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Articles

A novel global Harmony Search method based on Ant Colony Optimisation algorithm

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Pages 215-238 | Received 31 Jan 2014, Accepted 07 Jan 2015, Published online: 19 Mar 2015

References

  • AliM. M., KhompatrapornC., & ZabinskyZ. B. (2005). A numerical evaluation of several stochastic algorithms on selected continuous global optimization test problems. Journal of Global Optimization, 31, 635–672. doi:10.1007/s10898-004-9972-2.
  • AllouaniF., BoukhetelaD., & BoudjemaF. (2013). Decentralised sliding mode controller design based on hybrid approach for interconnected uncertain non-linear systems. International Journal of Instrumentation Technology, 1, 155–187. doi:10.1504/IJIT.2013.053298.
  • AminiF., & GhaderiP. (2013). Hybridization of Harmony Search and Ant Colony Optimization for optimal locating of structural dampers. Applied Soft Computing, 13, 2272–2280. doi:10.1016/j.asoc.2013.02.001.
  • AroraJ. S. (1989). Introduction to Optimum Design. New York: McGraw-Hill.
  • BelegunduA. D. (1982). A study of mathematical programming methods for structural optimization. PhD thesis, Department of Civil and environmental Engineering, University of Iowa, Iowa.
  • CagninaL. C., EsquivelS. C., & CoelloC. A. C. (2008). Solving engineering optimization problems with the simple constrained particle swarm optimizer. Informatica (Slovenia), 32, 319–326.
  • ChenJ., PanQ. K., WangL., & LiJ. Q. (2012). A hybrid dynamic harmony search algorithm for identical parallel machines scheduling. Engineering Optimization, 44, 209–224. doi:10.1080/0305215X.2011.576759.
  • CoelloC. A. C. (2000a). Use of a self-adaptive penalty approach for engineering optimization problems. Computers in Industry, 41, 113–127. doi:10.1016/S0166-3615(99)00046-9.
  • CoelloC. A. C. (2000b). Constraint-handling using an evolutionary multi-objective optimization technique. Civil Engineering Systems, 17, 319–346. doi:10.1080/02630250008970288.
  • CoelloC. A. C., & MontesE. M. (2002). Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Advanced Engineering Informatics, 16, 193–203. doi:10.1016/S1474-0346(02)00011-3.
  • Das SharmaK., ChatterjeeA., & RakshitA. (2010). Design of a Hybrid Stable Adaptive Fuzzy Controller Employing Lyapunov Theory and Harmony Search Algorithm. IEEE Transactions on Control Systems Technology, 18, 1440–1447.
  • DebK. (2000). An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering, 186, 311–338. doi:10.1016/S0045-7825(99)00389-8.
  • Del SerJ., MatinmikkoM., Gil-LopezS., & MustonenM. (2010). A novel harmony search based spectrum allocation technique for cognitive radio networks. Proceedings of IEEE International Symposium on Wireless Communication Systems, 233–237. doi:10.1109/ISWCS.2010.5624341.
  • DorigoM., ManiezzoV., & ColorniA. (1996). Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 26, 29–41. doi:10.1109/3477.484436.
  • EberhartR. C., & KennedyJ. (1995). A new optimizer using particle swarm theory. Proceedings of the sixth international symposium on micro machine and human science, Nagoya, Japan, IEEE, 39–43. doi:10.1109/MHS.1995.494215.
  • EstahbanatiM. J. (2014). Hybrid probabilistic-harmony search algorithm methodology in generation scheduling problem. Journal of Experimental & Theoretical Artificial Intelligence, 26, 283–296. doi:10.1080/0952813X.2013.861876.
  • GaoX. Z., WangX., JokinenT., OvaskaS., ArkkioJ., & ZengerK. (2012). A hybrid PBIL-based harmony search method. Neural Computing and Applications, 21, 1071–1083. doi:10.1007/s00521-011-0675-6.
  • GaoX. Z., WangX., OvaskaS. J., & ZengerK. (2012). A hybrid optimization method of harmony search and opposition-based learning. Engineering Optimization, 44, 895–914. doi:10.1080/0305215X.2011.628387.
  • GeemZ. W. (2006). Optimal cost design of water distribution networks using harmony search. Engineering Optimization, 38, 259–277. doi:10.1080/03052150500467430.
  • GeemZ. W., LeeK. S., & ParkY. (2005). Application of harmony search to vehicle routing. American Journal of Applied Sciences, 2, 1552–1557. doi:10.3844/ajassp.2005.1552.1557.
  • GognaA., & TayalA. (2013). Metaheuristics: review and application. Journal of Experimental & Theoretical Artificial Intelligence, 25, 503–526. doi:10.1080/0952813X.2013.782347.
  • HeQ., & WangL. (2007). An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Engineering Applications of Artificial Intelligence, 20, 89–99. doi:10.1016/j.engappai.2006.03.003.
  • HuangF. Z., WangL., & HeQ. (2007). An effective co-evolutionary differential evolution for constrained optimization. Applied Mathematics and Computation, 186, 340–356. doi:10.1016/j.amc.2006.07.105.
  • JaberipourM., & KhorramE. (2010). Two improved harmony search algorithms for solving engineering optimization problems. Communications in Nonlinear Science and Numerical Simulation, 15, 3316–3331. doi:10.1016/j.cnsns.2010.01.009.
  • KazemM. A., IrajM., NaderP., & MohammadP. A. (2014). Design of water distribution networks using accelerated momentum particle swarm optimisation technique. Journal of Experimental & Theoretical Artificial Intelligence, 1–17. org/101080/0952813X2013863227.
  • LiuH., CaiZ., & WangY. (2010). Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Applied Soft Computing, 10, 629–640. doi:10.1016/j.asoc.2009.08.031.
  • MahdaviM., FesangharyM., & DamangirE. (2007). An improved harmony search algorithm for solving optimization problems. Applied Mathematics and Computation, 188, 1567–1579. doi:10.1016/j.amc.2006.11.033.
  • MolgaM., & SmutnickiC. (2005). Test functions for optimization needs [pdf]. Retrieved from http://www.zsd.ict.pwr.wroc.pl/files/docs/functions.pdf.
  • NahasN., & Thien-MyD. (2010). Harmony search algorithm: application to the redundancy optimization problem. Engineering Optimization, 42, 845–861. doi:10.1080/03052150903468746.
  • OmranM. G. H., & MahdaviM. (2008). Global-best harmony search. Applied Mathematics and Computation, 198, 643–656. doi:10.1016/j.amc.2007.09.004.
  • PanQ. K., SuganthanP. N., LiangJ. J., & TasgetirenM. F. (2011). A local-best harmony search algorithm with dynamic sub-harmony memories for lot-streaming flow shop scheduling problem. Expert Systems with Applications, 38, 3252–3259. doi:10.1016/j.eswa.2010.08.111.
  • ReklaitisG. V., RavindranA., & RagsdellK. M. (1983). Engineering optimization methods and applications. New York: Wiley.
  • WangX., & YanX. (2013). Global best harmony search algorithm with control parameters co-evolution based on PSO and its application to constrained optimal problems. Applied Mathematics and Computation, 219, 10059–10072. doi:10.1016/j.amc.2013.03.111.
  • Xin YaoX., Yong LiuY., & Guangming LinG. (1999). Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation, 3, 82–102. doi:10.1109/4235.771163.
  • Zong Woo GeemZ. W., Joong Hoon KimJ. H., & LoganathanG. V. (2001). A new heuristic optimization algorithm: harmony search. Simulation, 76, 60–68. doi:10.1177/003754970107600201.

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