation (CEC). IEEE, pp 1034–1040

  • Franken N, Engelbrecht AP (2005) Particle swarm optimization approaches to coevolve strategies for the iterated prisoners dilemma. IEEE Trans Evol Comput 9(6):562–579

    Article  Google Scholar 

  • Granovetter MS (1973) The strenght of weak ties. Am J Sociol 78(6):1360–1380

    Article  Google Scholar 

  • Hu X, Eberhart RC (2002) Multiobjective optimization using dynamic neighborhood particle swarm optimization. In: IEEE congress on evolutionary computation, Honolulu, HI, pp 1677–1681

  • Janson S, Middendorf M (2005) A hierarchical particle swarm optimizer and its adaptive variant. IEEE Trans Syst Man Cybern Part B 35(6):1272–1282

    Article  Google Scholar 

  • Kennedy J (1999) Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. In: IEEE CEC, pp 1931–1938

  • Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: IEEE international conference on neural networks, vol 4, pp 1942–1948

  • Kennedy J, Mendes R (2002) Population structure and particle swarm performance. In: IEEE congress on evolutionary computation, vol 2. IEEE, Honolulu, Hawaii, pp 1671–1676

  • Kleinberg J (1999) The small-world phenomenon: an algorithmic perspective. Technical report, Cornell University Ithaca, NY, USA

  • Li X (2004) Adaptively choosing neighborhood best using species in a particle swarm optimizer for multimodal function optimization. In: Genetic evolutionary computation conference, pp 105–116

    Chapter  Google Scholar 

  • Li X (2010) Niching without niching parameters: particle swarm optimization using a ring topology. IEEE Trans Evol Comput 14(1):150–169

    Article  Google Scholar 

  • Li C, Yang S (2009) An adaptive learning particle swarm optimizer for function optimization. In: IEEE congress on evolutionary computation. IEEE, pp 381–388

  • Li C, Yang S, Nguyen T (2012) A self-learning particle swarm optimizer for global optimization problems. IEEE Trans Syst Man Cybern Part B 42(3):627–646

    Article  Google Scholar 

  • Liang JJ, Suganthan PN (2005) Dynamic multi-swarm particle swarm optimizer. In: Swarm intelligence symposium, pp 124–129

  • Liang JJ, Qin AK, Suganthan PN, Baska S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281–295

    Article  Google Scholar 

  • Liu B, Wang L, Jin YH (2007) An effective PSO-based memetic algorithm for flow shop scheduling. IEEE Trans Syst Man Cybern Part B Cybern 37(1):18–27

    Article  Google Scholar 

  • Mahmoodabadi M, Mottaghi ZS, Bagheri A (2014) HEPSO: high exploration particle swarm optimization. Inf Sci 273:101–111

    Article  MathSciNet  Google Scholar 

  • Mendes R (2004) Population topologies and their influence in particle swarm performance. Ph.D. Thesis, University of Minho

  • Mendes R, Kennedy J, Neves J (2004) The fully informed particle swarm: simpler, maybe better. IEEE Trans Evol Comput 8(3):204–210

    Article  Google Scholar 

  • Milgram S (1967) The small world problem. Psychol Today 2:60–67

    Google Scholar 

  • Park JB, Jeong YW, Shin JR, Lee KY (2010) An improved particle swarm optimization for nonconvex economic dispatch problems. IEEE Trans Power Syst 25(1):156–166

    Article  Google Scholar 

  • Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization: an overview. Swarm Intell 1(1):33–58

    Article  Google Scholar 

  • Ratnaweera A, Halgamuge SK, Watson HC (2004) Self organizing hierarchical particle swarm optimizer with time varying acceleration coefficients. IEEE Trans Evol Comput 8(3):240–255

    Article  Google Scholar 

  • Saxena AK, Vora M (2008) Novel approach for the use of small world theory in particle swarm optimization. In: \(16^{th}\) International conference on advanced computing and communications. IEEE, pp 363–366

  • Shi Y, Eberhart R (2001) Tracking and optimizing dynamic systems with particle swarms. In: IEEE congress on evolutionary computation. IEEE, Seoul Korea, pp 94–97

  • Shi Y, Eberhart RC (1998) A modified particle swarm optimizer. In: IEEE world congress on computational intelligence. IEEE, pp 69–73

  • Shi Y, Eberhart RC (2001) Fuzzy adaptive particle swarm optimization. In: IEEE world congress on computational intelligence. IEEE, pp 101–106

  • Suganthan PN (1999) Particle swarm optimizer with neighborhood operator. In: Congress on evolutionary computation, pp 1958–1962

  • Vora M, Mirnalinee TT (2013) Small world particle swarm optimizer for global optimization problems. In: Maji P, Ghosh A, Murty NM, Ghosh K, Pal SK (eds) Pattern recognition and machine intelligence, LNCS, vol 8251. Springer, Berlin, Heidelberg, pp 575–580

    Chapter  Google Scholar 

  • Vora M, Mirnalinee TT (2014) Small world particle swarm optimizer for data clustering. In: Pant M, Deep K, Nagar A, Bansal JC (eds) Third international conference on soft computing for problem solving, advances in intelligent systems and computing, vol 259. Springer, pp 403–410

  • Watts D, Strogatz S (1998) Collective dynamics of small-world networks. Nature 393:440–442

    Article  Google Scholar 

  • Xie X, Zhang W, Yang Z (2002) Dissipative particle swarm optimization. In: Congress on evolutionary computation. IEEE, pp 1456–1461

  • Xua X, Tang Y, Li J, Hua C, Guan X (2015) Dynamic multi-swarm particle swarm optimizer with cooperative learning strategy. Appl Soft Comput 29:169–183

    Article  Google Scholar 

  • Zambrano-Bigiarini M, Clerc M, Rojas R (2013) Standard particle swarm optimisation 2011 at CEC-2013: a baseline for future PSO improvements. In: IEEE congress on evolutionary computation (CEC’13), pp 2337–2344

  • Zhan Z, Zhang J, Li Y, Chung HS (2009) Adaptive particle swarm optimization. IEEE Trans Syst Man Cybern Part B 39(6):1362–1381

    Article  Google Scholar 

Download references

Acknowledgements

Authors would like to thank Prof. Changhe Li, for helping with SLPSO code. The authors are also thankful to the reviewers for their stimulating comments, which helped to improve the quality of the manuscript.

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Correspondence to Megha Vora.

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Vora, M., Mirnalinee, T.T. Dynamic small world particle swarm optimizer for function optimization. Nat Comput 17, 901–917 (2018). https://doi.org/10.1007/s11047-017-9639-9

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