An Analysis of the Parameter Modifications in Varieties of Harmony Search Algorithm
A Harmony Search (HS) algorithm is a population based-meta-heuristics approach that is superior in solving diversified and large scale optimization problems. Several studies have pointed out that Harmony Search is an efficient and flexible tool to resolve optimization problems in diverse areas of construction, engineering, robotics, telecommunication, health and energy. Considering its increasing usage in diverse areas, this paper aims to present the historical development of HS, highlighting on its different features, modifications for improvements and limitations. Based on the description of the fundamental concept of HS, recent variations of the extended HS were analyzed focusing on its algorithm’s theory as well as its fundamental and primary concepts. It was found that the enhancements made on the extended HS are mainly on the modification of the parameters, such as the harmony memory consideration rate (HMCR), pitch adjusting rate (PAR) and distance bandwidth (BW). This analysis provides a useful motivation for researchers interested to improve the achievement of the standard HS algorithm and enhance the solution convergence rate and flexibility.
Copyright © 2014 Praise Worthy Prize - All rights reserved.
Marco Dorigo, “Metaheuristics Network,” European Community, 2004.
D. Manjarres, I. Landa-Torres, S. Gil-Lopez, J. Del Ser, M. N. Bilbao, S. Salcedo-Sanz, and Z. W. Geem, “A survey on applications of the harmony search algorithm,” Eng. Appl. Artif. Intell., pp. 1–14, Jun. 2013.
Geem et al, “A New Heuristic Optimization Algorithm: Harmony Search,” Simulation, vol. 76, no. 2, pp. 60–68, Feb. 2001.
E. Talbi, Metaheuristics: from design to implementation. University of Lille - CNRS - INRIA, 2009.
Z. Geem, Music-inspired harmony search algorithm: theory and applications. Springer,Berlin, 2009.
Z. Geem and J. Choi, “Music composition using harmony search algorithm,” Appl. Evol. Comput., 2007.
Z. Geem, “Harmony search algorithm for solving sudoku,” … Based Intell. Inf. Eng. Syst., 2007.
Z. Geem, C. Tseng, and Y. Park, “Harmony search for generalized orienteering problem: best touring in China,” Adv. Nat. Comput., 2005.
Z. W. Geem, Ed., Harmony Search Algorithms for Structural Design Optimization, vol. 239. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009.
Z. W. Geem, Particle-swarm harmony search for water network design - Engineering Optimization - Volume 41, Issue 4. 2009.
Z. W. Geem, K. S. Lee, and Y. Park, “Application of Harmony Search to Vehicle Routing,” Am. J. Appl. Sci., vol. 2, no. 12, pp. 1552–1557, Dec. 2005.
a. Vasebi, M. Fesanghary, and S. M. T. Bathaee, “Combined heat and power economic dispatch by harmony search algorithm,” Int. J. Electr. Power Energy Syst., vol. 29, no. 10, pp. 713–719, Dec. 2007.
O. M. Alia, R. Mandava, D. Ramachandram, and M. E. Aziz, “Dynamic fuzzy clustering using Harmony Search with application to image segmentation,” 2009 IEEE Int. Symp. Signal Process. Inf. Technol., pp. 538–543, Dec. 2009.
J. We, W. Jing, W. Wei, C. Liulin, and J. Qibing, “A parallel harmony search algorithm with dynamic harmony-memory size,” 2013 25th Chinese Control Decis. Conf., pp. 2342–2347, May 2013.
M. T. Ayvaz, “Identification of Groundwater Parameter Structure Using Harmony Search Algorithm,” pp. 129–140, 2009.
Q.-K. Pan, P. N. Suganthan, M. F. Tasgetiren, and J. J. Liang, “A self-adaptive global best harmony search algorithm for continuous optimization problems,” Appl. Math. Comput., vol. 216, no. 3, pp. 830–848, 2010.
O. M. Alia and R. Mandava, “The variants of the harmony search algorithm: an overview,” Artif. Intell. Rev., vol. 36, no. 1, pp. 49–68, Jan. 2011.
M. Mahdavi, M. Fesanghary, and E. Damangir, “An improved harmony search algorithm for solving optimization problems,” Appl. Math. Comput., vol. 188, no. 2, pp. 1567–1579, 2007.
M. G. H. Omran and M. Mahdavi, “Global-best harmony search,” Appl. Math. Comput., vol. 198, no. 2, pp. 643–656, 2008.
R. Enayatifar and M. Yousefi, “LAHS: A Novel Harmony Search Algorithm Based on Learning Automata,” … Nonlinear Sci. …, 2013.
A. Kattan, R. Abdullah, and R. A. Salam, “Harmony Search Based Supervised Training of Artificial Neural Networks,” 2010 Int. Conf. Intell. Syst. Model. Simul., pp. 105–110, Jan. 2010.
S. Kulluk, L. Ozbakir, and A. Baykasoglu, “Training neural networks with harmony search algorithms for classification problems,” Eng. Appl. Artif. Intell., vol. 25, no. 1, pp. 11–19, Feb. 2012.
C. Worasucheep, “A Harmony Search with Adaptive Pitch Adjustment for Continuous Optimization,” vol. 4, no. 4, pp. 13–24, 2011.
C.-M. Wang and Y.-F. Huang, “Self-adaptive harmony search algorithm for optimization,” Expert Syst. Appl., vol. 37, no. 4, pp. 2826–2837, Apr. 2010.
C. Xing, “A new improved harmony search algorithm for continuous optimization problems,” Proc. 2011 Int. Conf. Comput. Sci. Netw. Technol., pp. 686–689, Dec. 2011.
X. Chang-ming and Y. Lin, “Research on adjustment strategy of PAR in harmony search algorithm,” Int. Conf. Autom. Control Artif. Intell. (ACAI 2012), pp. 1705–1708, 2012.
J. Chen, Q. Pan, and J. Li, “Harmony search algorithm with dynamic control parameters,” Appl. Math. Comput., vol. 219, no. 2, pp. 592–604, Oct. 2012.
M. El-Abd, “An improved global-best harmony search algorithm,” Appl. Math. Comput., vol. 222, pp. 94–106, Oct. 2013.
M. A. Al-Betar, A. T. Khader, Z. W. Geem, I. A. Doush, and M. a. Awadallah, “An analysis of selection methods in memory consideration for harmony search,” Appl. Math. Comput., vol. 219, no. 22, pp. 10753–10767, Jul. 2013.
J. H. Holland, Adaptation in natural and artificial systems. The MIT Press, 1992.
T. Back, “Selective pressure in evolutionary algorithms: a characterization of selection mechanisms,” Proc. First IEEE Conf. Evol. Comput. IEEE World Congr. Comput. Intell., no. 1, pp. 57–62, 1899.
J. J. Grefenstette and J. E. Baker, “How genetic algorithms work a critical look at implicit parallelism.” pp. 20–27, 1989.
H. de Garis, Introduction to Evolutionary Computing, vol. 12, no. 2. 2004, pp. 269–271.
J. E. Baker, “Adaptive Selection Methods for Genetic Algorithms,” pp. 101–111, 1985.
V. Kumar, J. K. Chhabra, and D. Kumar, “Parameter adaptive harmony search algorithm for unimodal and multimodal optimization problems,” J. Comput. Sci., vol. 5, no. 2, pp. 144–155, Mar. 2014.
J. Contreras, I. Amaya, and R. Correa, “An improved variant of the conventional Harmony Search algorithm,” Appl. Math. Comput., vol. 227, pp. 821–830, Jan. 2014.
M. Khalili, R. Kharrat, K. Salahshoor, and M. H. Sefat, “Global Dynamic Harmony Search algorithm: GDHS,” Appl. Math. Comput., vol. 228, pp. 195–219, Feb. 2014.
Gupta, A., A Novel evolutionary neural network based temperature forecaster using ant colony optimization metaheuristic, (2011) International Review on Computers and Software (IRECOS), 6 (4), pp. 481-485.
Shankar, T., Shanmugavel, S., Karthikeyan, A., Modified harmony search algorithm for energy optimization in WSN, (2013) International Review on Computers and Software (IRECOS), 8 (6), pp. 1469-1475.
Ghanizadeh, R., Delbari, M., Ebadian, M., Mathematical modeling and harmonics optimization of a multi-level magnetically controlled reactor with harmony search algorithm, (2012) International Review on Modelling and Simulations (IREMOS), 5 (4), pp. 1858-1866.
Shankar, T., Shanmugavel, S., Karthikeyan, A., Modified harmony search algorithm for energy optimization in WSN, (2013) International Journal on Communications Antenna and Propagation (IRECAP), 3 (4), pp. 214-220.
- There are currently no refbacks.
Please send any question about this web site to firstname.lastname@example.org
Copyright © 2005-2019 Praise Worthy Prize