Abstract
An information sharing artificial bee colony (ABC) algorithm has been proposed for locating and tracking multiple peaks in non-stationary environments. The niching method has been adapted by hybridizing two techniques. A modified variant of the fitness sharing has been used for detecting multiple peaks simultaneously and a speciation based technique is employed to keep the better individuals of the previous generation. The base algorithm used here is a modified variant of ABC that helps to synchronize the employer and onlooker forager swarms by synergizing the local information. The main crux of our algorithm is its independency of the problem dependent control parameters, like niche radius, and the absence of any hard-partitioning technique that leads to high computational burden. Our framework aims at bringing about a simple, robust approach that can be applied to a variety of dynamic functional landscapes. Experimental investigations are undertaken on standard benchmarks focussing on the competitive performance of our algorithm in contrast to the existing state-of-the-art to highlight the significance of our work.
Similar content being viewed by others
References
Bonabeau E, Dorigo M, Theraulaz G (1999) Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York
Branke J (1999) “Memory enhanced evolutionary algorithms for changing optimization problems”. In: IEEE Congress on Evolutionary Computation, CEC, IEEE, 3:1875–1882
Cavicchio D (1970) Adapting Search Using Simulated Evolution, Ph.D. Dissertation, Univ. Michigan, Ann, Arbor
Cioppa AD, Stefano CD, Marcelli A (2007) Where are the niches? dynamic fitness sharing. IEEE Trans Evol Comput 11(4):453–465
Cobb HG, Grefenstette JJ (1993) “Genetic algorithms for tracking changing environments”. In: International Conference on Genetic Algorithms, Morgan Kaufmann, pp 523–530
Cruz C, González JR, Pelta DA (2011) Optimization in dynamic environments: a survey on problems, methods and measures. Soft Comput 15(7):1427–1448
Cuevas E, Sención-Echauri F, Zaldivar D, Pérez-Cisneros M (2012) Multi-circle detection on images using artificial bee colony (ABC) optimization. Soft Comput 16:281–296
Das S, Maity S, Qu B-Y, Suganthan PN (2011) Real-parameter evolutionary multimodal optimization: a survey of the state-of-the-art. Swarm Evol Comput 1:71–88
De Jong KA (1975) An analysis of the behavior of a class of genetic adaptive systems. University of Michigan, Doctoral Dissertation
Deb K, Srinivasan A (2006) “Innovization: innovative design principles through optimization”, Genetic and Evolutionary Computation Conference ( GECCO-2006), New York, pp 1629–1636
Eberhart RC, Shi Y, Kennedy J (2001) Swarm Intelligence, Morgan Kaufmann
Eiben AE, Smith JE (2003) Introduction to Evolutionary Computing, Springer
Garg H, Rani M, Sharma SP (2013) Predicting uncertain behavior of press unit in a paper industry using artificial bee colony and fuzzy Lambda-Tau methodology. Appl Soft Comput 13(4):1869–1881
Goldberg DE, Richardson J (1987) “Genetic algorithms with sharing for multimodal function optimization”. In: Proceedings of the Second International Conference on Genetic Algorithms, pp 41–49
Goldberg DE, Smith RE (1987) “Nonstationary function optimization using genetic algorithms with dominance and diploidy”. In: Grefenstette JJ (ed) International Conference on Genetic Algorithms, Lawrence Erlbaum Associates, pp 59–68
Hardin G (1960) The competitive exclusion principle. Science 131:1292–1297
Holland J (1975) Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor
Hsieh T-J, Hsiao H-F, Yeh W-C (2011) Forecasting stock markets using wavelet transforms and recurrent neural networks: an integrated system based on artificial bee colony algorithm. Appl Soft Comput 11(2):2510–2525
Ji J, Wei H, Liu C (2013) An artificial bee colony algorithm for learning Bayesian networks. Soft Comput 17:983–994
Jiang H, Zhang B (2013) Dynamical memory control based on projection technique for online regression. Soft Comput 17:587–596
Jin Y, Branke J (2005) Evolutionary optimization in uncertain environments–a survey. IEEE Trans Evol Comput 9(3):303–317
Karaboga D (2005) “An idea based on honey bee swarm for numerical optimization”, Technical Report TR06. Computer Engineering Department. Engineering Faculty, Erciyes University
Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471
Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697
Li J-P, Balazs ME, Parks GT, Clarkson PJ (2002) A species conserving genetic algorithm for multimodal function optimization. Evol Comput 10(3):207–234
Li X (2010) Niching without niching parameters: particle swarm optimization using a ring topology. IEEE Trans Evol Comput 14(1):150–169
Li C, Yang S, Nguyen TT, Yu EL, Yao X, Jin Y, Beyer H-G, Suganthan PN (2008) Benchmark Generator for CEC 2009 Competition on Dynamic Optimization. University of Leicester and University of Birmingham, UK, Technical Report
Ma M, Lieang J, Guo M, Fan Y, Yin Y (2011) SAR image segmentation based on artificial bee colony algorithm. Appl Soft Comput 11:5205–5214
Manoj VJ, Elias E (2012) Artificial bee colony algorithm for the design of multiplierless nonuniform filter bank transmultiplexer. Inf Sci 192:193–203
Maravall D, de Lope J (2007) Multi-objective dynamic optimization with genetic algorithms for automatic parking. Soft Comput 11:249–257
Morrison R (2003) Performance measurement in dynamic environments. In: Branke J (ed) GECCO Workshop on Evolutionary Algorithms for Dynamic Optimization Problems, pp 5–8
Nguyen TT (2011) Continuous Dynamic Optimisation Using Evolutionary Algorithms, Ph.D. Thesis, School of Computer Science, University of Birmingham
Nguyen TT, Yang S, Branke J (2012) Evolutionary dynamic optimization: a survey of the state of the art. Swarm Evol Comput 6:1–24
Parrott D, Li X (2006) “Locating and Tracking Multiple Dynamic Optima by a Particle Swarm Model Using Speciation”. IEEE Trans Evol Comput 10:(4)
Petrowski A (1996) “A clearing procedure as a niching method for genetic algorithms”, Proceedings of 3rd IEEE Congress on, Evolutionary Computation, pp 798–803
Qu BY, Suganthan PN, Liang JJ (Oct. 2012) Differential evolution with neighborhood mutation for multimodal optimization. IEEE Trans Evol Comput 16(5):601–614
Rohlfshagen P, Yao X (2011) Dynamic combinatorial optimisation problems: an analysis of the subset sum problem. Soft Comput 15:1723–1734
Rubio-Largo A, Vega-Rodríguez MA, Goómez-Pulido JA, Sánchez-Pérez JM (2013) A multiobjective approach based on artificial bee colony for the static routing and wavelength assignment problem. Soft Comput 17:199–211
Samanta S, Chakraborty S (2011) Parametric optimization of some non-traditional machining processes using artificial bee colony algorithm. Eng Appl Artif Intell 24:946–957
Singh A, Sundar S (2011) An artificial bee colony algorithm for the minimum routing cost spanning tree problem. Soft Comput 15:2489–2499
Sonmez M (2011) Artificial bee colony algorithm for optimization of truss structures. Appl Soft Comput 11:2406–2418
Stoean C, Preuss M, Stoean R, Dumitersu D (2007) “Disburdeing the species conservation evolutioanry algorithm of arguing with radii”. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp 1420–1427
Stoean C, Preuss M, Stoean R, Dumitrescu D (2010) “Multimodal Optimization by Means of a Topological Species Conservation Algorithm”. IEEE Trans Evol Comput 14(6)
Yang S, Li C (2010) “A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments”. IEEE Trans Evol Comput 14(6)
Yildiz AR (2013) A new hybrid artificial bee colony algorithm for robust optimal design and manufacturing. Appl Soft Comput 13(5):2906–2912
Zhai J-H, Xu H-Y, Wang X-Z (2012) Dynamic ensemble extreme learning machine based on sample entropy. Soft Comput 16:1493–1502
Acknowledgments
The authors are grateful to the Associated Editor and the reviewers for their constructive comments that helped us in improving our research article. We also extend our sincerest thanks to Dr. X. Li and Dr. P. N. Suganthan for providing us with the necessary software needed for experimentation.
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by W. Pedrycz.
Rights and permissions
About this article
Cite this article
Biswas, S., Das, S., Kundu, S. et al. Utilizing time-linkage property in DOPs: An information sharing based Artificial Bee Colony algorithm for tracking multiple optima in uncertain environments. Soft Comput 18, 1199–1212 (2014). https://doi.org/10.1007/s00500-013-1138-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-013-1138-z