University course timetabling using a new ecogeography-based optimization algorithm

Abdullah S, Burke EK, McColloum B (2005) An investigation of variable neighborhood search for university course timetabling. In: The 2nd multidisciplinary international conference on scheduling: theory and applications, pp 413–427 Abdullah S, Burke EK, McColloum B (2007) A hybrid evolutionary approach to the university course timetabling problem. In: 2007 IEEE congress on evolutionary computation, pp 1764–1768 Ahmed LN, Özcan E, Kheiri A (2015) Solving high school timetabling problems worldwide using selection hyper-heuristics. Expert Syst Appl 42(13):5463–5471 Aladag CH, Hocaoglu GA, Basaran M (2009) The effect of neighborhood structures on tabu search algorithm in solving course timetabling problem. Expert Syst Appl 36:12349–12356 Alsmadi OMK, Abo-Hammour ZS, Abu-Al-Nadi DI, Algsoon A (2011) A novel genetic algorithm technique for solving university course timetabling problems. In: 7th IEEE international workshop on systems signal processing and their applications, pp 195–198 Alvarez R, Crespo E, Tamarit JM (2002) Design and implementation of a course scheduling system using Tabu search. Eur J Oper Res 137:512–523 Aycan E, Ayav T (2009) Solving the course scheduling problem using simulated annealing. In: 2009 IEEE advance computing conference, pp 462–466 Ayob M, Jaradat G (2009) Hybrid ant colony systems for course timetabling problems. In: 2009 2nd IEEE conference on data mining and optimization, pp 120–126 Babaei H, Karimpour J, Hadidi A (2015) A survey of approaches for university course timetabling problem. Comput Ind Eng 86:43–59 Badoni RP, Gupta DK, Mishra P (2014) A new hybrid algorithm for university course timetabling problem using events based on groupings of students. Comput Ind Eng 78:12–25 Boussaïd I, Chatterjee A, Siarry P, Ahmed-Nacer M (2012) Biogeography-based optimization for constrained optimization problems. Comput Oper Res 39(12):3293–3304 Burke EK, Hyde M, Kendall G et al (2010) A classification of hyper-heuristic approaches. Handbook of metaheuristics. Springer, New York Chen RM, Shih HF (2013) Solving university course timetabling problems using constriction particle swarm optimization with local search. Algorithms 6(2):227–244 Cowling P, Kendall G, Soubeiga E (2001) A hyperheuristic approach to scheduling a sales summit. Practice and theory of automated timetabling III. Springer, Berlin, Heidelberg Dorigo M, Di Caro G (1999) Ant colony optimization: a new meta-heuristic. In: 1999 IEEE congress on evolutionary computation, vol 2, pp 1470–1477 Fonseca GH, Brito SS, Santos HG (2012) A simulated annealing based approach to the high school timetabling problem. In: Yin HJ, Costa J, Barreto G (eds) Intelligent data engineering and automated learning-IDEAL. Springer, Berlin, Heidelberg, pp 540–549 Fonseca GH, Santos HG (2014) Variable neighborhood search based algorithms for high school timetabling. Comput Oper Res 52:203–208 Fonseca GH, Santos HG, Toffolo TM et al (2014) GOAL solver: a hybrid local search based solver for high school timetabling. Ann Oper Res 1–21. doi:10.1007/s10479-014-1685-4 Glover F (1989) Tabu search, part I. ORSA J Comput 1:190–206 Glover F (1990) Tabu search, part II. ORSA J Comput 2:4–32 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 Garey MR, Johnson DS (1979) Computers and intractability: a guide to the theory of NP-completeness. W. H. Freeman, New York Hadidi A (2015) A robust approach for optimal design of plate fin heat exchangers using biogeography based optimization (BBO) algorithm. Appl Energy 150:196–210 Holland JH (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control and artificial intelligence. MIT Press, Cambridge Hopcroft J, Karp RM (1973) An \(n^{5/2}\) algorithm for maximum matchings in bipartite graphs. SIAM J Comput 2:225–231 ITC2007 (2007) Second international timetabling competition. http://www.cs.qub.ac.uk/itc2007/ ITC2011 (2011) Third international timetabling competition. http://www.utwente.nl/ctit/hstt/itc2011/welcome/ Jasper J, Berlin Shaheema S, Berlin Shiny S (2014) Natural image enhancement using a biogeography based optimization enhanced with blended migration operator. Math Probl Eng 2014:1–11 Kennedy J, Eberhart R (1995) Particle swarm optimization. In: 1995 IEEE international conference on neural networks, vol 4. pp 1942–1948 Kennedy J (1999) Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. In: 1999 IEEE congress on evolutionary computation, vol 3. pp 1931–1938 Khonggamnerd P, Innet S (2009) On improvement of effectiveness in automatic university timetabling arrangement with applied genetic algorithm. In: 2009 4th ieee international conference on computer sciences and convergence information technology, pp 1266–1270 Kingston JH (2012) A software library for school timetabling. http://sydney.edu.au/engineering/it/~jeff/khe/ Kirkpatrick S Jr, Gelatt D, Vecchi MP (1983) Optimization by simulated annealing. Science 220:671–680 Lin J (2015) A hybrid discrete biogeography-based optimization for the permutation flow shop scheduling problem. Int J Prod Res 1–10. doi:10.1080/00207543.2015.1094584 Lohokare M, Pattnaik S, Panigrahi B, Das S (2013) Accelerated biogeography-based optimization with neighborhood search for optimization. Appl Soft Comput 13:2318–2342 Lourenço HR, Martin OC, Stützle T (2003) Iterated local search. Springer, New York Ma H (2010) An analysis of the equilibrium of migration models for biogeography-based optimization. Inform Sci 180(18):3444–3464 Ma H, Simon D (2011) Blended biogeography-based optimization for constrained optimization. Eng Appl Artif Intell 24(3):517–525 MacArthur R, Wilson E (1967) The theory of biogeography. Princeton University Press, Princeton Mladenovic N, Hansen P (1997) Variable neighborhood search. Comput Oper Res 24(11):1097–1100 Mühlenbein H, Schlierkamp-Voosen D (1993) Predictive models for the breeder genetic algorithm i. Continuous parameter optimization. Evol Comput 1(1):25–49 Nothegger C, Mayer A, Chwatal A, Raidl GR (2012) Solving the post enrolment course timetabling problem by ant colony optimization. Ann Oper Res 194(1):325–339 Qarouni-Fard D, Najafi-Ardabifi A, Moeinzadeh MH, et al. (2007) Finding feasible timetables with particle swarm optimization. In: 2007 4th IEEE international conference on innovations in information technology, pp 387–391 Shiau DF (2011) A hybrid particle swarm optimization for a university course scheduling problem with flexible preferences. Expert Syst Appl 38(1):235–248 Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713 Socha K, Knowles J, Samples M (2002) A max–min ant system for the university course timetabling problem. In: Dorigo M, Caro GD, Sampels M (eds) Ant Algorithms, Lecture Notes Computer Science, vol 2463, pp 1–13 Soria-Alcaraz JA, Ochoa G, Swan J et al (2014) Effective learning hyper-heuristics for the course timetabling problem. Eur J Oper Res 238(1):77–86 Tamjidy M, Paslar S, Baharudin BHT, Hong TS, Ariffin MKA (2015) Biogeography based optimization (BBO) algorithm to minimise non-productive time during hole-making process. Int J Prod Res 53:1880–1894 Tassopoulos IX, Beligiannis GN (2012) A hybrid particle swarm optimization based algorithm for high school timetabling problems. Appl Soft Comput 12(11):3472–3489 Tuga M, Berretta R, Mendes A (2007) A hybrid simulated annealing with kempe chain neighborhood for the university timetabling problem. In: 2007 6th IEEE/ACIS international conference on computer and information science, pp 400–405 Zhang B, Zhang MX, Qian N (2015) A discrete ecogeography-based optimization algorithm for university course timetabling. In: Tan Y, Shi YH, Buarque F, et al (eds) Advances in swarm and computational intelligence, part II. Lecture notes on computer science, vol 9141, pp 247–257 Zheng YJ, Ling HF, Xue JY (2014a) Ecogeography-based optimization: enhancing biogeography-based optimization with ecogeographic barriers and differentiations. Comput Oper Res 50:115–127 Zheng YJ, Ling HF, Xue JY, Chen SY (2014b) Population classification in fire evacuation: a multiobjective particle swarm optimization approach. IEEE Trans Evol Comput 18:70–81 Zheng YJ, Ling HF, Wu XB, Xue JY (2014c) Localized biogeography-based optimization. Soft Comput 18(11):2323–2334 Zheng YJ, Ling HF, Shi HH, Chen HS, Chen SY (2014d) Emergency railway wagon scheduling by hybrid biogeography-based optimization. Comput Oper Res 43:1–8 Zheng YJ (2015) Water wave optimization: a new nature-inspired metaheuristic. Comput Oper Res 55:1–11 Zheng YJ, Wu XB (2015) Tuning maturity model of ecogeography-based optimization on CEC 2015 single-objective optimization test problems. In: 2015 IEEE congress on evolutionary computation, pp 1018–1024 Zheng YJ, Ling HF, Chen SY, Xue JY (2015) A hybrid neuro-fuzzy network based on differential biogeography-based optimization for online population classification in earthquakes. IEEE Trans Fuzzy Syst 23:1070–1083