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BGSA: binary gravitational search algorithm

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Abstract

Gravitational search algorithm is one of the new optimization algorithms that is based on the law of gravity and mass interactions. In this algorithm, the searcher agents are a collection of masses, and their interactions are based on the Newtonian laws of gravity and motion. In this article, a binary version of the algorithm is introduced. To evaluate the performances of the proposed algorithm, several experiments are performed. The experimental results confirm the efficiency of the BGSA in solving various nonlinear benchmark functions.

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References

  • Avishek P, Maiti J (2010) Development of a hybrid methodology for dimensionality reduction in Mahalanobis–Taguchi system using Mahalanobis distance and binary particle swarm optimization. Expert Syst Appl 37(2):1286–1293

    Article  Google Scholar 

  • Beretaa M, Burczynski T (2007) Comparing binary and real-valued coding in hybrid immune algorithm for feature selection and classification of ECG signals. Eng Appl Artif Intell 20:571–585

    Article  Google Scholar 

  • Cheng YM, Li L et al (2007) Performance studies on six heuristic global optimization methods in the location of critical slip surface. Comput Geotech 34(6):462–484

    Article  Google Scholar 

  • Chuang LH, Chang HW et al (2008) Improved binary PSO for feature selection using gene expression data. Comput Biol Chem 32(1):29–38

    Article  MATH  Google Scholar 

  • Digalakis JG, Margaritis KG (2002) An experimental study of benchmarking functions for genetic algorithms. Int J Comput Math 79(4):403–416

    Article  MATH  MathSciNet  Google Scholar 

  • Dorigo M, Maniezzo V et al (1996) The Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern B 26(1):29–41

    Article  Google Scholar 

  • Elbetagi E, Hegazy T et al (2005) Comparison among five evolutionary-based optimization algorithms. Adv Eng Inf 19:43–53

    Article  Google Scholar 

  • Engelbrecht AP (2005) Fundamentals of computational swarm intelligence. Wiley, Chichester, UK

    Google Scholar 

  • Farmer JD, Packard NH et al (1986) The immune system, adaptation, and machine learning. Phys D 22:187–204

    Article  MathSciNet  Google Scholar 

  • Goldberg D (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading, MA

    MATH  Google Scholar 

  • Holland JH (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor, Michigan

    Google Scholar 

  • Holliday D, Resnick R et al (1993) Fundamentals of physics. Wiley, New York

    Google Scholar 

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

  • Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm algorithm. In: IEEE international conference on computational cybernetics and simulation, vol 5, pp 4104–4108

  • Kirkpatrick S, Gelatto CD et al (1983) Optimization by simulated annealing. Science 220:671–680

    Article  MathSciNet  Google Scholar 

  • Rashedi E, Nezamabadi-pour H et al (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248

    Article  MATH  Google Scholar 

  • Schutz B (2003) Gravity from the ground up. Cambridge University Press, Cambridge

    Google Scholar 

  • Srinivasa KG, Venugopal KR et al (2007) A self-adaptive migration model genetic algorithm for data mining applications. Inf Sci 177(20):4295–4313

    Article  MATH  Google Scholar 

  • Wang X, Yang J et al (2007) Feature selection based on rough sets and particle swarm optimization. Pattern Recogn Lett 28:459–471

    Article  Google Scholar 

  • Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1:67–82

    Article  Google Scholar 

  • Wu TH, Chang CC et al (2008) A simulated annealing algorithm for manufacturing cell formation problems. Expert Syst Appl 34(3):1609–1617

    Article  MathSciNet  Google Scholar 

  • Yao X, Liu Y et al (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3:82–102

    Article  Google Scholar 

  • Youssef H, Sait SM et al (2001) Evolutionary algorithms, simulated annealing and tabu search: a comparative study. Eng Appl Artif Intell 14:167–181

    Article  Google Scholar 

  • Yuan X, Nie et al (2009) An improved binary particle swarm optimization for unit commitment problem. Expert Syst Appl 36(4):8049–8055

    Article  Google Scholar 

  • Zeng XP, Li YM et al (2009) A dynamic chain-like agent genetic algorithm for global numerical optimization and feature selection. Neurocomputing 72:214–1228

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the guest Editors and the anonymous reviewers for their very helpful suggestions. In addition, the authors would like to extend their appreciation to Dr. Saeid Seydnejad for proof reading the manuscript and providing valuable comments.

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Authors and Affiliations

  1. Department of Electrical Engineering, Shahid Bahonar University of Kerman, P.O. Box 76169-133, Kerman, Iran

    Esmat Rashedi, Hossein Nezamabadi-pour & Saeid Saryazdi

Authors
  1. Esmat Rashedi
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  2. Hossein Nezamabadi-pour
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  3. Saeid Saryazdi
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Corresponding author

Correspondence to Hossein Nezamabadi-pour.

Appendix

Appendix

See Tables 11, 12, 13, 14, 15, and 16.

Table 11 a ij in F14
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Table 12 \( a_{i} \) and b i in F15
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Table 13 a ij , c i and P ij in F19
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Table 14 a ij , c i and P ij in F20
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Table 15 a ij and c i in F21, F22 and F23
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Table 16 Optima in functions of Table 3
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Rashedi, E., Nezamabadi-pour, H. & Saryazdi, S. BGSA: binary gravitational search algorithm. Nat Comput 9, 727–745 (2010). https://doi.org/10.1007/s11047-009-9175-3

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