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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 4700] [SCOPUS Times Cited 6598]
 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 501] [SCOPUS Times Cited 688]
 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 2456]
 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 13] [SCOPUS Times Cited 18]
 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 38] [SCOPUS Times Cited 52]
 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 3] [SCOPUS Times Cited 4]
 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 361] [SCOPUS Times Cited 484]
 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 387] [SCOPUS Times Cited 522]
 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 176] [SCOPUS Times Cited 221]
 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 15] [SCOPUS Times Cited 18]
 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 147] [SCOPUS Times Cited 187]
 M. S. Kiran, O. Findik, "A directed artificial bee colony algorithm," Appl. Soft Comput. 26, pp. 454462, 2015
[CrossRef] [Web of Science Times Cited 42] [SCOPUS Times Cited 61]
 W. Gao, S. Liu, "A modified artificial bee colony algorithm," Comput. Oper. Res. 39, pp. 687697, 2012
[CrossRef] [Web of Science Times Cited 212] [SCOPUS Times Cited 294]
 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 68] [SCOPUS Times Cited 92]
 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 27] [SCOPUS Times Cited 32]
 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 123] [SCOPUS Times Cited 155]
 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 70] [SCOPUS Times Cited 86]
 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 146] [SCOPUS Times Cited 190]
 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 460] [SCOPUS Times Cited 562]
 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 46] [SCOPUS Times Cited 72]
 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 35] [SCOPUS Times Cited 47]
 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 100] [SCOPUS Times Cited 127]
 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 5] [SCOPUS Times Cited 7]
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