A new modification approach on bat algorithm for solving optimization problems

Applied Soft Computing Journal

Volumn 28, Issue , 2015, Pages 259-275

  Yilmaz, Selim ^a   Küçüksille, Ecir U ^b  

^a HACETTEPE UNIVERSITY   (Turkey)

^b SULEYMAN DEMIREL UNIVERSITY   (Turkey)

Author keywords

Bat algorithm; Heuristics; Real world problems; Unconstrained problems

Indexed keywords

HEURISTIC ALGORITHMS; HEURISTIC METHODS; PARTICLE SWARM OPTIMIZATION (PSO);

BAT ALGORITHMS; HEURISTICS; OPTIMIZATION METHOD; OPTIMIZATION PROBLEMS; REAL-WORLD PROBLEM; SOCIAL BEHAVIOR; STANDARD TEST FUNCTIONS; UNCONSTRAINED PROBLEMS;

PROBLEM SOLVING;

EID: 84919816292     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2014.11.029     Document Type: Article

References (85)

1. S. Rao Engineering Optimization: Theory and Practice 1996 New Age International
2. E.K.P. Chong, and S.H. Zak An Introduction to Optimization (Wiley-Interscience Series in Discrete Mathematics and Optimization) third ed. 2008 Wiley-Interscience
3. X. Yang Nature-Inspired Metaheuristic Algorithms second ed. 2010 Luniver Press
4. M.M. Noel A new gradient based particle swarm optimization algorithm for accurate computation of global minimum Appl. Soft Comput. 12 1 2012 353 359
5. C. Blum, and A. Roli Metaheuristics in combinatorial optimization: overview and conceptual comparison ACM Comput. Surv. 35 3 2003 268 308
6. X.-S. Yang Optimization and metaheuristic algorithms in engineering X.-S. Yang, A.H. Gandomi, S. Talatahari, A.H. Alavi, Metaheuristics in Water, Geotechnical and Transport Engineering 2013 Elsevier Oxford 1 23
7. A.H. Gandomi, X.-S. Yang, S. Talatahari, and A.H. Alavi Metaheuristic algorithms in modeling and optimization A.H. Gandomi, X.-S. Yang, S. Talatahari, A.H.A. Newnes, Metaheuristic Applications in Structures and Infrastructures 2013 Elsevier Oxford 1 24 10.1016/B978-0-12-398364-0.00001-2 URL http://www.sciencedirect.com/science/article/pii/B9780123983640000012
8. J. Kennedy, and R. Eberhart Particle swarm optimization Neural Networks, 1995. Proceedings. IEEE International Conference on, vol. 4 1995 1942 1948
9. M. Dorigo, V. Maniezzo, and A. Colorni Ant system: optimization by a colony of cooperating agents IEEE Trans. Syst. Man Cybern. B: Cybern. 26 1 1996 29 41
10. S.-C. Chu, P.-W. Tsai, and J.-S. Pan Cat swarm optimization Q. Yang, G. Webb, PRICAI 2006: Trends in Artificial Intelligence, Vol. 4099 of Lecture Notes in Computer Science 2006 Springer Berlin, Heidelberg 854 858
11. X.-S. Yang, and S. Deb Cuckoo search via levy flights Nature Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on 2009 210 214
12. W.-T. Pan A new fruit fly optimization algorithm: taking the financial distress model as an example Knowl. Based Syst. 26 2012 69 74
13. K. Krishnanand, and D. Ghose Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions Swarm Intell. 3 2 2009 87 124
14. A.H. Gandomi, and A.H. Alavi Krill herd: a new bio-inspired optimization algorithm Commun. Nonlinear Sci. Numer. Simul. 17 12 2012 4831 4845
15. H. Duan, and P. Qiao Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning Int. J. Intell. Comput. Cybern. 7 1 2014 24 37
16. J. Bansal, H. Sharma, S. Jadon, and M. Clerc Spider monkey optimization algorithm for numerical optimization Memet. Comput. 6 1 2014 31 47
17. C. Sur, S. Sharma, and A. Shukla Egyptian vulture optimization algorithm - a new nature inspired meta-heuristics for knapsack problem P. Meesad, H. Unger, S. Boonkrong, The Ninth International Conference on Computing and Information Technology (IC2IT2013). Vol. 209 of Advances in Intelligent Systems and Computing 2013 Springer Berlin, Heidelberg 227 237
18. X.-S. Yang A new metaheuristic bat-inspired algorithm J. Gonzlez, D. Pelta, C. Cruz, G. Terrazas, N. Krasnogor, Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), Vol. 284 of Studies in Computational Intelligence 2010 Springer Berlin, Heidelberg 65 74
19. W. Feng Gao, and S. Yang Liu A modified artificial bee colony algorithm Comput. Oper. Res. 39 3 2012 687 697 10.1016/j.cor.2011.06.007
20. K. Tan, S. Chiam, A. Mamun, and C. Goh Balancing exploration and exploitation with adaptive variation for evolutionary multi-objective optimization Eur. J. Oper. Res. 197 2 2009 701 713 10.1016/j.ejor.2008.07.025
21. I. F. Jr., D. Fister, X.-S. Yang, A hybrid bat algorithm, CoRR abs/1303.6310.
22. A. Mehrabian, and C. Lucas A novel numerical optimization algorithm inspired from weed colonization Ecol. Inf. 1 4 2006 355 366 10.1016/j.ecoinf.2006.07.003
23. D. Karaboga, and B. Akay A comparative study of artificial bee colony algorithm Appl. Math. Comput. 214 1 2009 108 132 10.1016/j.amc.2009.03.090
24. L.C. Cagnina, and S.C. Esquivel Solving engineering optimization problems with the simple constrained particle swarm optimizer Informatica 32 3 2008 319 326
25. A. Gandomi, and X.-S. Yang Benchmark problems in structural optimization S. Koziel, X.-S. Yang, Computational Optimization, Methods and Algorithms, Vol. 356 of Studies in Computational Intelligence 2011 Springer Berlin, Heidelberg 259 281
26. A.H. Gandomi, and X.-S. Yang Chaotic bat algorithm J. Comput. Sci. 5 2 2014 224 232
27. Z. Geem, J. Kim, and G. Loganathan A new heuristic optimization algorithm: harmony search Simulation 76 2 2001 60 68
28. G. Wang, and L. Guo A novel hybrid bat algorithm with harmony search for global numerical optimization J. Appl. Math. 2013 2013 21
29. R.Y.M. Nakamura, L.A.M. Pereira, K.A. Costa, D. Rodrigues, J.P. Papa, and X.S. Yang BBA: a binary bat algorithm for feature selection Graphics, Patterns and Images (SIBGRAPI), 2012 25th SIBGRAPI Conference on 2012 291 297
30. L. Li, and Y. Zhou A novel complex-valued bat algorithm Neural Comput. Appl. 2014 1 13
31. E. Ali Optimization of power system stabilizers using {BAT} search algorithm Int. J. Electr. Power Energy Syst. 61 2014 683 690
32. O. Hasancebi, T. Teke, and O. Pekcan A bat-inspired algorithm for structural optimization Comput. Struct. 128 2013 77 90 10.1016/j.compstruc.2013.07.006
33. O. Hasancebiebi, and S. Carbas Bat inspired algorithm for discrete size optimization of steel frames Adv. Eng. Softw. 67 2014 173 185 10.1016/j.advengsoft.2013.10.003
34. C.-H.Y.H.-L.T. Jiann-Horng Lin, and Chao-Wei Chou A chaotic levy flight bat algorithm for parameter estimation in nonlinear dynamic biological systems J. Comput. Inf. Technol. 2 2 2012 56 63
35. J.-H. Lin, C.-W. Chou, C.-H. Yang, and H.-L. Tsai A novel bat algorithm based on differential operator and lvy flights trajectory Comput. Intell. Neurosci. 2013 2013 13
36. A. Baziar, A. Kavoosi-Fard, and J. Zare A novel self adaptive modification approach based on bat algorithm for optimal management of renewable MG J. Intell. Learn. Syst. Appl. 5 1 2013 11 18
37. A.M. Taha, and A. Tang Bat algorithm for rough set attribute reduction J. Theor. Appl. Inf. Technol. 51 1 2013 1 8
38. P.-W. Tsai, J.S. Pan, B.Y. Liao, M.J. Tsai, and V. Istanda Bat algorithm inspired algorithm for solving numerical optimization problems Appl. Mech. Mater. 148 2012 134 137
39. I. Fister Jr., D. Fister, and I. Fister Differential evolution strategies with random forest regression in the bat algorithm Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation, GECCO '13 Companion 2013 1703 1706
40. S. YIlmaz, E.U. Kucuksille, and Y. Cengiz Modified bat algorithm Elektron. Elektrotech. 20 2 2014 71 78
41. X. Cai, L. Wang, Q. Kang, and W. Qidi Bat algorithm with Gaussian walk Int. J. Bio-Inspired Comput. 6 3 2014 166 174
42. S. Sabba, and S. Chikhi A discrete binary version of bat algorithm for multidimensional knapsack problem Int. J. Bio-Inspired Comput. 6 2 2014 140 152
43. S. Tabatabaei A new stochastic framework for optimal generation scheduling considering wind power sources J. Intell. Fuzzy Syst. 26 3 2014 1571 1579
44. J. Guo, Y. Gao, and G. Cui The navigation of mobile robot based on hybrid dijkstra algorithm J. Comput. Inf. Syst. 10 9 2014 3879 3886
45. X.-S. Yang, and A. Hossein Gandomi Bat algorithm: a novel approach for global engineering optimization Eng. Comput. 29 5 2012 464 483
46. A. Gandomi, X.-S. Yang, A. Alavi, and S. Talatahari Bat algorithm for constrained optimization tasks Neural Comput. Appl. 22 6 2013 1239 1255 10.1007/s00521-012-1028-9
47. X.S. Yang, S. Deb, and S. Fong Bat algorithm is better than intermittent search strategy J. Multiple-Valued Logic Soft Comput. 22 3 2014 223 237
48. W. Peres, E.J. de Oliveira, J.A.P. Filho, and I.C. da Silva Junior Coordinated tuning of power system stabilizers using bio-inspired algorithms Int. J. Electr. Power Energy Syst. 64 2015 419 428
49. M. Sathya, and M.M.T. Ansari Load frequency control using bat inspired algorithm based dual mode gain scheduling of {PI} controllers for interconnected power system Int. J. Electr. Power Energy Syst. 64 2015 365 374
50. D. Rodrigues, L.A. Pereira, R.Y. Nakamura, K.A. Costa, X.-S. Yang, A.N. Souza, and J.P. Papa A wrapper approach for feature selection based on bat algorithm and optimum-path forest Expert Syst. Appl. 41 5 2014 2250 2258
51. T. Bora, L. Coelho, and L. Lebensztajn Bat-inspired optimization approach for the brushless dc wheel motor problem IEEE Trans. Magn. 48 2 2012 947 950
52. S. Biswal, A. Barisal, A. Behera, and T. Prakash Optimal power dispatch using bat algorithm Energy Efficient Technologies for Sustainability (ICEETS), 2013 International Conference on 2013 1018 1023
53. A.S. Koffka Khan A comparison of ba, ga, pso, bp and lm for training feed forward neural networks in e-learning context Int. J. Intell. Syst. Appl. 4 7 2012 23 29
54. S. Akhtar, A.R. Ahmad, and E.M. Abdel-Rahman A metaheuristic bat-inspired algorithm for full body human pose estimation Computer and Robot Vision (CRV), 2012 Ninth Conference on 2012 369 375
55. S. Sakthivel, R. Natarajan, and P. Gurusamy Application of bat optimization algorithm for economic load dispatch considering valve point effects Int. J. Comput. Appl. 67 11 2013 35 39
56. M. Marichelvam, T. Prabaharan, Y. Xin-She, and M. Geetha Solving hybrid flow shop scheduling problems using bat algorithm Int. J. Logist. Econ. Glob. 5 1 2013 15 29
57. P. Musikapun, and P. Pongcharoen Solving multi-stage multi-machine multi-product scheduling problem using bat algorithm Int. Proc. Econ. Dev. Res. 35 2012 98 102
58. X. Cai, W. Li, L. Wang, Q. Kang, Q. Wu, and X. Huang Bat algorithm with Gaussian walk for directing orbits of chaotic systems Int. J. Comput. Sci. Math. 5 2 2014 198 208
59. S. Gholizadeh, and A.M. Shahrezaei Optimal placement of steel plate shear walls for steel frames by bat algorithm Struct. Des. Tall Spec. Build. 24 1 2014 1 18
60. M. Fenton Bat natural history and echolocation R. Brigham, K. Elisabeth, J. Gareth, P. Stuart, A. Herman, Bat Echolocation Research Tools, Techniques and Analysis 2004 Bat Conservation International 2 6
61. Y. Shi, and R. Eberhart A modified particle swarm optimizer Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Conference on 1998 69 73
62. S. YIlmaz, and E.U. Kucuksille Improved bat algorithm (IBA) on continuous optimization problems Lect. Notes Softw. Eng. 1 3 2013 279 283
63. A. Nickabadi, M.M. Ebadzadeh, and R. Safabakhsh A novel particle swarm optimization algorithm with adaptive inertia weight Appl. Soft Comput. 11 4 2011 3658 3670 10.1016/j.asoc.2011.01.037
64. A.H. Gandomi Interior search algorithm (ISA): A novel approach for global optimization ISA Trans. 53 4 2014 1168 1183 10.1016/j.isatra.2014.03.018 Disturbance estimation and mitigation
65. A. Gandomi, and X.-S. Yang Evolutionary boundary constraint handling scheme Neural Comput. Appl. 21 6 2012 1449 1462
66. I. Fister Jr., D. Fister, and I. Fister Differential evolution strategies with random forest regression in the bat algorithm Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation, GECCO '13 Companion 2013 1703 1706
67. H. Bernardino, I. Barbosa, and A. Lemonge A hybrid genetic algorithm for constrained optimization problems in mechanical engineering Evolutionary Computation, 2007. CEC 2007. IEEE Congress on 2007 646 653
68. H. Bernardino, I. Barbosa, A. Lemonge, and L. Fonseca A new hybrid AIS-GA for constrained optimization problems in mechanical engineering Evolutionary Computation, 2008. CEC 2008 (IEEE World Congress on Computational Intelligence). IEEE Congress on 2008 1455 1462 10.1109/CEC.2008.4630985
69. E. Mezura-Montes, and B. Hernández-Ocaña Bacterial foraging for engineering design problems: Preliminary results Proceedings of the Fourth Mexican congress on evolutionary computation (COMCEV 2008) 2008
70. M. Zhang, W. Luo, and X. Wang Differential evolution with dynamic stochastic selection for constrained optimization Inf. Sci. 178 15 2008 3043 3074 10.1016/j.ins.2008.02.014 Nature inspired problem-solving
71. E. Zahara, and Y.-T. Kao Hybrid Nelder-Mead simplex search and particle swarm optimization for constrained engineering design problems Expert Systems with Applications 36 2, Part 2 2009 3880 3886 10.1016/j.eswa.2008.02.039 URL http://www.sciencedirect.com/science/article/pii/S0957417408001735
72. J. Zhang, C. Liang, Y. Huang, J. Wu, and S. Yang An effective multiagent evolutionary algorithm integrating a novel roulette inversion operator for engineering optimization Appl. Math. Comput. 211 2 2009 392 416 10.1016/j.amc.2009.01.048
73. V.S. Aragón, S.C. Esquivel, and C.A.C. Coello A modified version of a t-cell algorithm for constrained optimization problems Int. J. Numer. Methods Eng. 84 3 2010 351 378 10.1002/nme.2904
74. A. Kaveh, and S. Talatahari A novel heuristic optimization method: charged system search Acta Mech. 213 3-4 2010 267 289 10.1007/s00707-009-0270-4
75. D. Datta, and J.R. Figueira A real-integer-discrete-coded particle swarm optimization for design problems Appl. Soft Comput. 11 4 2011 3625 3633 10.1016/j.asoc.2011.01.034 URL http://www.sciencedirect.com/science/article/pii/S1568494611000445
76. A.H. Gandomi, X.-S. Yang, and A.H. Alavi Mixed variable structural optimization using firefly algorithm Comput. Struct. 89 23-24 2011 2325 2336 10.1016/j.compstruc.2011.08.002
77. A. Sadollah, A. Bahreininejad, H. Eskandar, and M. Hamdi Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems Appl. Soft Comput. 13 5 2013 2592 2612 10.1016/j.asoc.2012.11.026 URL http://www.sciencedirect.com/science/article/pii/S1568494612005108
78. Q. He, and L. Wang An effective co-evolutionary particle swarm optimization for constrained engineering design problems Eng. Appl. Artif. Intell. 20 1 2007 89 99 10.1016/j.engappai.2006.03.003
79. Y.-L. Hsu, and T.-C. Liu Developing a fuzzy proportional-derivative controller optimization engine for engineering design optimization problems Eng. Optim. 39 6 2007 679 700 10.1080/03052150701252664
80. E. Mezura-Montes, and C.A.C. Coello An empirical study about the usefulness of evolution strategies to solve constrained optimization problems Int. J. Gen. Syst. 37 4 2008 443 473 10.1080/03081070701303470
81. L. dos Santos Coelho Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems Expert Syst. Appl. 37 2 2010 1676 1683 10.1016/j.eswa.2009.06.044
82. M. Mahdavi, M. Fesanghary, and E. Damangir An improved harmony search algorithm for solving optimization problems Appl. Math. Comput. 188 2 2007 1567 1579 10.1016/j.amc.2006.11.033
83. E. Mezura-Montes, C.A. Coello Coello, J. Velázquez-Reyes, and L. Muñoz-Dávila Multiple trial vectors in differential evolution for engineering design Eng. Optim. 39 5 2007 567 589 10.1080/03052150701364022
84. J.J. Liang, B.Y. Qu, and P.N. Suganthan Problem definitions and evaluation criteria for the CEC 2013 special session and competition on single objective real-parameter numerical optimization, Tech. rep. 2013 Computational Intelligence Laboratory, Zhengzhou University
85. J.J. Liang, B.Y. Qu, and P.N. Suganthan Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization, Technical Report 2013 Computational Intelligence Laboratory, Zhengzhou University