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Repeating Successful Movement Strategy for ABC AlgorithmKOCER, B.
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artificial intelligence, machine learning, evolutionary computation, particle swarm optimization, machine intelligence
optimization(18), algorithm(18), artificial(13), colony(12), comput(8), jasoc(6), swarm(5), soft(5), search(5), jins(5)
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About this article
Date of Publication: 2017-08-31
Volume 17, Issue 3, Year 2017, On page(s): 85 - 94
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.03011
Web of Science Accession Number: 000410369500011
SCOPUS ID: 85028548845
ABC is a well-known nature inspired algorithm. In short ABC algorithm mimics the foraging behavior of the bee colonies. ABC is very intensively worked algorithm. It has many variants. The base algorithm and most of the variants uses an update equation to improve the solutions. The update equation finds a feasible movement based on neighbor solutions and adds that movement to current to create a mutant solution. If the mutant solution is better than the original one then original solution is updated. None of the ABC variant use a successful movement again. In this work when a successful move found then it is used again. Proposed approach is applied to ABCVSS algorithm which is a recently proposed ABC variant and that modified ABCVSS algorithm (ABCVSSRSM) is tested on numerical benchmark functions and results compared the well-known ABC variants. Results show that proposed method is superior under multiple criteria.
|References|||||Cited By «-- Click to see who has cited this paper|
| D. Karaboga, "An idea based on honey bee swarm for numerical optimization," Erciyes University, Kayseri, Turkey, Tech. Rep., TR06, 2005.
 J. Kennedy, R. Eberhart, "Particle swarm optimization," in 1995 IEEE international conference on neural networks proceedings, Vols. 16 pp. 19421948, 1995
 M. Dorigo, V. Maniezzo, A. Colorni, "Ant system: Optimization by a colony of cooperating agents," IEEE Transactions on Systems Man and Cybernetics Part B, Cybernetics, 26(1), 1996, pp. 2941.
[CrossRef] [Web of Science Times Cited 5185] [SCOPUS Times Cited 7295]
 X. S. Yang, S. Deb, "Engineering optimization by cuckoo search," Int. J. Math. Model. Numer. Opt. 1, pp. 330343, 2010
 X. S. Yang, "Firefly algorithm, stochastic test functions and design optimisation," International Journal of Bio-Inspired Computation, 2(2), pp. 7884, 2010
[CrossRef] [Web of Science Times Cited 729] [SCOPUS Times Cited 979]
 Z. W. Geem, J. H. Kim, G. V. Loganathan, "A new heuristic optimization algorithm: Harmony search. Simulation," 76(2), pp. 6068, 2001
[CrossRef] [SCOPUS Times Cited 3041]
 Uymaz S. A. , Tezel G., Yel E., Artificial algae algorithm (AAA) for nonlinear global optimization, Applied Soft Computing, Volume 31, June 2015, pp. 153-171, ISSN 1568-4946,
[CrossRef] [Web of Science Times Cited 31] [SCOPUS Times Cited 39]
 H. M. Harmanani, F. Drouby, S. B. Ghosn, A parallel genetic algorithm for the open-shop scheduling problem using deterministic and random moves, International Journal of Artificial Intelligence, vol. 14, no. 1, pp. 130-144, 2016.
 Z. C. Johanyák, O. Papp, "A hybrid algorithm for parameter tuning in fuzzy model identification," Acta Polytechnica Hungarica, vol. 9, no. 6, pp. 153-165, 2012.
 R.-E. Precup, R.-C. David, E. M. Petriu, S. Preitl, M.-B. Radac, "Novel adaptive charged system search algorithm for optimal tuning of fuzzy controllers," Expert Systems with Applications, vol. 41, no. 4, pp. 1168-1175, 2014
[CrossRef] [Web of Science Times Cited 49] [SCOPUS Times Cited 62]
 A. Basgumus, M. Namda, R, G. Yilmaz, A. Altuncu, "Performance comparison of the differential evolution and particle swarm optimization algorithms in free-space optical communications systems," Advances in Electrical and Computer Engineering, vol. 15, no. 2, pp. 17-22, 2015.
[CrossRef] [Full Text] [Web of Science Times Cited 8] [SCOPUS Times Cited 8]
 B. Akay, D. Karaboga, "A modified artificial bee colony algorithm for real-parameter optimization," Inf. Sci. 192, pp. 120142, 2012
[CrossRef] [Web of Science Times Cited 471] [SCOPUS Times Cited 685]
 G. Zhu, S. Kwong, "Gbest-guided artificial bee colony algorithm for numerical function optimization," Appl. Math. Comput. 217, pp. 31663173, 2010
[CrossRef] [Web of Science Times Cited 509] [SCOPUS Times Cited 694]
 A. Banharnsakun, T. Achalakul, B. Sirinaovakul, "The best-so-far selection in artificial bee colony algorithm," Appl. Math. Comput. 11, pp. 28882901, 2011
[CrossRef] [Web of Science Times Cited 218] [SCOPUS Times Cited 285]
 A. Banharnsakun, B. Sirinaovakul, T. Achalakul, "The best-so-far ABC with multiple patrilines for clustering problems," Neurocomputing 116, pp. 355366, 2013
[CrossRef] [Web of Science Times Cited 18] [SCOPUS Times Cited 21]
 W. Gao, S. Liu, L. Huang, "A global best artificial bee colony algorithm for global optimization," J. Comput. Appl. Math. 236 pp. 27412753, 2012
[CrossRef] [Web of Science Times Cited 196] [SCOPUS Times Cited 247]
 M. S. Kiran, O. Findik, "A directed artificial bee colony algorithm," Appl. Soft Comput. 26, pp. 454462, 2015
[CrossRef] [Web of Science Times Cited 79] [SCOPUS Times Cited 106]
 W. Gao, S. Liu, "A modified artificial bee colony algorithm," Comput. Oper. Res. 39, pp. 687697, 2012
[CrossRef] [Web of Science Times Cited 261] [SCOPUS Times Cited 359]
 D. Karaboga, B. Gorkemli, "A quick artificial bee colony (qABC) algorithm and its performance on optimization problems," Appl. Soft Comput. 23, pp. 227238, 2014
[CrossRef] [Web of Science Times Cited 115] [SCOPUS Times Cited 149]
 N. Imanian, M.E. Shiri, P. Moradi, "Velocity based artificial bee colony algorithm for high dimensional continuous optimization problems," Eng. Appl. Artif. Intell. 36, pp. 148163, 2014
[CrossRef] [Web of Science Times Cited 36] [SCOPUS Times Cited 44]
 W. Du, B. Li "Multi-strategy ensemble particle swarm optimization for dynamic optimization," Inform. Sci., 178 (15), pp. 30963109, 2008
[CrossRef] [Web of Science Times Cited 140] [SCOPUS Times Cited 176]
 R. Mallipeddi, S. Mallipeddi, P.N. Suganthan, "Ensemble strategies with adaptive evolutionary programming," Inform. Sci., 180 (9), pp. 15711581, 2010
[CrossRef] [Web of Science Times Cited 75] [SCOPUS Times Cited 90]
 R. Mallipeddi, P.N. Suganthan, "Ensemble of constraint handling techniques," IEEE Trans. Evolut. Comput., 14(4), pp. 561579, 2010
[CrossRef] [Web of Science Times Cited 182] [SCOPUS Times Cited 230]
 R. Mallipeddi, P.N. Suganthan, Q.K. Pan, M.F. Tasgetiren, "Differential evolution algorithm with ensemble of parameters and mutation strategies," Appl. Soft Comput., 11(2), , pp. 16791696, 2011
[CrossRef] [Web of Science Times Cited 611] [SCOPUS Times Cited 694]
 H. Wang, Z. Wu, S. Rahnamayan, H. Sun, Y. Liu, J. Pan, "Multi-strategy ensemble artificial bee colony algorithm," Inf. Sci. 279, pp. 587603, 2014
[CrossRef] [Web of Science Times Cited 73] [SCOPUS Times Cited 110]
 M. S. Kiran, H. Hakli, M. Gunduz, H. Uguz, "Artificial bee colony algorithm with variable search strategy for continuous optimization," Information Sciences 300, pp. 140157, 2010
[CrossRef] [Web of Science Times Cited 76] [SCOPUS Times Cited 102]
 W. Gao, S. Liu, L. Huang, "A novel artificial bee colony algorithm based on modified search equation and orthogonal learning," IEEE T. Syst. Man Cy. B, 2012,
[CrossRef] [Web of Science Times Cited 153] [SCOPUS Times Cited 202]
 P. N. Suganthan, N. Hansen, J.J. Liang, K. Deb, Y.-P. Chen, A. Auger, S. Tiwari, "Problem definitions and evaluation criteria for the CEC 2005 special session on real parameter optimization," Technical report, 2005005, ITT Kanpur, India, 2005.
 V. Muthiah-Nakarajan, M. M. Noel, "Galactic Swarm Optimization: A new global optimization metaheuristic inspired by galactic motion," Applied Soft Computing, Volume 38, January 2016, Pages 771-787, ISSN 1568-4946,
[CrossRef] [Web of Science Times Cited 12] [SCOPUS Times Cited 17]
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