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
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
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
Chuang LH, Chang HW et al (2008) Improved binary PSO for feature selection using gene expression data. Comput Biol Chem 32(1):29–38
Digalakis JG, Margaritis KG (2002) An experimental study of benchmarking functions for genetic algorithms. Int J Comput Math 79(4):403–416
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
Elbetagi E, Hegazy T et al (2005) Comparison among five evolutionary-based optimization algorithms. Adv Eng Inf 19:43–53
Engelbrecht AP (2005) Fundamentals of computational swarm intelligence. Wiley, Chichester, UK
Farmer JD, Packard NH et al (1986) The immune system, adaptation, and machine learning. Phys D 22:187–204
Goldberg D (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading, MA
Holland JH (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor, Michigan
Holliday D, Resnick R et al (1993) Fundamentals of physics. Wiley, New York
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
Rashedi E, Nezamabadi-pour H et al (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248
Schutz B (2003) Gravity from the ground up. Cambridge University Press, Cambridge
Srinivasa KG, Venugopal KR et al (2007) A self-adaptive migration model genetic algorithm for data mining applications. Inf Sci 177(20):4295–4313
Wang X, Yang J et al (2007) Feature selection based on rough sets and particle swarm optimization. Pattern Recogn Lett 28:459–471
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1:67–82
Wu TH, Chang CC et al (2008) A simulated annealing algorithm for manufacturing cell formation problems. Expert Syst Appl 34(3):1609–1617
Yao X, Liu Y et al (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3:82–102
Youssef H, Sait SM et al (2001) Evolutionary algorithms, simulated annealing and tabu search: a comparative study. Eng Appl Artif Intell 14:167–181
Yuan X, Nie et al (2009) An improved binary particle swarm optimization for unit commitment problem. Expert Syst Appl 36(4):8049–8055
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
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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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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DOI: https://doi.org/10.1007/s11047-009-9175-3