Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Ecosystem particle swarm optimization

  • Foundations
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Particle swarm optimization (PSO) is a well-known swarm intelligence algorithm inspired by the foraging behavior of bird flocking. PSO has been widely used in many optimization and engineering problems due to its simplicity and efficiency, even though there still exist some disadvantages. The standard PSO often suffers with premature convergence or slow convergence when the optimization problem is multimodal or high-dimensional. To overcome these drawbacks, an ecosystem PSO (ESPSO) inspired by the characteristic that a natural ecosystem can excellently keep the biological diversity and make the whole ecosystem be in a dynamic balance is presented in this paper. ESPSO not only prevents the algorithm trapping into local optima but also balances the exploration and exploitation in both unimodal and multimodal problems as compared to other PSO variants. Twenty benchmark functions including unimodal functions and multimodal nonlinear functions are used to test the searching ability of ESPSO. Experimental results show that ESPSO considerably improves the searching accuracy, the algorithm reliability and the searching efficiency in comparison with other six well-known PSO variants and four evolutionary algorithms. Moreover, ESPSO was successfully applied to the antenna array pattern synthesis design and gained satisfactory results.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Beheshti Z, Shamsuddin SMH (2014) CAPSO: centripetal accelerated particle swarm optimization. Inf Sci 258:54–79

  • Beheshti Z, Shamsuddin SM (2015) Non-parametric particle swarm optimization for global optimization. Appl Soft Comput 28:345–359

    Article  Google Scholar 

  • Chatterjee S, Goswami D, Mukherjee S, Das S (2014) Behavioral analysis of the leader particle during stagnation in a particle swarm optimization algorithm. Inf Sci 279:18–36

    Article  MathSciNet  MATH  Google Scholar 

  • Chen D, Zou F, Wang J, Yuan W (2015) A teaching–learning-based optimization algorithm with producer scrounger model for global optimization. Soft Comput 19:745–762

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Eslami M, Shareef H, Taha MR, Khajehzadeh M (2014) Adaptive particle swarm optimization for simultaneous design of UPFC damping controllers. Int J Electr Power Energy Syst 57:116–128

    Article  Google Scholar 

  • Fan Y, Jin R, Geng J, Liu B (2004) A hybrid optimized algorithm based on differential evolution and genetic algorithm and its applications in pattern synthesis of antenna arrays. Acta Electr Sin 32:1997–2000

    Google Scholar 

  • Ganapathy K, Vaidehi V, Kannan B, Murugan H (2014) Hierarchical particle swarm optimization with ortho-cyclic circles. Expert Syst Appl 41:3460–3476

    Article  Google Scholar 

  • Idris I, Selamat A, Nguyen NT, Omatu S, Krejcar O, Kuca K, Penhaker M (2015) A combined negative selection algorithm particle swarm optimization for an email spam detection system. Eng Appl Artif Intell 39:33–44

    Article  Google Scholar 

  • Kenndy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks. pp 1942–1948

  • Kennedy J, Mendes R (2002) Population structure and particle swarm performance

  • Kennedy J, Mendes R (2006) Neighborhood topologies in fully informed and best-of-neighborhood particle swarms. IEEE Trans Syst Man Cybern Part C Appl Rev 36:515

    Article  Google Scholar 

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

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

    Article  Google Scholar 

  • Lim WH, Isa NAM (2013) Two-layer particle swarm optimization with intelligent division of labor. Eng Appl Artif Intell 26:2327–2348

    Article  Google Scholar 

  • Lim WH, Isa NAM (2014a) An adaptive two-layer particle swarm optimization with elitist learning strategy. Inf Sci 273:49–72

    Article  MathSciNet  Google Scholar 

  • Lim WH, Isa NAM (2014b) Particle swarm optimization with increasing topology connectivity. Eng Appl Artifi Intell 27:80–102

    Article  Google Scholar 

  • Lim WH, Isa NAM (2014c) Teaching and peer-learning particle swarm optimization. Appl Soft Comput 18:39–58

    Article  Google Scholar 

  • Lim WH, Isa NAM (2015) Adaptive division of labor particle swarm optimization. Expert Syst Appl 42:5887–5903

    Article  Google Scholar 

  • Liu Y, Mu C, Kou W, Liu J (2014) Modified particle swarm optimization-based multilevel thresholding for image segmentation. Soft Comput 19:1311–1327

    Article  Google Scholar 

  • Mazhoud I, Hadj-Hamou K, Bigeon J, Joyeux P (2013) Particle swarm optimization for solving engineering problems: a new constraint-handling mechanism. Eng Appl Artif Intell 26:1263–1273

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Niu B, Zhu Y, He X, Wu H (2007) MCPSO: a multi-swarm cooperative particle swarm optimizer. Appl Math Comput 185:1050–1062

    MATH  Google Scholar 

  • Ren Z, Zhang A, Wen C, Feng Z (2014) A scatter learning particle swarm optimization algorithm for multimodal problems. Cybern IEEE Trans 44:1127–1140

    Article  Google Scholar 

  • Roy PK, Paul C, Sultana S (2014) Oppositional teaching learning based optimization approach for combined heat and power dispatch. Int J Electr Power Energy Syst 57:392–403

    Article  Google Scholar 

  • Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: Evolutionary computation proceedings, 1998. IEEE world congress on computational intelligence. The 1998 IEEE international conference on. IEEE, pp 69–73

  • Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. In: Evolutionary computation, 1999. CEC 99. Proceedings of the 1999 congress on. IEEE

  • Suganthan PN (1999) Particle swarm optimiser with neighbourhood operator. In: Evolutionary computation, 1999. CEC 99. Proceedings of the 1999 congress on. IEEE

  • Suganthan PN, Hansen N, Liang JJ, Deb K, Chen Y-P, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. KanGAL, Report 2005005

  • Tsai C-W, Huang K-W, Yang C-S, Chiang M-C (2014) A fast particle swarm optimization for clustering. Soft Comput 19:321–338

    Article  Google Scholar 

  • Wang C, Liu Y, Zhao Y, Chen Y (2014) A hybrid topology scale-free Gaussian-dynamic particle swarm optimization algorithm applied to real power loss minimization. Eng Appl Artif Intell 32:63–75

  • Wang H, Sun H, Li C, Rahnamayan S, Pan J-S (2013) Diversity enhanced particle swarm optimization with neighborhood search. Inf Sci 223:119–135, 119–135

  • Wang L, Yang B, Chen Y (2014) Improving particle swarm optimization using multi-layer searching strategy. Inf Sci 274:70–94

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Zhan Z-H, Zhang J, Li Y, Shi Y-H (2011) Orthogonal learning particle swarm optimization. Evol Comput IEEE Trans 15:832–847

    Article  Google Scholar 

  • Zhang J, Ding X (2011) A multi-swarm self-adaptive and cooperative particle swarm optimization. Eng Appl Artif Intell 24:958–967

  • Zhang L, Tang Y, Hua C, Guan X (2015) A new particle swarm optimization algorithm with adaptive inertia weight based on Bayesian techniques. Appl Soft Comput 28:138–149

  • Zhang W, Ma D, Wei J-J, Liang H-F (2014) A parameter selection strategy for particle swarm optimization based on particle positions. Expert Syst Appl 41:3576–3584

    Article  Google Scholar 

  • Zhao F, Tang J, Wang J,Jonrinaldi,(2014) An improved particle swarm optimization with decline disturbance index (DDPSO) for multi-objective job-shop scheduling problem. Comput Oper Res 45:38–50

  • Zhao X, Liu Z, Yang X (2014) A multi-swarm cooperative multistage perturbation guiding particle swarm optimizer. Appl Soft Comput 22:77–93

    Article  Google Scholar 

Download references

Acknowledgments

The authors wish to acknowledge the Natural Science Foundation of Shanxi Province, China (Grant No. 2015011019) for the financial support.

Author information

Authors and Affiliations

  1. College of Chemistry and Chemical Engineering, Taiyuan University of Technology, Taiyuan, 030024, China

    Jiao Liu, Di Ma, Teng-bo Ma & Wei Zhang

Authors
  1. Jiao Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Di Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Teng-bo Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wei Zhang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Communicated by A. Di Nola.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, J., Ma, D., Ma, Tb. et al. Ecosystem particle swarm optimization. Soft Comput 21, 1667–1691 (2017). https://doi.org/10.1007/s00500-016-2111-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-016-2111-4

Keywords

Search

Navigation