Empirical analysis of a modified Artificial Bee Colony for constrained numerical optimization

Applied Mathematics and Computation

Volumn 218, Issue 22, 2012, Pages 10943-10973

  Mezura Montes, Efrén ^a   Cetina Domínguez, Omar ^a  

^a National Laboratory of Advanced Informatics Xalapa   (Mexico)

Author keywords

Constraint handling; Evolutionary algorithms; Global optimization; Swarm Intelligence

Indexed keywords

ARTIFICIAL BEE COLONIES; ARTIFICIAL BEE COLONY ALGORITHMS; BENCH-MARK PROBLEMS; BOUNDARY CONSTRAINTS; CONSTRAINT HANDLING; EMPIRICAL ANALYSIS; EQUALITY CONSTRAINTS; FEASIBLE REGIONS; FEASIBLE SOLUTION; GLOBAL OPTIMUM SOLUTIONS; NUMERICAL OPTIMIZATIONS; PERFORMANCE MEASURE; SEARCH SPACES; SELECTION MECHANISM; STATE-OF-THE-ART ALGORITHMS; SWARM INTELLIGENCE; TEST DESIGNS;

ARTIFICIAL INTELLIGENCE; CONSTRAINED OPTIMIZATION; GLOBAL OPTIMIZATION;

EVOLUTIONARY ALGORITHMS;

EID: 84862850206     PISSN: 00963003     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.amc.2012.04.057     Document Type: Article

References (44)

1. A.P. Engelbrecht Fundamentals of Computational Swarm Intelligence 2005 Wiley
2. A. Eiben, and J.E. Smith Introduction to Evolutionary Computing Natural Computing Series 2003 Springer-Verlag
3. E. Mezura-Montes Constraint-Handling in Evolutionary Optimization of Studies in Computational Intelligence vol. 198 2009 Springer-Verlag
4. Z. Michalewicz, and M. Schoenauer Evolutionary algorithms for constrained parameter optimization problems Evolutionary Computation 4 1 1996 1 32
5. C.A.C. Coello Theoretical and numerical constraint handling techniques used with evolutionary algorithms: a survey of the state of the art Computer Methods in Applied Mechanics and Engineering 191 11-12 2002 1245 1287
6. C.K. Monson, and K.D. Seppi Linear equality constraints homomorphous mappings in PSO IEEE Congress on Evolutionary Computation (CEC'2005) vol. 1 2005 IEEE Service Center Edinburgh, Scotland 73 80
7. L. Cagnina, S. Esquivel, and C. Coello-Coello A Bi-population PSO with a shake-mechanism for solving constrained numerical optimization IEEE Congress on Evolutionary Computation (CEC'2007) 2007 IEEE Press Singapore 670 676
8. E. Mezura-Montes, J.I. Flores-Mendoza, Multiobjective problems solving from nature: From concepts to applications, in: R. Chiong (Ed.), Nature-Inspired Algorithms for Optimization, Studies in Computational Intelligence Series, Ch. Improved Particle Swarm Optimization in Constrained Numerical Search Spaces, ISBN: 978-3-540-72963-1, 2009, Springer-Verlag, vol. 193, (2009), pp. 299-332.
9. Angel E. Muñoz-Zavala, A. Hernández-Aguirre, E.R. Villa-Diharce, and S. Botello-Rionda PESO + for constrained optimization IEEE Congress on Evolutionary Computation (CEC'2006) 2006 IEEE, Vancouver 942 935
10. A.E. Muñoz Zavala, A. Hernández Aguirre, E.R. Villa Diharce, and S. Botello Rionda Constrained optimization with an improved particle swarm optimization algorithm International Journal of Intelligent Computing and Cybernetics 1 3 2008 425 453
11. H.A. Abbass Marriage in honey bees optimization (mbo): a haplometrosis polygynous swarming approach Congress on Evolutionary Computation, CEC 2001 IEEE Press 207 214
12. O.B. Haddad, and M.A. Marino Dynamic penalty function as a strategy in solving water resources combinatorial optimization problems with honey-bee mating operation (hbmo) algorithm Journal of Hydroinformatics 9 3 2007 233 250
13. A. Baykasoglu, L. Ozbakir, and P. Tapkan Artificial bee colony algorithm and its application to generalized assignment problem Swarm Intelligence: Focus on Ant and Particle Swarm Optimization 2007 Itech Education and Pub. Vienna, Austria 978-3-902613-09-7 113 144
14. D. Pham, A. Ghanbarzadeh, S.O.E. Ko, S. Rahim, and M. Zaidi The bees algorithm: a novel tool for complex optimisation problems Second Virtual International Conference on Intelligent Production Machines and Systems (IPROMS 2006) 2006 Oxford:Elsevier 454 461
15. D. Karaboga, B. Akay, Artificial bee colony (abc), harmony search and bees algorithms on numerical optimization, in: Innovative Production Machines and Systems Virtual Conference (IPROMS 2009), 2009. .
16. X.-S. Yang Engineering optimizations via nature-inspired virtual bee algorithms Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach 3562 2005 Springer Berlin, Heidelberg 317 323
17. D. Karaboga, and B. Basturk A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm Journal of Global Optimization 39 3 2007 459 471
18. D. Karaboga, B. Basturk, Artificial bee colony(abc) optimization algorithm for solving constrained optimization problems, in: P. Melin, O. Castillo, L.T. Aguilar, J. Kacptrzyk, W. Pedrycz (Eds.), Foundations of Fuzzy Logic and Soft Computing, 12th International Fuzzy Systems Association, World Congress, IFSA 2007, Lecture Notes in Artificial Intelligence, Springer, Cancun, Mexico, vol. 4529, 2007, pp. 789-798.
19. E. Mezura-Montes, and O. Cetina-Domínguez Exploring promising regions of the search space with the scout bee in the artificial bee colony for constrained optimization C.H. Dagli, Intelligent Engineering Systems through Artificial Neural Networks (ANNIE'2001) vol. 19 2009 ASME Press St. Louis Missouri, USA 253 260
20. D. Karaboga An Idea Based on Honey Bee Swarm for Numerical Optimization 2005 Erciyes University, Engineering Faculty, Turkey Tech. rep.
21. P.-W. TSai, J.-S. Pan, B.-Y. Liao, S.-C. Chu, Enhanced artificial bee colony optimization, International Journal of Innovative Computing, Information and Control 5 (12).
22. O.B. Haddad, A. Afshar, and M.A. Mario Honey-bees mating optimization (hbmo) algorithm: a new heuristic approach for water resources optimization Water Resources Management 20 2006 661 680
23. D.E. Goldberg Genetic Algorithms in Search, Optimization and Machine Learning 1989 Addison-Wesley Publishing Co., Reading, Massachusetts
24. D. Karaboga, and B. Basturk On the performance of artificial bee colony (abc) algorithm Applied Soft Computing 8 1 2008 687 697
25. K. Deb An efficient constraint handling method for genetic algorithms Computer Methods in Applied Mechanics and Engineering 186 2/4 2000 311 338
26. K. Zielinski, S.P. Vudathu, R. Laur, and N. Krasnogor Influence of different deviations allowed for equality constraints on particle swarm optimization and differential evolution N. Krasnogor, G. Nicosia, M. Pavone, D. Pelta, Nature Inspired Cooperative Strategies for Optimization 2008 Springer Berlin 978-3-540-78986-4
27. J.J. Liang, T. Runarsson, E. Mezura-Montes, M. Clerc, P. Suganthan, C.A. Coello Coello, K. Deb, Problem definitions and evaluation criteria for the CEC 2006 special session on constrained real-parameter optimization, Technical report, Nanyang Technological University, Singapore, (December, 2005). .
28. G. Eiben, and M. Schut New Ways to Calibrate Evolutionary Algorithms P. Siarry, Z. Michalewicz, Advances in Metaheuristics for Hard Optimization 2008 Natural Computing Springer, Heidelberg, Germany 153 177
29. S.B. Hamida, and M. Schoenauer ASCHEA: new results using adaptive segregational constraint handling Proceedings of the Congress on Evolutionary Computation 2002 (CEC'2002) vol. 1 2002 IEEE Service Center Piscataway, New Jersey 884 889
30. E. Mezura-Montes, and C.A.C. Coello Identifying on-line behavior some sources of difficulty in two competitive approaches for constrained optimization IEEE Congress on Evolutionary Computation (CEC'2005) vol. 2 2005 IEEE Press Edinburgh, Scotland 1477 1484
31. H. Lu, and W. Chen Self-adaptive velocity particle swarm optimization for solving constrained optimization problems Journal of Global Optimization 41 3 2008 427 445
32. H.-P. Schwefel Evolution and Optimization Seeking 1995 Wiley New York
33. K. Price, R. Storn, and J. Lampinen Differential evolution: a practical approach to global optimization Natural Computing Series 2005 Springer-Verlag
34. J. Kennedy, and R.C. Eberhart Swarm Intelligence 2001 Morgan Kaufmann UK
35. S. Kukkonen, and J. Lampinen Constrained real-parameter optimization with generalized differential evolution IEEE Congress on Evolutionary Computation (CEC'2006) 2006 IEEE, Vancouver BC, Canada 911 918
36. K.V. Price, and J.I. Rönkkönen Comparing the uni-modal scaling performance of global and local selection in a mutation-only differential evolution algorithm IEEE Congress on Evolutionary Computation (CEC'2006) 2006 IEEE Press, Vancouver Canada 7387 7394
37. J. Lampinen A constraint handling approach for the differential evolution algorithm Proceedings of the Congress on Evolutionary Computation (CEC'2002) vol. 2 2002 IEEE Service Center Piscataway, New Jersey 1468 1473
38. R. Mallipeddi, and P. Suganthan Ensemble of constraint handling techniques IEEE Transactions on Evolutionary Computation 14 4 2010 561 579
39. Y. Wang, Z. Cai, G. Guo, and Y. Zhou Multiobjective optimization and hybrid evolutionary algorithm to solve constrained optimization problems IEEE Transactions on Systems, Man and Cybernetics Part B - Cybernetics 37 3 2007 560 575
40. Y. Wang, Z. Cai, Y. Zhou, and W. Zeng An adaptive tradeoff model for constrained evolutionary optimization IEEE Transactions on Evolutionary Computation 12 1 2008 80 92
41. M. Zhang, W. Luo, and X. Wang Differential evolution with dynamic stochastic selection for constrained optimization Information Sciences 178 15 2008 3043 3074
42. D.H. Wolpert, and W.G. Macready No free lunch theorems for optimization IEEE Transactions on Evolutionary Computation 1 1 1997 67 82
43. T. Takahama, and S. Sakai Constrained optimization by the constrained differential evolution with gradient-based mutation and feasible elites IEEE Congress on Evolutionary Computation (CEC'2006) 2006 IEEE, Vancouver BC, Canada 308 315
44. V.L. Huang, A.K. Qin, and P.N. Suganthan Self-adaptative differential evolution algorithm for constrained real-parameter optimization IEEE Congress on Evolutionary Computation (CEC'2006) 2006 IEEE, Vancouver BC, Canada 324 331