Hybrid multiple objective artificial bee colony with differential evolution for the time-cost-quality tradeoff problem

Knowledge-Based Systems

Volumn 74, Issue , 2015, Pages 176-186

  Tran, Duc Hoc ^a   Cheng, Min Yuan ^a   Cao, Minh Tu ^a  

^a NATIONAL TAIWAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

Author keywords

Artificial bee colony; Construction management; Differential evolution; Multi objective analysis; Time cost quality tradeoff

Indexed keywords

ALGORITHMS; COST BENEFIT ANALYSIS; COSTS; GENETIC ALGORITHMS; MULTIOBJECTIVE OPTIMIZATION; OPTIMIZATION; PARTICLE SWARM OPTIMIZATION (PSO); PROJECT MANAGEMENT; SCREENING;

ARTIFICIAL BEE COLONIES; CONSTRUCTION MANAGEMENT; DIFFERENTIAL EVOLUTION; MULTI-OBJECTIVE ANALYSIS; TIME COST;

EVOLUTIONARY ALGORITHMS;

EID: 84926247898     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2014.11.018     Document Type: Article

References (57)

1. B. Zahraie, M. Tavakolan, Stochastic time-cost-resource utilization optimization using nondominated sorting genetic algorithm and discrete fuzzy sets, J. Construct. Eng. Manage. 135 (2009) 1162-1171.
2. L. Feng, Burns Liu, Using genetic algorithms to solve construction time-cost trade-off problems, J. Comput. Civil Eng. 11 (1997) 184-189.
3. T. Hegazy, Optimization of construction time-cost trade-off analysis using genetic algorithms, Can. J. Civil Eng. 26 (1999) 685-697.
4. I. Yang, Using elitist particle swarm optimization to facilitate bicriterion time-cost trade-off analysis, J. Construct. Eng. Manage. 133 (2007) 498-505.
5. Y. Xiong, Y. Kuang, Applying an ant colony optimization algorithm-based multiobjective approach for time-cost trade-off, J. Construct. Eng. Manage. 134 (2008) 153-156.
6. Z. Geem, Multiobjective optimization of time-cost trade-off using harmony search, J. Construct. Eng. Manage. 136 (2010) 711-716.
7. H. Zhang, F. Xing, Fuzzy-multi-objective particle swarm optimization for time-cost-quality tradeoff in construction, Autom. Construct. 19 (2010) 1067-1075.
8. W. Hu, X. He, An Innovative time-cost-quality tradeoff modeling of building construction project based on resource allocation, Scient. World J. 2014 (2014) 10.
9. A.J.G. Babu, N. Suresh, Project management with time, cost, and quality considerations, Euro. J. Operat. Res. 88 (1996) 320-327.
10. D.B. Khang, Y.M. Myint, Time, cost and quality trade-off in project management: a case study, Int. J. Project Manage. 17 (1999) 249-256.
11. K. El-Rayes, A. Kandil, Time-cost-quality trade-off analysis for highway construction, J. Construct. Eng. Manage. 131 (2005) 477-486.
12. L. Zhang, J. Du, S. Zhang, Solution to the time-cost-quality trade-off problem in construction projects based on immune genetic particle swarm optimization, J. Manage. Eng. 30 (2014) 163-172.
13. S. Mungle, L. Benyoucef, Y.-J. Son, M.K. Tiwari, A fuzzy clustering-based genetic algorithm approach for time-cost-quality trade-off problems: a case study of highway construction project, Eng. Appl. Artif. Intell. 26 (2013) 1953-1966.
14. D. Karaboga, B. Basturk, On the performance of artificial bee colony (ABC) algorithm, Appl. Soft Comput. 8 (2008) 687-697.
15. D. Karaboga, B. Basturk, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm, J. Global Optimiz. 39 (2007) 459-471.
16. D. Karaboga, B. Akay, A comparative study of artificial bee colony algorithm, Appl. Math. Comput. 214 (2009) 108-132.
17. R. Akbari, R. Hedayatzadeh, K. Ziarati, B. Hassanizadeh, A multi-objective artificial bee colony algorithm, Swarm Evolution. Comput. 2 (2012) 39-52.
18. Y. Li, Y. Wang, B. Li, A hybrid artificial bee colony assisted differential evolution algorithm for optimal reactive power flow, Int. J. Electr. Power Energy Syst. 52 (2013) 25-33.
19. R.M. Storn, K. Price, Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces, J. Global Optimiz. 11 (1997) 341-359.
20. S. Das, P.N. Suganthan, Differential evolution: a survey of the state-of-the-art, IEEE Trans. Evolution. Comput. 15 (2011) 4-31.
21. A. Zhou, B.-Y. Qu, H. Li, S.-Z. Zhao, P.N. Suganthan, Q. Zhang, Multiobjective evolutionary algorithms: a survey of the state of the art, Swarm Evolution. Comput. 1 (2011) 32-49.
22. W. Gao, S. Liu, Improved artificial bee colony algorithm for global optimization, Inform. Process. Lett. 111 (2011) 871-882.
23. X. Li, M. Yin, Hybrid differential evolution with biogeography-based optimization for design of a reconfigurable antenna array with discrete phase shifters, Int. J. Anten. Propag. 2011 (2011) 12.
24. X. Li, M. Yin, Hybrid differential evolution with artificial bee colony and its application for design of a reconfigurable antenna array with discrete phase shifters, IET Microwav., Anten. Propag. 6 (2012) 1573-1582.
25. V. Plevris, M. Papadrakakis, A hybrid particle swarm-gradient algorithm for global structural optimization, Comp.-Aided Civil Infrastruct. Eng. 26 (2011) 48-68.
26. S. Kumar, V.K. Sharma, R. Kumari, A novel hybrid crossover based artificial bee colony algorithm for optimization problem, Int. J. Comp. Appl. 82 (2013) 18-25.
27. D. González-Álvarez, M. Vega-Rodríguez, Hybrid multiobjective artificial bee colony with differential evolution applied to Motif finding, in: L. Vanneschi, W. Bush, M. Giacobini (Eds.), Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, Springer, Berlin Heidelberg, 2013, pp. 68-79.
28. A. Rajasekhar, A. Abraham, M. Pant, A hybrid differential artificial bee colony algorithm based tuning of fractional order controller for permanent magnet synchronous motor drive, Int. J. Mach. Learn. Cybernet. (2012) 1-11.
29. J. Lu, G. Zhang, D. Ruan, Multi-objective Group Decision Making: Methods, Software and Applications With Fuzzy Set Techniques, Imperial College Press, 2007.
30. K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evolution. Comput. 6 (2002) 182-197.
31. R.T. Marler, J.S. Arora, Survey of multi-objective optimization methods for engineering, Struct. Multidisc. Optimiz. 26 (2004) 369-395.
32. C. Min-Yuan, T. Duc-Hoc, Two-phase differential evolution for the multiobjective optimization of time-cost tradeoffs in resource-constrained construction projects, IEEE Trans. Eng. Manage. 61 (2014) 450-461.
33. M. Cheng, D. Tran, Opposition-based multiple-objective differential evolution to solve the time-cost-environment impact trade-off problem in construction projects, J. Comput. Civil Eng. 0 (2014) 04014074.
34. D. Lei, Multi-objective production scheduling: a survey, Int. J. Adv. Manuf. Technol. 43 (2009) 926-938.
35. F. Ramezani, J. Lu, F. Hussain, Task scheduling optimization in cloud computing applying multi-objective particle swarm optimization, in: S. Basu, C. Pautasso, L. Zhang, X. Fu (Eds.), Service-Oriented Computing, Springer, Berlin Heidelberg, 2013, pp. 237-251.
36. M.G. Castillo Tapia, C.A. Coello Coello, Applications of multi-objective evolutionary algorithms in economics and finance: a survey, in: IEEE Congress on Evolutionary Computation, 2007, CEC 2007, 2007, pp. 532-539.
37. K.V. Price, R.M. Storn, J.A. Lampinen, Differential Evolution A Practical Approach to Global Optimization, Springer-Verlag, Berlin Heidelberg, 2005.
38. D. Karaboga, An Idea Based on Honey Bee Swarm for Numerical Optimization, Technical Report-TR06, Erciyes University, Kayseri/Turkey, 2005.
39. A.L.a. Bolaji, A.T. Khader, M.A. Al-Betar, M.A. Awadallah, Artificial bee colony algorithm, its variants and applications: a survey, J. Theoret. Appl. Inform. Technol. 47 (2013) 434-459.
40. H. Talbi, M. Batouche, Hybrid particle swarm with differential evolution for multimodal image registration, in: IEEE International Conference on Industrial Technology, 2004, IEEE ICIT '04, 2004, vol. 1563, 2004, pp. 1567-1572.
41. C. Zhang, J. Ning, S. Lu, D. Ouyang, T. Ding, A novel hybrid differential evolution and particle swarm optimization algorithm for unconstrained optimization, Operat. Res. Lett. 37 (2009) 117-122.
42. S.N. Omkar, J. Senthilnath, R. Khandelwal, G. Narayana Naik, S. Gopalakrishnan, Artificial Bee Colony (ABC) for multi-objective design optimization of composite structures, Appl. Soft Comput. 11 (2011) 489-499.
43. F. Kang, J. Li, Z. Ma, Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions, Inform. Sci. 181 (2011) 3508-3531.
44. M.S. Kiran, M. Gündüz, A novel artificial bee colony-based algorithm for solving the numerical optimization problems, Int. J. Innov. Comput., Inform. Control 8 (2012) 6107-6122.
45. A. Rajasekhar, M. Pant, A. Abraham, A hybrid differential artificial bee algorithm based tuning of fractional order controller for PMSM drive, in: Third World Congress on Nature and Biologically Inspired Computing (NaBIC), 2011, 2011, pp. 1-6.
46. G. Zhu, S. Kwong, Gbest-guided artificial bee colony algorithm for numerical function optimization, Appl. Math. Comput. 217 (2010) 3166-3173.
47. M.-Y. Cheng, D.-H. Tran, Y.-W. Wu, Using a fuzzy clustering chaotic-based differential evolution with serial method to solve resource-constrained project scheduling problems, Autom. Construct. 37 (2014) 88-97.
48. N. Noman, H. Iba, Accelerating differential evolution using an adaptive local search, IEEE Trans. Evolution. Comput. 12 (2008) 107-125.
49. M.S. Kiran, M. Gündüz, A recombination-based hybridization of particle swarm optimization and artificial bee colony algorithm for continuous optimization problems, Appl. Soft Comput. 13 (2013) 2188-2203.
50. Y.-N. Wang, L.-H. Wu, X.-F. Yuan, Multi-objective self-adaptive differential evolution with elitist archive and crowding entropy-based diversity measure, Soft Comput.-Fus. Found., Methodol. Appl. 14 (2010) 193-209.
51. M. Ali, P. Siarry, M. Pant, An efficient differential evolution based algorithm for solving multi-objective optimization problems, Euro. J. Operat. Res. 217 (2012) 404-416.
52. L.H. Wu, Y.N. Wang, X.F. Yuan, S.W. Zhou, Environmental/economic power dispatch problem using multi-objective differential evolution algorithm, Electr. Power Syst. Res. 80 (2010) 1171-1181.
53. C.M. Fonseca, P.J. Fleming, An overview of evolutionary algorithms in multiobjective optimization, Evol. Comput. 3 (1995) 1-16.
54. E. Zitzler, L. Thiele, M. Laumanns, C.M. Fonseca, V.G.d. Fonseca, Performance assessment of multiobjective optimizers: an analysis and review, Trans. Evol. Comp 7 (2003) 117-132.
55. C.A. Coello Coello, Evolutionary multi-objective optimization: a historical view of the field, IEEE Comput. Intell. Magaz. 1 (2006) 28-36.
56. E. Zitzler, L. Thiele, Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach, IEEE Trans. Evolution. Comput. 3 (1999) 257-271.
57. L. Wang, C. Singh, Reserve-constrained multiarea environmental/economic dispatch based on particle swarm optimization with local search, Eng. Appl. Artif. Intell. 22 (2009) 298-307.