Biogeographic harmony search for emergency air transportation
详细信息 查看全文
- 作者：Yu-Jun Zheng ; Min-Xia Zhang ; Bei Zhang
- 关键词：Biogeography ; based optimization (BBO) ; Harmony search (HS) ; Hybrid method ; Emergency air transportation
- 刊名：Soft Computing - A Fusion of Foundations, Methodologies and Applications
- 出版年：2016
- 出版时间：March 2016
- 年：2016
- 卷：20
- 期：3
- 页码：967-977
- 全文大小：543 KB
- 参考文献：Al-Betar MA, Khader AT (2012) A harmony search algorithm for university course timetabling. Ann Oper Res 194(1):3–31. doi:10.1007/s10479-010-0769-z CrossRef MathSciNet MATH
Basturk B, Karaboga D (2006) An artificial bee colony (abc) algorithm for numeric function optimization. In: IEEE swarm intelligence symposium, pp 12–14
Beyer HG, Schwefel HP (2002) Evolution strategies—a comprehensive introduction. Nat Comput 1(1):3–52. doi:10.1023/A:1015059928466 CrossRef MathSciNet MATH
Bhattacharya A, Chattopadhyay P (2010) Biogeography-based optimization for different economic load dispatch problems. IEEE Trans Power Syst 25(2):1064–1077. doi:10.1109/TPWRS.2009.2034525 CrossRef
Boussaïd I, Chatterjee A, Siarry P, Ahmed-Nacer M (2011) Two-stage update biogeography-based optimization using differential evolution algorithm (DBBO). Comput Oper Res 38(8):1188–1198. doi:10.1016/j.cor.2010.11.004 CrossRef MathSciNet MATH
Boussaïd I, Chatterjee A, Siarry P, Ahmed-Nacer M (2012) Biogeography-based optimization for constrained optimization problems. Comput Oper Res 39(12):3293–3304. doi:10.1016/j.cor.2012.04.012 CrossRef MathSciNet
Chakraborty P, Roy GG, Das S, Jain D, Abraham A (2009) An improved harmony search algorithm with differential mutation operator. Fundam Inform 95(4):401–426. doi:10.3233/FI-2009-157 MathSciNet MATH
Cheng MY, Huang KY, Chen HM (2012) Dynamic guiding particle swarm optimization with embedded chaotic search for solving multidimensional problems. Optim Lett 6(4):719–729. doi:10.1007/s11590-011-0297-z CrossRef MATH
Degertekin S (2008) Optimum design of steel frames using harmony search algorithm. Struct Multidiscip Optim 36(4):393–401. doi:10.1007/s00158-007-0177-4 CrossRef
Du D, Simon D, Ergezer M (2009) Biogeography-based optimization combined with evolutionary strategy and immigration refusal. In: IEEE international conference on systems, man and cybernetics, pp 997–1002. doi:10.1109/ICSMC.2009.5346055
Forsati R, Haghighat A, Mahdavi M (2008) Harmony search based algorithms for bandwidth-delay-constrained least-cost multicast routing. Comput Commun 31(10):2505–2519. doi:10.1016/j.comcom.2008.03.019 CrossRef
Gao X, Wang X, Ovaska S (2010) A harmony search-based differential evolution method. In: IEEE 13th international conference on computational science and engineering, pp 333–339. doi:10.1109/CSE.2010.50
Gao X, Wang X, Ovaska S, Zenger K (2012a) A hybrid optimization method of harmony search and opposition-based learning. Eng Optim 44(8):895–914. doi:10.1080/0305215X.2011.628387 CrossRef
Gao X, Wang X, Zenger K, Wang X (2012b) A bee foraging-based memetic harmony search method. In: IEEE international conference on systems, man, and cybernetics, pp 184–189. doi:10.1109/ICSMC.2012.6377697
Gao XZ, Wang X, Zenger K (2013) A modified harmony search method for wind generator design. Int J Bio-Inspired Comput 5(6):336–349. doi:10.1504/IJBIC.2013.058911 CrossRef
Geem ZW (2006) Optimal cost design of water distribution networks using harmony search. Eng Optim 38(3):259–277CrossRef
Geem ZW (2009) Particle-swarm harmony search for water network design. Eng Optim 41(4):297–311. doi:10.1080/03052150802449227 CrossRef
Geem ZW, Sim KB (2010) Parameter-setting-free harmony search algorithm. Appl Math Comput 217(8):3881–3889. doi:10.1016/j.amc.2010.09.049 CrossRef MathSciNet MATH
Geem ZW, Kim JH, Loganathan G (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68. doi:10.1177/003754970107600201 CrossRef
Gong W, Cai Z, Ling CX (2010) DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization. Soft Comput 15(4):645–665. doi:10.1007/s00500-010-0591-1 CrossRef
Guo L, Wang GG, Wang H, Wang D (2013) An effective hybrid firefly algorithm with harmony search for global numerical optimization. Sci World J 2013:9. doi:10.1155/2013/125625 . (article ID: 125625)
Kennedy J, Eberhart R (1995) Particle swarm optimization. IEEE Int Conf Neural Netw 4:1942–1948. doi:10.1109/ICNN.1995.488968 CrossRef
Laskari EC, Parsopoulos KE, Vrahatis MN (2002) Particle swarm optimization for integer programming. IEEE Congr Evol Comput IEEE 2:1582–1587. doi:10.1109/CEC.2002.1004478
Lee KS, Geem ZW (2005) A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput Methods Appl Mech Eng 194(36):3902–3933CrossRef MATH
Li X, Yin M (2012) Multi-operator based biogeography based optimization with mutation for global numerical optimization. Comput Math Appl 64(9):2833–2844. doi:10.1016/j.camwa.2012.04.015 CrossRef MathSciNet MATH
Liang JJ, Qu BY, Suganthan PN (2014) Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Tech. Rep. 201311, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China
Lohokare M, Pattnaik S, Panigrahi B, Das S (2013) Accelerated biogeography-based optimization with neighborhood search for optimization. Appl Soft Comput 13(5):2318–2342. doi:10.1016/j.asoc.2013.01.020 CrossRef
Ma H, Simon D (2011) Blended biogeography-based optimization for constrained optimization. Eng Appl Artif Intell 24(3):517–525. doi:10.1016/j.engappai.2010.08.005 CrossRef
Ma H, Fei M, Ding Z, Jin J (2012) Biogeography-based optimization with ensemble of migration models for global numerical optimization. In: IEEE congress on evolutionary computation, pp 1–8. doi:10.1109/CEC.2012.6252930
Mahdavi M, Fesanghary M, Damangir E (2007) An improved harmony search algorithm for solving optimization problems. Appl Math Comput 188(2):1567–1579. doi:10.1016/j.amc.2006.11.033 CrossRef MathSciNet MATH
Omran MG, Mahdavi M (2008) Global-best harmony search. Appl Math Comput 198(2):643–656. doi:10.1016/j.amc.2007.09.004 CrossRef MathSciNet MATH
Omran MG, Geem ZW, Salman A (2011) Improving the performance of harmony search using opposition-based learning and quadratic interpolation. Int J Math Model Numer Optim 2(1):28–50MATH
Pandi VR, Panigrahi BK, Das S, Cui Z (2010) Dynamic economic load dispatch with wind energy using modified harmony search. Int J Bio-Inspired Comput 2(3):282–289CrossRef
Rolland E, Patterson RA, Ward K, Dodin B (2010) Decision support for disaster management. Oper Manag Res 3(1–2):68–79. doi:10.1007/s12063-010-0028-0 CrossRef
Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713. doi:10.1109/TEVC.2008.919004 CrossRef
Singh U, Kumar H, Kamal T (2010) Design of Yagi–Uda antenna using biogeography based optimization. IEEE Trans Anten Propag 58(10):3375–3379. doi:10.1109/TAP.2010.2055778 CrossRef
Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359. doi:10.1023/A:1008202821328 CrossRef MathSciNet MATH
Tizhoosh H (2005) Opposition-based learning: a new scheme for machine intelligence. Comput Intell Modell Control Autom 1:695–701. doi:10.1109/CIMCA.2005.1631345
Wang CM, Huang YF (2010) Self-adaptive harmony search algorithm for optimization. Expert Syst Appl 37(4):2826–2837. doi:10.1016/j.eswa.2009.09.008 CrossRef
Wang G, Guo L, Duan H, Wang H, Liu L, Shao M (2013) Hybridizing harmony search with biogeography based optimization for global numerical optimization. J Comput Theor Nanosci 10(10):2312–2322CrossRef
Wang L, Zhou P, Fang J, Niu Q (2011) A hybrid binary harmony search algorithm inspired by ant system. In: IEEE 5th international conference on cybernetics and intelligent systems, pp 153–158. doi:10.1109/ICCIS.2011.6070319
Wu B, Qian C, Ni W, Fan S (2012) Hybrid harmony search and artificial bee colony algorithm for global optimization problems. Comput Math Appl 64(8):2621–2634. doi:10.1016/j.camwa.2012.06.026 CrossRef MathSciNet MATH
Yang GP, Liu SY, Zhang JK, Feng QX (2013) Control and synchronization of chaotic systems by an improved biogeography-based optimization algorithm. Appl Intell 39(1):132–143. doi:10.1007/s10489-012-0398-0 CrossRef
Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: González JR, Pelta DA, Cruz C, Terrazas G, Krasnogor N (eds) Nature inspired cooperative strategies for optimization, studies in computational intelligence, vol 284. Springer, Berlin, pp 65–74. doi:10.1007/978-3-642-12538-6_6
Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102. doi:10.1109/4235.771163 CrossRef
Yuan X, Zhao J, Yang Y, Wang Y (2014) Hybrid parallel chaos optimization algorithm with harmony search algorithm. Appl Soft Comput 17(4):12–22. doi:10.1016/j.asoc.2013.12.016 CrossRef
Yuan Y, Xu H, Yang J (2013) A hybrid harmony search algorithm for the flexible job shop scheduling problem. Appl Soft Comput 13(7):3259–3272. doi:10.1016/j.asoc.2013.02.013 CrossRef
Zheng YJ, Ling HF (2013) Emergency transportation planning in disaster relief supply chain management: a cooperative fuzzy optimization approach. Soft Comput 17(7):1301–1314. doi:10.1007/s00500-012-0968-4
Zheng YJ, Ling HF, Shi HH, Chen HS, Chen SY (2014a) Emergency railway wagon scheduling by hybrid biogeography-based optimization. Comput Oper Res 43(3):1–8. doi:10.1016/j.cor.2013.09.002 CrossRef MathSciNet
Zheng YJ, Ling HF, Wu XB, Xue JY (2014b) Localized biogeography-based optimization. Soft Comput 18(11):2323–2334. doi:10.1007/s00500-013-1209-1 CrossRef
Zheng YJ, Ling HF, Xue JY (2014) Ecogeography-based optimization: enhancing biogeography-based optimization with ecogeographic barriers and differentiations. Comput Oper Res 50:115–127. doi:10.1016/j.cor.2014.04.013 CrossRef
Zheng YJ, Chen SY, Ling HF (2015a) Evolutionary optimization for disaster relief operations: a survey. Appl Soft Comput (in press). doi:10.1016/j.asoc.2014.09.041
Zheng YJ, Ling HF, Chen SY, Xue JY (2015b) A hybrid neuro-fuzzy network based on differential biogeography-based optimization for online population classification in earthquakes. IEEE Trans Fuzzy Syst (in press). doi:10.1109/TFUZZ.2014.2337938
Zou D, Gao L, Wu J, Li S, Li Y (2010) A novel global harmony search algorithm for reliability problems. Comput Ind Eng 58(2):307–316. doi:10.1016/j.cie.2009.11.003 CrossRef - 作者单位：Yu-Jun Zheng (1)
Min-Xia Zhang (2)
Bei Zhang (1)
1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, China
2. College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China - 刊物类别：Engineering
- 刊物主题：Numerical and Computational Methods in Engineering
Theory of Computation
Computing Methodologies
Mathematical Logic and Foundations
Control Engineering - 出版者：Springer Berlin / Heidelberg
- ISSN：1433-7479
文摘
Harmony search (HS) and biogeography-based optimization (BBO) are two metaheuristic optimization methods which have demonstrated effectiveness on a wide variety of optimization problems. The paper proposes a new hybrid biogeographic harmony search (BHS) method, which integrates the blended migration operator of BBO with HS to enrich harmony diversity, and thus achieves a much better balance between exploration and exploitation. We then apply the BHS method to an emergency air transportation problem, and show that the proposed method is very competitive with the state-of-the-art BBO, HS, and other comparative algorithms on a set of problem instances from real-world disaster relief operations in China. Keywords Biogeography-based optimization (BBO) Harmony search (HS) Hybrid method Emergency air transportation