Improve of Harmony Search by Scramble Mutation for Global Optimizations Problems
Article Sidebar
-
Harmony search, Scramble mutation, Genetic algorithms, Global optimization, Evolutionary algorithms
Abstract
Harmony search (HS) is a new meta-heuristic optimization method imitating the music improvisation process where musicians improvise their instruments pitches searching for a perfect state of harmony. The usage of HS has become a common thing for a variety of numerical and real-world problems. It has several advantages over other meta-heuristics. It considers all existing vectors to generate a new vector. It imposes fewer mathematical requirements. The main disadvantage of HS encompasses its tendency to converge prematurely, which in essence leads to lose diversity during the search. In this study, a new variant of HS, called Scramble Mutation Harmony Search (SMHS), is proposed in this work where concepts from Genetic Algorithm (GA) process are borrowed to enhance the performance of HS. The Scramble Mutation is original step of GA, and is popular with permutation representations. In this, from the entire chromosome, a subset of genes is chosen and their values are scrambled or shuffled randomly. The performance of the SMHS is evaluated and compared with HS (a recently developed variation of HS that is, DLHS, and MHS). The experiments conducted show that the SMHS generally outperformed the other approaches when applied to ten benchmark problems. The effect of the SMHS parameters is analysed. Finally, the results show that cellular approaches seem to be an efficient alternative for optimization problems.
Full text article
References
[1] Weise, T. Z. M. C. R. a. N. A. (2009). Why is optimization difficult? Berlin: Springer, Berlin, Heidelberg.
[2] Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm: Harmony search. Simulation, 76(2), 60–68.
[3] Lee, K., Geem, Z., & others. (2005). The harmony search heuristic algorithm for discrete. Engineering Optimization, 37(7), 663–684.
[4] Deepa, S. S., & Sivanandam, S. N. (2007). Introduction to Genetic Algorithms. Springer. ISBN 9783540731894.
[5] Geem, Z. W. (2010). Novel derivative of harmony search algorithm for discrete design variables. Applied Mathematics and Computation, 199(1), 223–230.
[6] Geem, Zong Woo, & Yoon, Y. (2017). Harmony search optimization of renewable energy charging with energy. Electrical Power and Energy Systems, 86, 120–126.
[7] Mahdavi, M., Fesanghary, M., & Damangir, E. (2007). An improved harmony search algorithm for solving optimization problems. Applied Mathematics and Computation, 188(2), 1567–1579.
[8] Al-Betar, M. K., Khader, A. T., & others. (2010). A harmony search with multi-pitch adjusting rate for the university course timetabling. Recent Advances in Harmony Search Algorithm, 147–161.
[9] Mahdavi, M., Geem, Z. W., & others. (2008). Global-best harmony search. Applied Mathematics and Computation, 198(2), 643–656.
[10] Maythaisong, E., & Songpan, W. (2018). Mutation-Based Harmony Search Algorithm for Hybrid Testing of Web Service Composition. Computational Intelligence and Neuroscience, 2018, 15.
[11] Geem, Z. W. (2008). Novel derivative of harmony search algorithm for discrete design variables. Applied Mathematics and Computation, 199(1), 223–230.
[12] Kattan, A., & Abdullah, R. (2010). Harmony search based supervised training of artificial neural networks. Intelligent Systems, Modelling and Simulation, IEEE, 105–110.
[13] Mansor, N., Zain, A. M., Ahmad, R., Farhana, A., & others. (2014). An optimization solution using a harmony search algorithm. Science International, 1745–1749.
[14] Geem, Z. W., & Williams, J. (2007). Harmony search and ecological optimization. International Journal of Energy and Environment, 1(2).
[15] Al-Betar, M., & Khader, A. (2008). A harmony search algorithm for university course. Annals of Operations Research, 1–29.
[16] Lee, K., & Geem, Z. (2004). A new structural optimization method based on the harmony search algorithm. Computers & Structures, 82(9), 781–798.
[17] da Conceição Cunha, M., & Sousa, J. (1999). Water distribution network design optimization: Simulated annealing approach. Journal of Water Resources Planning and Management, 125(4), 215–221.
[18] Mallawaarachchi, V. (2017). Introduction to Genetic Algorithms—Including Example Code. Online resource. Accessed 18 July 2019.
[19] Whitley, D., Rana, S., Dzubera, J., & Mathias, K. E. (1996). Evaluating Evolutionary Algorithms. Artificial Intelligence, 85(1–2), 245–276. doi:10.1016/0004-3702(95)00124-7.
[20] Yao, X., Liu, Y., & Lin, G. (1999). Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation, 3(2), 82–102.
[21] Pan, Q. K., Suganthan, P. N., Liang, J. J., & Tasgetiren, M. F. (2010). A local-best harmony search algorithm with dynamic subpopulations. Engineering Optimization, 42(2), 101–117.
Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
In a brief statement, the rights relate to the publication and distribution of research published in the journal of the University of Sebha where authors who have published their articles in the journal of the university of Sebha should how they can use or distribute their articles. They reserve all their rights to the published works, such as (but not limited to) the following rights:
- Copyright and other property rights related to the article, such as patent rights.
- Research published in the journal of the University of Sebha and used in its future works, including lectures and books, the right to reproduce articles for their own purposes, and the right to self-archive their articles.
- The right to enter a separate article, or for a non-exclusive distribution of their article with an acknowledgment of its initial publication in the journal of Sebha University.
Privacy Statement The names and e-mail addresses entered on the Sabha University Journal site will be used for the aforementioned purposes only and for which they were used.