Enhancing Harmony Search Parameters Based On Step And Linear Function For Bus Driver Scheduling And Rostering Problems

Optimization is a major challenge in numerous practical world problems.According to the “No Free Lunch (NFL)” theorem,there is no existing single optimizer algorithm that is able to resolve all issues in an effective and efficient manner.It is varied and need to be solved according to the specific c...

Full description

Saved in:
Bibliographic Details
Main Author: Mansor, Nur Farraliza
Format: Thesis
Language:English
English
Published: 2018
Subjects:
Online Access:http://eprints.utem.edu.my/id/eprint/23372/1/Enhancing%20Harmony%20Search%20Parameters%20Based%20On%20Step%20And%20Linear%20Function%20For%20Bus%20Driver%20Scheduling%20And%20Rostering%20Problems.pdf
http://eprints.utem.edu.my/id/eprint/23372/2/Enhancing%20Harmony%20Search%20Parameters%20Based%20On%20Step%20And%20Linear%20Function%20For%20Bus%20Driver%20Scheduling%20And%20Rostering%20Problems.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
id my-utem-ep.23372
record_format uketd_dc
institution Universiti Teknikal Malaysia Melaka
collection UTeM Repository
language English
English
advisor Abal Abas, Zuraida

topic Q Science (General)
QA Mathematics
spellingShingle Q Science (General)
QA Mathematics
Mansor, Nur Farraliza
Enhancing Harmony Search Parameters Based On Step And Linear Function For Bus Driver Scheduling And Rostering Problems
description Optimization is a major challenge in numerous practical world problems.According to the “No Free Lunch (NFL)” theorem,there is no existing single optimizer algorithm that is able to resolve all issues in an effective and efficient manner.It is varied and need to be solved according to the specific capabilities inherent to certain algorithms making it hard to foresee the algorithm that is best suited for each problem.As a result,the heuristic technique is adopted for this research as it has been identified as a potentially suitable algorithm.Alternative heuristic algorithms are also suggested to obtain optimal solutions with reasonable computational effort.However,the heuristic approach failed to produce a solution that nears optimum when the complexity of a problem increases;therefore a type of nature-inspired algorithm known as meta-euristics which utilises an intelligent searching mechanism over a population is considered and consequently used.The meta-heuristic approach is widely used to substitute heuristic terms and is broadly applied to address problems with regards to driver scheduling.However,this meta-heuristic technique is still unable to address the fairness issue in the scheduling and rostering problems.Hence,this research proposes a strategy to adopt an amendment of the harmony search algorithm in order to address the fairness issue which in turn will escalate the level of fairness in driver scheduling and rostering.The harmony search algorithm is classified as a meta-heuristics algorithm that is capable of solving hard and combinatorial or discrete optimisation problems.In this respect,the three main operators in harmony search,namely the Harmony Memory Consideration Rate (HMCR),Pitch Adjustment Rate (PAR) and Bandwidth (BW) play a vital role in balancing local exploitation and global exploration.These parameters influence the overall performance of the HS algorithm,and therefore it is crucial to fine-tune them. Therefore,it is of great interest that we find adjustments for these parameters in this research.There are two contributions to this research.The first one is having HMCR parameter using step function and the linear increase function while the second is applying the fret spacing concept on guitars that is associated with mathematical formulae is also applied in the BW parameter.There are three proposed models on the alteration of HMCR parameters based on the use of the fundamental step function;namely,the constant interval of step function, and its dynamic increase and decrease interval functions.The experimental results revealed that our proposed approach is superior to other state of the art harmony searches either in specific or generic cases. This is achieved by using a first type of association between the linear increase function with a second model of the dynamic increase of step function being remarkable in other combinations and also other models.In conclusion,this proposed approach managed to generate a fairer roster and was thus capable of maximising the balancing distribution of shifts and routes among drivers,which contributed to the lowering of illness, incidents,absenteeism and accidents.
format Thesis
qualification_name Doctor of Philosophy (PhD.)
qualification_level Doctorate
author Mansor, Nur Farraliza
author_facet Mansor, Nur Farraliza
author_sort Mansor, Nur Farraliza
title Enhancing Harmony Search Parameters Based On Step And Linear Function For Bus Driver Scheduling And Rostering Problems
title_short Enhancing Harmony Search Parameters Based On Step And Linear Function For Bus Driver Scheduling And Rostering Problems
title_full Enhancing Harmony Search Parameters Based On Step And Linear Function For Bus Driver Scheduling And Rostering Problems
title_fullStr Enhancing Harmony Search Parameters Based On Step And Linear Function For Bus Driver Scheduling And Rostering Problems
title_full_unstemmed Enhancing Harmony Search Parameters Based On Step And Linear Function For Bus Driver Scheduling And Rostering Problems
title_sort enhancing harmony search parameters based on step and linear function for bus driver scheduling and rostering problems
granting_institution UTeM
granting_department Faculty Of Information And Communication Technology
publishDate 2018
url http://eprints.utem.edu.my/id/eprint/23372/1/Enhancing%20Harmony%20Search%20Parameters%20Based%20On%20Step%20And%20Linear%20Function%20For%20Bus%20Driver%20Scheduling%20And%20Rostering%20Problems.pdf
http://eprints.utem.edu.my/id/eprint/23372/2/Enhancing%20Harmony%20Search%20Parameters%20Based%20On%20Step%20And%20Linear%20Function%20For%20Bus%20Driver%20Scheduling%20And%20Rostering%20Problems.pdf
_version_ 1747834044605792256
spelling my-utem-ep.233722022-02-17T11:07:34Z Enhancing Harmony Search Parameters Based On Step And Linear Function For Bus Driver Scheduling And Rostering Problems 2018 Mansor, Nur Farraliza Q Science (General) QA Mathematics Optimization is a major challenge in numerous practical world problems.According to the “No Free Lunch (NFL)” theorem,there is no existing single optimizer algorithm that is able to resolve all issues in an effective and efficient manner.It is varied and need to be solved according to the specific capabilities inherent to certain algorithms making it hard to foresee the algorithm that is best suited for each problem.As a result,the heuristic technique is adopted for this research as it has been identified as a potentially suitable algorithm.Alternative heuristic algorithms are also suggested to obtain optimal solutions with reasonable computational effort.However,the heuristic approach failed to produce a solution that nears optimum when the complexity of a problem increases;therefore a type of nature-inspired algorithm known as meta-euristics which utilises an intelligent searching mechanism over a population is considered and consequently used.The meta-heuristic approach is widely used to substitute heuristic terms and is broadly applied to address problems with regards to driver scheduling.However,this meta-heuristic technique is still unable to address the fairness issue in the scheduling and rostering problems.Hence,this research proposes a strategy to adopt an amendment of the harmony search algorithm in order to address the fairness issue which in turn will escalate the level of fairness in driver scheduling and rostering.The harmony search algorithm is classified as a meta-heuristics algorithm that is capable of solving hard and combinatorial or discrete optimisation problems.In this respect,the three main operators in harmony search,namely the Harmony Memory Consideration Rate (HMCR),Pitch Adjustment Rate (PAR) and Bandwidth (BW) play a vital role in balancing local exploitation and global exploration.These parameters influence the overall performance of the HS algorithm,and therefore it is crucial to fine-tune them. Therefore,it is of great interest that we find adjustments for these parameters in this research.There are two contributions to this research.The first one is having HMCR parameter using step function and the linear increase function while the second is applying the fret spacing concept on guitars that is associated with mathematical formulae is also applied in the BW parameter.There are three proposed models on the alteration of HMCR parameters based on the use of the fundamental step function;namely,the constant interval of step function, and its dynamic increase and decrease interval functions.The experimental results revealed that our proposed approach is superior to other state of the art harmony searches either in specific or generic cases. This is achieved by using a first type of association between the linear increase function with a second model of the dynamic increase of step function being remarkable in other combinations and also other models.In conclusion,this proposed approach managed to generate a fairer roster and was thus capable of maximising the balancing distribution of shifts and routes among drivers,which contributed to the lowering of illness, incidents,absenteeism and accidents. 2018 Thesis http://eprints.utem.edu.my/id/eprint/23372/ http://eprints.utem.edu.my/id/eprint/23372/1/Enhancing%20Harmony%20Search%20Parameters%20Based%20On%20Step%20And%20Linear%20Function%20For%20Bus%20Driver%20Scheduling%20And%20Rostering%20Problems.pdf text en public http://eprints.utem.edu.my/id/eprint/23372/2/Enhancing%20Harmony%20Search%20Parameters%20Based%20On%20Step%20And%20Linear%20Function%20For%20Bus%20Driver%20Scheduling%20And%20Rostering%20Problems.pdf text en validuser http://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=112999 phd doctoral UTeM Faculty Of Information And Communication Technology Abal Abas, Zuraida 1. Abedinpourshotorban, H., Hasan, S., Shamsuddin, S.M., and As.Sahra, N.F., 2016. A differential-based harmony search algorithm for the optimization of continuous problems. Expert Systems with Applications, 62, pp.317.332. 2. Aderhold, A., Diwold, K., Scheidler, A., Middendorf, M., Gonzalez, J., Pelta, D., Cruz, C., Terrazas, G., and Krasnogor, N., 2010. A New Metaheuristic Bat-Inspired Algorithm. Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), 284 (Nicso 2013), pp.283.294. 3. Affenzeller, M., Beham, A., Kofler, M., Kronberger, G., and Wagner, S.A., 2009. Chapter III Metaheuristic Optimization, pp.103.155. 4. Agoston E. Eiben, J.E.S., 2004. Introduction to Evolutionary Computing. Evolutionary Computation. SpringerVerlag. 5. Akkoyunlu, M.C., Engin, O., and Buyukozkan, K., 2015. A harmony search algorithm for hybrid flow shop scheduling with multiprocessor task problems. Modeling, Simulation, and Applied Optimization (ICMSAO), 2015 6th International Conference on, pp.1.3. 6. Al-Betar, M.A., Awadallah, M.A., Khader, A.T., and Abdalkareem, Z.A., 2015. Island-based harmony search for optimization problems. Expert Systems with Applications, 42 (4), pp.2026.2035. 7. Al-Betar, M.A., Khader, A.T., Geem, Z.W., Doush, I.A., and Awadallah, M. a., 2013. An analysis of selection methods in memory consideration for harmony search. Applied Mathematics and Computation, 219 (22), pp.10753.10767. 8. Alatas, B., 2010a. Chaotic harmony search algorithms. Applied Mathematics and Computation, 216 (9), pp.2687.2699. 9. Alba, E. and Troya, J.M., 1999. A survey of parallel distributed genetic algorithms. Complexity, 4, pp.31.52. 10. Alia, O.M. and Mandava, R., 2011. The variants of the harmony search algorithm: an overview. Artificial Intelligence Review, 36 (1), pp.49.68. 11. Ayvaz, M.T., 2009. Identification of Groundwater Parameter Structure Using Harmony Search Algorithm, pp.129.140. 12. B.Liu, 2009. Theory and Practice of Uncertain Programming. Springer - Verlag Berlin Heidelberg, pp.9.17. 13. Back, T., 1899. Selective pressure in evolutionary algorithms: a characterization of selection mechanisms. Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence, (1), pp.57.62. 14. Back, T. and Schwefel, H.-P., 1993. An Overview of Evolutionary Algorithms for Parameter Optimization. Evolutionary Computation. 15. Baghel, M., Agrawal, S., and Silakari, S., 2012. Survey of Metaheuristic Algorithms for Combinatorial Optimization. International Journal of Computer Applications, 58 (19), pp.975.8887. 16. Baker, J.E., 1985. Adaptive Selection Methods for Genetic Algorithms, pp.101.111. 17. Banks, A., Vincent, J., and Anyakoha, C., 2007. A review of particle swarm optimization. Part I: Background and development. Natural Computing, 6 (4), pp.467.484. 18. Beyer, H.-G., Beyer, H.-G., Schwefel, H.-P., and Schwefel, H.-P., 2002. Evolution strategies . A comprehensive introduction. Natural Computing, 1 (1), pp.3.52. 19. Bianchi, L., Dorigo, M., Gambardella, L.M., and Gutjahr, W.J., 2008. A survey on metaheuristics for stochastic combinatorial optimization. Natural Computing, 8 (2), pp.239.287. 20. Blackwell, T., 2007. Particle Swarm Optimization in Dynamic Environments. Evolutionary Computation in Dynamic and Uncertain Environments, 51/2007, pp.29.49. 21. Blum, C. and Roli, A., 2003a. Metaheuristics in Combinatorial Optimization : Overview and Conceptual Comparison, 35 (3), pp.268.308. 22. Blum, C. and Roli, A., 2003b. Metaheuristics in Combinatorial Optimization : Overview and Conceptual Comparison, pp.1.42. 23. Bonabeau, E., Dorigo, M., and Theraulaz, G., 1999. Swarm Intelligence: From Natural to Artificial Systems. Santa Fe Institute Studies on the Sciences of Complexity OUP, USA. 24. Boussaid, I., Lepagnot, J., and Siarry, P., 2013. A survey on optimization metaheuristics. Information Sciences, 237, pp.82.117. 25. Bratton, D. and Kennedy, J., 2007. Defining a Standard for Particle Swarm Optimization. 2007 IEEE Swarm Intelligence Symposium, (Sis), pp.120.127. 26. C. Voudouris, E.P.K. Tsang, A.A., 2010. Effective Application of Guided Local Search. 27. Cantarella, J., Murty, M.R., Bachelis, G.F., and Cohen, M.D., 2002. Combinatorial Optimization - Algorithms and Complexity. Topology, 109 (5), pp.1014.1016. 28. Cao, F. and Wang, W., 2012. Harmony search based particle swarm optimisation approach for optimal PID control in electroslag remelting process. International Journal of Modelling, Identification and Control, 15 (1), pp.20. 29. Castro, Leandro Nunes de, Timmis, J., 2002. Artificial Immune Systems: A New Computational Intelligence Approach. Springer-Verlag London. 30. Ceberio, J., Mendiburu, A., and Lozano, J.A., 2015. The Linear Ordering Problem, Exact and Heuristic Methods in Combinatorial Optimization. European Journal of Operational Research, 241 (3), pp.686.696. 31. .erny, V., 1985. Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm. Journal of Optimization Theory and Applications, 45 (1), pp.41.51. 32. Chang-ming, X. and Lin, Y., 2012. Research on adjustment strategy of PAR in harmony search algorithm. International Conference on Automatic Control and Artificial Intelligence (ACAI 2012), pp.1705.1708. 33. Chen, J., Pan, Q., and Li, J., 2012. Harmony search algorithm with dynamic control parameters. Applied Mathematics and Computation, 219 (2), pp.592.604. 34. Cheng, M.Y., Prayogo, D., Wu, Y.W., and Lukito, M.M., 2016. A Hybrid Harmony Search Algorithm for Discrete Sizing Optimization of Truss Structure. Automation in Construction, 69, pp.21.33. 35. Coello, C. a C., Lamont, G.B., and Veldhuizen, D. a. Van, 2007. Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation). 36. Coello Coello, C. a. and Reyes-Sierra, M., 2006. Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art. International Journal of Computational Intelligence Research, 2 (3), pp.287.308. 37. Contreras, J., Amaya, I., and Correa, R., 2014. An improved variant of the conventional Harmony Search algorithm. Applied Mathematics and Computation, 227, pp.821.830. 38. Darwin, C., 1859. On the origin of species by means of natural selection, or the preservation of favoured races in the struggle for life. 39. David S.Johnson, 1985. The Column : An Ongoing Guide. Journal of Algorithms, 6, 609, pp.145.169. 40. Desale, S., Rasool, A., Andhale, S., and Rane, P., 2015. Heuristic and Meta-Heuristic Algorithms and Their Relevance to the Real World: A Survey. International Journal of Computer Engineering in Research Trends, 351 (5), pp.2349.7084. 41. Dorigo, M., Caro, G. Di, and Gambardella, L., 1999. Ant algorithms for discrete optimization. Artificial life, pp.1.36. 42. Dorigo, M., Maniezzo, V., and Colorni, a, 1996. Ant system: optimization by a colony of cooperating agents. IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society, 26 (1), pp.29.41. 43. Dorigo, M., Member, S., and Gambardella, L.M., 1997. Ant Colony System : A Cooperative Learning Approach to the Traveling Salesman Problem, 1 (1), pp.53.66. 44. Dorigo, M. and Stutzle, T., 2004. Ant Colony Optimization. Cambridge, Massachusetts: The MIT Press. 45. E.Goldberg, D., 1989. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc. Boston, MA, USA. 46. El-Abd, M., 2013. An improved global-best harmony search algorithm. Applied Mathematics and Computation, 222, pp.94.106. 47. Enayatifar, R. and Yousefi, M., 2013. LAHS: A Novel Harmony Search Algorithm Based on Learning Automata.Communication in Nonlinear Science and Numerical Simulation, 18 (12), pp.3481-3497 48. Engelbrecht, A.P., 2005. Fundamentals of Computational Swarm Intelligence. John Wiley & Sons. 49. Evans, J.R., Zanakis, S.H., and Vazacopuolos, A.A., 1989. Heuristic methods and applications : A categorized survey. European Journal of Operational Research, 43, pp.88.110. 50. Fan, Q. and Yan, X., 2015. Self-adaptive differential evolution algorithm with discrete mutation control parameters. Expert Systems with Applications, 42 (3), pp.1551.1572. 51. Farmer, J.D., Packard, N.H., and Perelson, A.S., 1986. The immune system, adaptation, and machine learning. Physica D: Nonlinear Phenomena. 52. Fesanghary, M., 2009. Harmony Search Applications in Mechanical , Chemical and Electrical Engineering, pp.71.86. 53. Fesanghary, M., Asadi, S., and Geem, Z.W., 2012. Design of low-emission and energy-efficient residential buildings using a multi-objective optimization algorithm. Building and Environment, 49 (1), pp.245.250. 54. Fesanghary, M., Mahdavi, M., Minary-Jolandan, M., and Alizadeh, Y., 2008a. Hybridizing harmony search algorithm with sequential quadratic programming for engineering optimization problems. Computer Methods in Applied Mechanics and Engineering, 197 (33.40), pp.3080.3091. 55. Fleischer, M., 1995. Simulated annealing: past, present, and future. Simulation Conference Proceedings, 1995.pp.155.161. 56. Fogel, D., 1995. Artificial Intelligence Through Simulated Evolution. Wiley-IEEE Press. 57. D.B.Fogel, L.J.Fogel, J.W.A., 1991. Meta-Evolutionary Programming. IEEE, pp.540.545. 58. Forrest, S., Perelson, A.S., and Allen, L., 1994. Self-Nonself Discrimination in a Computer. 1994 IEEE Computer Society Symposium on, in: Research in Security and Privacy, 1994. Proceedings., 1994 IEEE Computer Society Symposium on, 1994, pp. 202.212., pp.202.212. 59. Gao, H., Kwong, S., Yang, J., and Cao, J., 2013. Particle swarm optimization based on intermediate disturbance strategy algorithm and its application in multi-threshold image segmentation. Information Sciences, 250, pp.82.112. 60. Gao, K.Z., Suganthan, P.N., Pan, Q.K., and Tasgetiren, M.F., 2015. An effective discrete harmony search algorithm for flexible job shop scheduling problem with fuzzy processing time. International Journal of Production Research, 53 (19), pp.5896. 61. Garey, M.R. and Johnson, D.S., 1979. A Guide to the Theory of NP-Completeness. A Series of Books in the Mathematical Sciences. 62. de Garis, H., 2004. Introduction to Evolutionary Computing. Evolutionary Computation. 63. Geem, Z., 2007. Harmony search algorithm for solving sudoku. ¡¦ Based Intelligent Information and Engineering Systems. 64. Geem, Z., Tseng, C., and Park, Y., 2005. Harmony search for generalized orienteering problem: best touring in China. Advances in natural computation. 65. Geem, Z.W., 2006. Optimal cost design of water distribution networks using harmony search. Engineering Optimization, 38 (3), pp.259.277. 66. Geem, Z.W., 2008. Harmony search applications in industry. Studies in Fuzziness and Soft Computing. 67. Geem et al, 2001. A New Heuristic Optimization Algorithm: Harmony Search. Simulation, 76 (2), pp.60.68. 68. Gendreau, M. and Potvin, J.-Y., 2005. Metaheuristics in Combinatorial Optimization. Annals of Operations Research, 140 (1), pp.189.213. 69. Glover, F., 1986. Future Paths for Integer Programming and Links to Artificial Intelligence. Computers and Operations Research, 13 (5), pp.533.549. 70. Glover, F., 1994. Tabu search for nonlinear and parametric optimization ( with links to genetic algorithms ), (92). 71. Grefenstette, J.J. and Baker, J.E., 1989. How genetic algorithms work a critical look at implicit parallelism. 72. Hansen, N. and Ostermeier, A., 2001. Completely Derandomized Self-Adaptation in Evolution Strategies, 9 (2), pp.159.195. 73. Hart, E. and Timmis, J., 2008. Application areas of AIS: The past, the present and the future. Applied Soft Computing, 8 (1), pp.191.201. 74. Hasancebi, O., Carba., S., Do.an, E., Erdal, F., and Saka, M.P., 2009. Performance evaluation of metaheuristic search techniques in the optimum design of real size pin jointed structures. Computers and Structures, 87 (5.6), pp.284.302. 75. Hegerty, B., Hung, C., and Kasprak, K., 2009. A Comparative Study on Differential Evolution and Genetic Algorithms for Some Combinatorial Problems. Mexican International Conference on Artificial Intelligence. 76. Holland, J.H., 1992. Adaptation in natural and artificial systems. The MIT Press. 77. I.Rechenberg, 1965. Cybernetic Solution Path of an Experimental Problem - ResearchGate. 78. Ioannidis, Y. and Wong, E., 1983. Query Optimization by Simulated Annealing. American Association for the Advancement of Science. Your, 220 (4598), pp.671.680. 79. Iori, M., 2005. Metaheuristic algorithms for combinatorial optimization problems. Universita Degli Studi Di Bologna. 80. Islam, S.M., Das, S., Ghosh, S., Roy, S., and Suganthan, P.N., 2012. An Adaptive Differential Evolution Algorithm With Novel Mutation and Crossover Strategies for Global Numerical Optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42 (2), pp.482.500. 81. Jesper Christensen, C.B., 2016. Chapter seven: Heuristic and Meta-Heuristic Optimization Algorithms. Nonlinear Optimization of Vehicle Safety Structures, pp.277.314. 82. Holland, J.H., 1975. Adaptation in natural and artificial systems. Ann Arbor, MI, USA: MIT Press Cambridge, MA, USA. 83. Kang, S. and Chae, J., 2017. Harmony Search for the Layout Design of an Unequal Area Facility. Expert Systems with Applications, 79, pp.269.281. 84. Karaboga, D., 2005. An idea based on honey bee swarm for numerical optimization. Techn. Rep. TR06, Erciyes Univ. Press, Erciyes. 85. Karaboga, D. and Akay, B., 2009. A survey: Algorithms simulating bee swarm intelligence. Artificial Intelligence Review, 31 (1.4), pp.61.85. 86. Karaboga, D. and Basturk, B., 2007. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39 (3), pp.459.471. 87. Karaboga, D. and Basturk, B., 2008. On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing, 8 (1), pp.687.697. 88. Kaveh, A. and Talatahari, S., 2009. Particle swarm optimizer, ant colony strategy and 89. harmony search scheme hybridized for optimization of truss structures. Computers and Structures, 87 (5.6), pp.267.283. 90. Keikha, M.M., 2011. Improved Simulated Annealing Using Momentum Terms. 2011 Second International Conference on Intelligent Systems, Modelling and Simulation, pp.44.48. 91. Kennedy, J. and Eberhart, R., 1995. Particle swarm optimization. Proceedings of ICNN¡¯95 - International Conference on Neural Networks, 4, pp.1942.1948. 92. Kennedy, J. and Eberhart, R., 1997. A discrete binary version of the particle swarm algorithm. Systems, Man, and Cybernetics, ¡¦, pp.4.8. 93. Kennedy, J.F., Kennedy, J., and Eberhart, R.C., 2001. Swarm Intelligence. Morgan Kaufmann. 94. Khalili, M., Kharrat, R., Salahshoor, K., and Sefat, M.H., 2014. Global Dynamic Harmony Search algorithm: GDHS. Applied Mathematics and Computation, 228, pp.195.219. 95. Khazali, A.H. and Kalantar, M., 2011. Optimal Reactive Power Dispatch based on Harmony Search Algorithm. International Journal of Electrical Power & Energy Systems, 33 (3), pp.684.692. 96. Kokash, N., 2005. An Introduction to Heuristic Algorithms. Department of Informatics and Telecommunications, (August), pp.1.8. 97. Konak, A., Coit, D.W., and Smith, A.E., 2006. Multi-Objective Optimization Using Genetic Algorithms : A Tutorial, 91 (9), pp.992.1007. 98. Koulamas, C., Antony, S., and Jaen, R., 1994. A survey of simulated annealing applications to operations research problems. International Journal Management Science, 22 (1), pp.41.56. 99. Koza, J.R., 1992. Genetic Programming. New York: The MIT Press. 100. Kramer, O., 2010. A Review of Constraint-Handling Techniques for Evolution Strategies. Applied Computational Intelligence and Soft Computing, 2010 (1), pp.1.11. 101. Kumar, V., Chhabra, J.K., and Kumar, D., 2014. Parameter adaptive harmony search algorithm for unimodal and multimodal optimization problems. Journal of Computational Science, 5 (2), pp.144.155. 102. Kunche, P. and Reddy, K.V.V.S., 2016. Metaheuristic Applications to Speech Enhancement. SpringerBriefs in Speech Technology, pp.17.24. 103. Langdon, W., 2008. A Field Guide to Genetic Programing. Springer, Berlin, Heidelberg. 104. Laporte, G. and Osman, I.H., 1995. Routing problems: A bibliography. Annals of Operations Research, 61 (1), pp.227.262. 105. Lee, C. and Yao, X., 2004. Evolutionary Programming Using Mutations Based on the Levy Probability Distribution. IEEE Transactions on Evolutionary Computation, Vol. 8, No. 1, February 2004, 8 (1), pp.1.13. 106. Li, H.L.H. and Li, L.L.L., 2007. A Novel Hybrid Particle Swarm Optimization Algorithm Combined with Harmony Search for High Dimensional Optimization Problems. The 2007 International Conference on Intelligent Pervasive Computing (IPC 2007), pp.94.97. 107. Liang, J.J., Qin, A.K., Member, S., Suganthan, P.N., Member, S., and Baskar, S., 2006. Comprehensive Learning Particle Swarm Optimizer for Global Optimization of Multimodal Functions, 10 (3), pp.281.295. 108. Liu, L. and Zhou, H., 2013. Hybridization of harmony search with variable neighborhood search for restrictive single-machine earliness / tardiness problem, 226 (37), pp.68.92. 109. Lynn, N. and Suganthan, P.N., 2017. Ensemble Particle Swarm Optimizer. Applied Soft Computing, 55, pp.533.548. 110. Mahdavi, M., Fesanghary, M., and Damangir, E., 2007. An improved harmony search algorithm for solving optimization problems. Applied Mathematics and Computation, 188 (2), pp.1567.1579. 111. Manfrin, M., Birattari, M., St, T., and Dorigo, M., 2006. Parallel Ant Colony Optimization for the Traveling Salesman Problem. Springer, Berlin, Heidelberg, pp.224.234. 112. Mansor, N.F., Abas, Z.A., Fadzli, A., Abdul, N., Shibghatullah, A.S., and Sidek, S., 2016. A New HMCR Parameter of Harmony Search for Better Exploration, 382, pp.181.195. 113. Mansor, N.F., Abas, Z.A., Shibghatullah, A.S., and Rahman, A.F.N.A., 2017. Modified Parameters of Harmony Search Algorithm for Better Searching. IOP Conference Series: Materials Science and Engineering, 226, pp.12113. 114. Martin, O., Otto, S., and Felten, E., 1991. Large-step Markov Chains for the Traveling Salesman Problem. Complex System, 5.3, pp.299. 115. Mauro Birattari, Luis Paquete, Thomas Stutzle, K.V., n.d. Classification of Metaheuristics and Design of Experiments for the Analysis of Components. 116. McKay, R.I., Hoai, N.X., Whigham, P.A., Shan, Y., and O.neill, M., 2010. Grammar-based Genetic programming: A survey. Genetic Programming and Evolvable Machines, 11 (3.4), pp.365.396. 117. Melanie, M., 1999. An Introduction to Genetic Algorithms. A Bradford Book The MIT Press. 118. Menzel, R., De Marco, R.J., and Greggers, U., 2006. Spatial memory, navigation and dance behaviour in Apis mellifera. Journal of comparative physiology. A, Neuroethology, sensory, 119. neural, and behavioral physiology, 192 (9), pp.889.903. 120. Muller-Merbach, H., 1981. Heuristics and their design: a survey. European Journal of Operational Research, 8 (1), pp.1.23. 121. Nicholas Metropolis, Arianna W.Rosenbluth, Marshall N.Rosenbluth, Augusta H.Teller, E.T., 1953. Equation of State Calculations by Fast Computing Machines. 122. Nikolaus Hansen, A.O. and A.G., 1995. On the Adapatation of Arbitrary Normal Mutations Distributions in Evolution Strategies: The Generating Set Adaptation. Proceedings of the Sixth International Conference on Genetic Algorthms, pp.57.64. 123. Nocedal, J. and Wright, S.J., 1999. Numerical Optimization. Springer Series in Operations Research. Springer Business Media. 124. Omran, M.G.H. and Mahdavi, M., 2008. Global-best harmony search. Applied Mathematics and Computation, 198 (2), pp.643.656. 125. Oreski, S., 2014. Hybrid Techniques of Combinatorial Optimization with Application to Retail Credit Risk Assessment. Artificial Intelligence and Applications, 1 (1), pp.21.43. 126. Osman, I.H. and Laporte, G., 1996. Metaheuristics : A bibliography. Annals of Operations Research, 63, pp.513.628. 127. Pan, Q.-K., Suganthan, P.N., Tasgetiren, M.F., and Liang, J.J., 2010. A self-adaptive global best harmony search algorithm for continuous optimization problems. Applied Mathematics and Computation, 216 (3), pp.830.848. 128. Pant, M., Thangaraj, R., and Abraham, A., 2009. Particle Swarm Optimization : Performance Tuning and Empirical Analysis. Foundations, 3, pp.101.128. 129. Patrick Mills, E.T. and J.F., 2003. Applying an Extended Guided Local Search to the Quadratic Assignment Problem. Kluwer Academic Publishers. 130. Pearl, J., 1985. Heuristics: Intelligent search strategies for computer problem solving. Information Processing & Management. 131. Peter J. Angeline, 1998. Evolutionary optimization versus particle swarm optimization Philosophy and performance differences - Springer. 132. Pham, D.T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., and Zaidi, M., 2006. The bees algorithm.A novel tool for complex optimisation. Proceedings of the 2nd International Virtual Conference on Intelligent Production Machines and Systems, pp.454.459. 133. Piroozfard, H., Wong, K.Y., and Asl, A.D., 2015. A Hybrid Harmony Search Algorithm for the Job Shop Scheduling Problems. 2015 8th International Conference on Advanced Software Engineering & Its Applications (ASEA), 13, pp.48.52. 134. Rajeev S. and Krishnamoorthy, C.S., 1992. Genetic Algorithms in Optimization Problem with Discrete and Integer Design Variable, 118 (5), pp.1233.1250. 135. Resende, M.G.C. and Ribeiro, C.C., 2002. Greedy Randomized aAdaptive Search Procedures. In: State of the Art Hnadbook in Metaheuristics. 136. Rodriguez, F.J., Lozano, M., Garcia-Martinez, C., and Gonzalez-Barrera, J.D., 2013. An artificial bee colony algorithm for the maximally diverse grouping problem. Information Sciences, 230, pp.183.196. 137. Saka, M.P., 2009. Optimum Design of Steel Sway Frames to BS5950 using Harmony Search Algorithm. Journal of Constructional Steel Research, 65 (1), pp.36.43. 138. Schwefel, H.-P. and Rudolph, G., 1995. Contemporary Evolution Strategies. In: Third European Conference on Advances in Artificial Life. Springer-Verlag Berlin Heidelberg, pp.893.907. 139. Shabani, M., Abolghasem Mirroshandel, S., and Asheri, H., 2017. Selective Refining Harmony Search: A new optimization algorithm. Expert Systems with Applications, 81, pp.423.443. 140. Shi, Y. and Eberhart, R., 1998. A modified particle swarm optimizer. 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360), pp.69.73. 141. Shivaie, M., Kazemi, M.G., and Ameli, M.T., 2015. A modified harmony search algorithm for solving load-frequency control of non-linear interconnected hydrothermal power systems. Sustainable Energy Technologies and Assessments, 10, pp.53.62. 142. Shukla, A., Tiwari, R., and Kala, R., 2010. Towards Hybrid and Adaptive Computing. Springer-Verlag Berlin Heidelberg 2010, pp.59.82. 143. Silberholz, J. and Golden, B., 2010. Handbook of Metaheuristics. In: M. Gendreau and J.-Y. Potvin, eds. Boston, MA: Springer Science+Business Media, pp.625.640. 144. Sinha, a and Goldberg, D.E., 2003. A Survey of Hybrid Genetic and Evolutionary Algorithms. 145. Sivakumar, S., Venkatesan, R., and Karthiga, M., 2015. Meta - heuristic Approaches for Minimizing Error in Localization of Wireless Sensor Networks. International Journal of Sensor Networks, 17 (1), pp.17.26. 146. Sivasubramani, S. and Swarup, K.S., 2011. Multi-objective harmony search algorithm for optimal power flow problem. International Journal of Electrical Power and Energy Systems, 147. 33 (3), pp.745.752. 148. Socha, K., 2004. ACO for Continuous and Mixed-Variable Optimization. Scientific Research Directorate of the French Community of Belgium, pp.25.36. 149. Socha, K. and Blum, C., 2007. An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training. Neural Computing and Applications, 16 (3), pp.235.247. 150. Socha, K. and Dorigo, M., 2008. Ant colony optimization for continuous domains. European Journal of Operational Research, 185 (3), pp.1155.1173. 151. Storn, R. and Price, K., 1995. Differential Evolution- A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces. Technical Report TR-95-012, pp.1.12. 152. Stutzle, T., 1999. Local Search Algorithms for Combinatorial Problems: Analysis, Improvements, and New Applications. Germany: Infix Sankt Augustin . 153. T. Back, 1996. Evolutionary algorithms in theory and practice evolution strategies evolutionary programming, genetic algorithms. Oxford University Press, Oxford, UK. 154. Talbi, E.-G., 2009. Metaheuristics: From Design to Implementation. John Wiley & Sons. 155. Timmis, J., Hone, a., Stibor, T., and Clark, E., 2008. Theoretical advances in artificial immune systems. Theoretical Computer Science, 403 (1), pp.11.32. 156. Tiwari, F.T.S.C. and M.K., 2007. Swarm Optimization Swarm Intelligence. I-Tech Education and Publishing. 157. Vandenbergh, F. and Engelbrecht, a, 2006. A study of particle swarm optimization particle trajectories. Information Sciences, 176 (8), pp.937.971. 158. Vazirani, V. V., 2001. Approximation Algorithm. College of Computing Georgia Institute of Technology. Springer Berlin Heidelberg New York. 159. Vo©¬, S., Martello, S., Osman, I.H., Roucairol, C., 1999. Meta-Heuristics - Advances and Trends in Local Search Paradigms for Optimization. Springer US. 160. Vo©¬, S., 2001. Meta-heuristics : The State of the Art. In: Local Search for Planning and Scheduling. Springer-Verlag Berlin Heidelberg 2001, pp.1.23. 161. Voudouris, C. and Tsang, E.P.K., 1995. Partial Constraint Satisfaction Problems and Guided Local Search. University of Essex. 162. Walker, A., Hallam, J., and Willshaw, D., 1993. Bee-havior in a mobile robot: The construction of a self-organized cognitive map and its use in robot navigation within a complex, natural environment. Neural Networks, 1993., IEEE ¡¦, 3, pp.1451.1456. 163. Wang, C.-M. and Huang, Y.-F., 2010. Self-adaptive harmony search algorithm for optimization. Expert Systems with Applications, 37 (4), pp.2826.2837. 164. Wang, J., Gao, X.Z., Tanskanen, J.M.A., and Guo, P., 2012. Epileptic EEG signal classification with ANFIS based on harmony search method. Proceedings of the 2012 8th International Conference on Computational Intelligence and Security, CIS 2012, pp.690.694. 165. Weise, T., 2009. Global Optimization Algorithms . Theory and Application. Self- Published., 1 (2), pp.820. 166. Weyland, D., 2015. A critical analysis of the harmony search algorithm -- How not to solve sudoku. Operations Research Perspectives, 2, pp.97.105. 167. Wolpert, D.H. and Macready, W.G., 1997. No free lunch theorems for search. IEEE Transactions on Evolutionary Computation, 1 (1), pp.1.38. 168. Worasucheep, C., 2011. A Harmony Search with Adaptive Pitch Adjustment for Continuous Optimization. International Journal of Hybrid Information Technology, 4 (4), pp.13.24. 169. Xing, C., 2011. A new improved harmony search algorithm for continuous optimization problems. Proceedings of 2011 International Conference on Computer Science and Network Technology, pp.686.689. 170. Yadav, P., Kumar, R., Panda, S.K., and Chang, C.S., 2012. An Intelligent Tuned Harmony Search algorithm for optimisation. Information Sciences, 196, pp.47.72. 171. Yang, X., 2010a. Nature - Inspired Metaheuristic Algorithms. Nature-Inspired Metaheuristic Algorithms Second Edition. Luniver Press. 172. Yang, X.-S., 2010b. Engineering Optimization: An Introduction with Metaheuristic Applications. A John Wiley & Sons, Incorporation 173. Yao, X., 2003. Chapter 2 Evolutionary Computation. Springer, Boston, MA, pp.27.53. 174. Yao, X. and Liu, Y., 1996. Fast Evolutionary Programming. Evolutionary Programming. University College, The University of New South Wales. 175. Yilldiz, A.R., 2008. Hybrid Taguchi-Harmony Search Algorithm for Solving Engineering Optimization Problems. International Journal of Industrial Engineering, 15 (3), pp.286.293. 176. Yin Shan, Robert I. McKay, Daryl Essam, H.A.A., 2006. A Survey of Probabilistic Model Building Genetic Programming. Springer Berlin Heidelberg. 177. Yuhui Shi, Meng-Hiot Lim, B.K.P., 2011. Handbook of Swarm Intelligence. In: Handbook of Swarm Intelligence. Springer-Verlag Berlin Heidelberg. 178. Zainuddin, Z., Lai, K.H., and Ong, P., 2016. An enhanced harmony search based algorithm for feature selection: Applications in epileptic seizure detection and prediction. Computers & Electrical Engineering, 53, pp.143.162. 179. Zeng, B. and Dong, Y., 2015. An Improved Harmony Search Based Energy-Efficient Routing Algorithm for Wireless Sensor Networks. Applied Soft Computing, 41, pp.135.147. 180. Zeng, D., He, Q., Leng, B., Zheng, W., Xu, H., Wang, Y., and Guan, G., 2012. An improved ant colony optimization algorithm based on dynamically adjusting ant number. 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp.2039.2043. 181. ZZhang, Y. and Wildemuth, B.M., 2005. Unstructured Interviews. Unstructured Interviews, (1998), pp.1.10. 182. Zhao, F., Liu, Y., Zhang, C., and Wang, J., 2015. A self-adaptive harmony PSO search algorithm and its performance analysis. Expert Systems with Applications, 42 (21), pp.7436.7455. 183. Zhao, X., 2010. An Enhanced Particle Swarm Optimization Algorithm with Passive Congregation. 2010 International Conference on Machine Vision and Human-machine Interface, (4), pp.432.435. 184. Zheng, J., Chen, Y., and Zhang, W., 2010. A Survey of artificial immune applications. Artificial Intelligence Review, 34 (1), pp.19.34.