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Particle swarm optimization with adaptive inertia weight based on cumulative binomial probability

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Abstract

Particle swarm optimization (PSO) is a population oriented heuristic numerical optimization algorithm, influenced by the combined behavior of some birds. Since its introduction in 1995, a large number of variants of PSO algorithm have been introduced that improves its performance. The performance of the algorithm mostly rely upon inertia weight and optimal parameter setting. Inertia weight brings equivalence among exploitation and exploration while searching optimal solution within the search region. This paper presents a new improved version of PSO that uses adaptive inertia weight technique which is based on cumulative binomial probability (CBPPSO). The proposed approach along with four other PSO variants are tested over a set of ten well-known optimization test problems. The result confirms that the performance of proposed algorithm (CBPPSO) is better than other PSO variants in most of the cases. Also, the proposed algorithm has been evaluated on three real-world engineering problems and the results obtained are promising.

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References

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

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

  3. Eberhart RC, Shi Y (2001) Tracking and optimizing dynamic systems with particle swarms. In: Evolutionary computation. Proceedings of the IEEE congress, Seoul, vol 1, pp 94–100

  4. Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. In: Proceedings of the IEEE congress, CEC 99, Washington, DC, vol 3, pp 1945–1950

  5. Al-Hassan W, Fayek MB, Shaheen SI (2006) Psosa: an optimized particle swarm technique for solving the urban planning problem. In: International conference on computer engineering and systems, pp 401–405

  6. Malik RF, Rahman TA, Hashim SZM, Ngah R (2007) New particle swarm optimizer with sigmoid increasing inertia weight. Int J Comput Sci Secur 1(2):35–44

    Google Scholar 

  7. Chen G, Huang X, Jia J, Min Z (2006) Natural exponential inertia weight strategy in particle swarm optimization. In: 6th world congress on intelligent control and automation, vol 1, pp 3672–3675

  8. Gao YL, An XH, Liu JM (2008) A particle swarm optimization algorithm with logarithm decreasing inertia weight and chaos mutation. In: Computational intelligence and security, IEEE international conference, vol 1, pp 61–65

  9. Nikabadi A, Ebadzadeh M (2008) Particle swarm optimization algorithms with adaptive inertia weight: a survey of the state of the art and a novel method. IEEE J Evol Comput

  10. Lei K, Qiu Y, He Y (2006) A new adaptive well-chosen inertia weight strategy to automatically harmonize global and local search ability in particle swarm optimization. In: 1st IEEE international symposium on systems and control in aerospace and astronautics, Harbin, pp 977–980

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

    Article  Google Scholar 

  12. Nickabadi A, Ebadzadeh MM, Safabakhsh R (2011) A novel particle swarm optimization algorithm with adaptive inertia weight. Appl Soft Comput 11(4):3658–3670

    Article  Google Scholar 

  13. Kessentini S, Barchiesi D (2015) Particle swarm optimization with adaptive inertia weight. Int J Mach Learn Comput 5(5):368–373

    Article  Google Scholar 

  14. Bansal JC, Singh PK, Saraswat M, Verma A, Jadon SS, Abraham A (2011) Inertia weight strategies in particle swarm optimization. In: Nature and biologically inspired computing (NaBIC), third world congress, pp 633–640

  15. Jordehi AR, Jasni J (2015) Particle swarm optimization for discrete optimization problems: a review. Artif Intell Rev 43(2):243–258

    Article  Google Scholar 

  16. Wang D, Tan D, Liu L (2018) Particle swarm optimization algorithm: an overview. Soft Comput 22(2):387–408

    Article  Google Scholar 

  17. Ghamisi P, Benediktsson JA (2015) Feature selection based on hybridization of genetic algorithm and particle swarm optimization. IEEE Geosci Remote Sens Lett 12(2):309–313

    Article  Google Scholar 

  18. Momeni E, Armaghani DJ, Hajihassani M, Amin MFM (2015) Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks. Measurement 60:50–63

    Article  Google Scholar 

  19. Gao Y, Du W, Yan G (2015) Selectively-informed particle swarm optimization. Sci Rep 5:9295

    Article  Google Scholar 

  20. Satapathy SC, Chittineni S, Krishna SM, Murthy JVR, Reddy PP (2012) Kalman particle swarm optimized polynomials for data classification. Appl Math Model 36(1):115–126

    Article  MathSciNet  Google Scholar 

  21. Tian D, Shi Z (2018) MPSO: modified particle swarm optimization and its applications. Swarm Evol Comput 41:49–68

    Article  Google Scholar 

  22. Agrawal A et al (2018) Particle swarm optimization with probabilistic inertia weight. In: Harmony search and nature inspired optimization algorithms, ICHSA 2018, pp 239–248

  23. Surjanovic S, Bingham D (2013) Virtual library of simulation experiments: test functions and datasets [online]. http://www.sfu.ca/~ssurjano/. Accessed Dec 2017

  24. Molga M, Smutnicki C (2005) Test functions for optimization needs. http://www.zsd.ict.pwr.wroc.pl/files/docs/functions.pdf. Accessed Dec 2017

  25. Global optimization test problems. http://www-optima.amp.i.kyoto-u.ac.jp/member/student/hedar/Hedar_files/TestGO.htm. Accessed Dec 2017

  26. Dixon LCW, Szego GP (1978) The global optimization problem: an introduction. Towards Glob Optim 2:1–15

    Google Scholar 

  27. Back T (1996) Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press, New York

    Book  Google Scholar 

  28. Laguna M, Marti R (2002) Experimental testing of advanced scatter search designs for global optimization of multimodal functions. http://www.uv.es/rmarti/paper/docs/global1.pdf. Accessed Dec 2017

  29. Global optimization test functions index. http://infinity77.net/global_optimization/test_functions.html#test-functions-index. Accessed Dec 2017

  30. Martínez JF, Gonzalo EG (2009) The PSO family: deduction, stochastic analysis and comparison. Swarm Intell 3(4):245–273

    Article  Google Scholar 

  31. Lobato FS, Steffen Jr V (2014) Fish swarm optimization algorithm applied to engineering system design. Latin Am J Solids Struct 11(1):143–156

    Article  Google Scholar 

  32. He Q, Wang L (2007) An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng Appl Artif Intell 20(1):89–99

    Article  Google Scholar 

  33. Sandgren E (1990) Nonlinear integer and discrete programming in mechanical design optimization. J Mech Des 112(2):223–229

    Article  Google Scholar 

  34. Dong M, Wang N, Cheng X, Jiang C (2014) Composite differential evolution with modified oracle penalty method for constrained optimization problems. Math Probl Eng 2014:617905

    Google Scholar 

  35. Omran MG (2010) CODEQ: an effective metaheuristic for continuous global optimization. Int J Metaheuristics 1(2):108–131

    Article  MathSciNet  Google Scholar 

  36. Lee KS, Geem ZW (2005) A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput Methods Appl Mech Eng 194(36):3902–3933

    Article  Google Scholar 

  37. Wu SJ, Chow PT (1995) Genetic algorithms for nonlinear mixed discrete-integer optimization problems via meta-genetic parameter optimization. Eng Optim 24(2):137–159

    Article  Google Scholar 

  38. Belegundu AD (1982) A study of mathematical programming methods for structural optimization. Dept. of Civil Environ. Eng., Iowa Univ

  39. Arora JS (1989) Introduction to optimum design. McGraw-Hill, New York

    Google Scholar 

  40. Deb K (1991) Optimal design of a welded beam via genetic algorithms. AIAA J 29(11):2013–2015

    Article  Google Scholar 

  41. Coello CAC (2000) Use of a self-adaptive penalty approach for engineering optimization problems. Comput Ind 41(2):113–127

    Article  Google Scholar 

  42. Coello CAC, Montes EM (2002) Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Adv Eng Inform 16(3):193–203

    Article  Google Scholar 

  43. Zheng H, Zhou Y (2013) A cooperative coevolutionary cuckoo search algorithm for optimization problem. J Appl Math 2013:912056

    MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Raipur, G.E Road, Raipur, Chhattisgarh, 492001, India

    Ankit Agrawal & Sarsij Tripathi

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  1. Ankit Agrawal
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  2. Sarsij Tripathi
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Correspondence to Ankit Agrawal.

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Agrawal, A., Tripathi, S. Particle swarm optimization with adaptive inertia weight based on cumulative binomial probability. Evol. Intel. 14, 305–313 (2021). https://doi.org/10.1007/s12065-018-0188-7

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