A New Algorithm: Wild Mice Colony Algorithm (WMC)

Authors

1 Department of Computer Engineering, Yasooj Branch, Islamic Azad University, Yasooj, Iran

2 Department of Computer Engineering, Nourabad Mamasani Branch, Islamic Azad University

3 Department of Computer Engineering, Islamic Azad University, Shiraz, Iran

Abstract

Optimization is an important and determinant task in structural design. Better designs will be achieved if designers be able to reduce design time and cost using optimization methods. Many optimization problems in engineering are naturally more complicated and difficult to be solved by conventional optimization methods such as mathematical programming. Nature is a basis of many optimizations algorithms, so researchers focus on behavioral patterns of organisms and events in nature by considering a structure toward a target. In this study, a new optimization algorithm is proposed based on the behavioral pattern of wild mice. Studying targeted and beneficial behaviors of wild mice in colony motivates these kinds of behaviors could be a pattern for solving an uncertain complex problem. In this research, based on the experimental results on this animal, the behavior of the mice in the production phases of the population, mating, struggle for survival has been implemented. The mice are organized in several colonies that will fight for survival based on the command of an colony head that is elite. Also, the motor pattern of the mice was defined based on the colony-head location and the average colony members that were effective in an optimal search in the problem space. The behavioral pattern of this living organism was implemented in the simulation environment and results show that the proposed algorithm is a suitable pattern to find an optimal solution for complicated problems. 

Keywords

References

[1] R L. Haupt and S. E. Haupt, Practical Genetic Algorithms, 2nd Edition, John Wiley & Sons Inc, 2004.
[2] S. B. L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004.
[3] W. Sun and Y.Yuan, “Optimization Theory and Methods: Nonlinear Programming”, Springer Science + Business Media, LLC Press, 2006.
[4] J. Nocedal and S. J. Wright, “Numerical Optimization”, 2nd Edition, Springer Science + Business Media, LLC Press, 2006.
[5] J. Holland, “Genetic algorithms and the optimal allocation of trial"s”, SIAM J. Comput. 2 , 88-105, 1979.
[6] J. Kennedy and R. Eberhart, “Particle Swarm Optimization”, Proceedings of IEEE International Conference on Neural Networks, 1942–1948,1995.
[7] D. Karaboga and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm”,  Journal of Global Optimization 39 , 459–471,2007.
[8] M. Dorigo and V. Maniezzo, “A. Colorni, Ant System: Optimization by a colony of cooperating agents”. IEEE Transactions on Systems, Man, and Cybernetics, 1996.
[9] S. Kirkpatrick, C D. Gelatt, M P. Vecchi, “Optimization by simulated annealing”. Science 220, 671–680, 1983.
[10] Glover, F.W.: Tabu search: A tutorial. Interfaces 20, 74–94 (1990) 16.
[11] D. T. Pham, S. Otri, A. Afify,  M. Mahmuddin, H. Al-Jabbouli, “Data clustering using the bees algorithm”, 40th CIRP International Seminar on Manufacturing Systems, p. p. s.p., 2007.
[12] X. Miao, J. Chu, L. Zhang, J. Qiao, “An Evolutionary Neural Network Approach to Simple Prediction of Dam Deformation”. Journal of Information & Computational Science. 10 , 315–1324, 2013.
[13] Z.W. Geem, J. H. Kim, G. V. Loganathan, “A new heuristic optimization algorithm: harmony search”, Simulation, vol. 76, no. 2, pp. 60–68, 2001.
[14] R. P. Feynman, “Simulating physics with computers”, International Journal ofTheoretical Physics. 467–488, 1982.
[15] R. P. Feynman, “Quantummechanical computers”, Foundations of Physics, 507–531, 1986.
[16] A. Narayanan and M.Moore, “Quantum-inspired genetic algorithms”, in Proceedings of the IEEE International Conference on Evolutionary Computation (ICEC ’96), 61–66, 1996.
[17] B. Bhattacharyya and V. K. Gupta, “Fuzzy based evolutionary algorithmfor reactive power optimizationwith FACTS devices,” International Journal of Electrical Power and Energy Systems, 39–47, 2014.
[18] Y. Wang, X. Feng, Y. Huang, “A novel quantum swarm evolutionary algorithm and its applications”, Neurocomputing, 633–640, 2007.
[19] S. I. Birbil and S. Fang, “An electromagnetism-like mechanism for global optimization”, Journal of Global Optimization, 263–282, 2003.
[20] O. K. Erol and I. Eksin, “A new optimizationmethod: Big Bang-Big Crunch”, Advances in Engineering Software, 106–111, 2006.
[21] M. Udrescu, L. Prodan, and M. Vlˇadut¸iu, “Implementing quantum genetic algorithms: a solution based on Grover’s algorithm,” in Proceedings of the 3rd Conference on Computing Frontiers (CF ’06), 71–81, ACM, 2006.
[22] B. Li and L. Wang, “A hybrid quantum-inspired genetic algorithm for multiobjective flow shop scheduling”, IEEE Transactions on Systems,Man, and Cybernetics B, 576–591, 2007.
[23] L. Wang, F. Tang, and H. Wu, “Hybrid genetic algorithm based on quantum computing for numerical optimization and parameter estimation”, Applied Mathematics and Computation, 1141–1156, 2005.
[24] A. Q. H. Badar, B. S. Umre, A. S. Junghare, “Reactive power control using dynamic particle swarm optimization for real power loss minimization,” International Journal of Electrical Power and Energy Systems, 133–136, 2012.
[25] M. Yazdani and F. Jolaei, “Lion Optimization Algorithm (LOA)”:Anature-inspired metaheuristic algorithm, Journal ofComputationalDesignandEngineering3, 24–36, 2016.
[26] AR. Mehrabian and C. Lucas, “A novel numerical optimization nalgorithm inspired fromweedcolonization”. Ecol. Inform. 1(4)355–66, 2006.
[27] D. Simon, “Biogeography-basedoptimization”, Evolut. Comput.IEEE Trans. 2008;12(6)702–13, 2008.
[28] X-S. Yang, “A new metaheuristic bat-inspired algorithm”. Nature Inspired Cooperative Strategies for Optimization, (NICSO2010). Springer;65–74, 2010.
[29] Y-J. Zheng, “Water wave optimization: an ewnature-inspired metaheuristic”, Comput. Oper.Res. 2014;55:1–11, 2014.
[30] J. Wang, B. Zhou, Sh. Zhou, “An Improved Cuckoo Search Optimization Algorithm for the Problem of Chaotic Systems Parameter Estimation”, Hindawi Publishing Corporation Computational Intelligence and Neuroscience, Article ID 2959370, 2016.
[31] Ch-F. Wang and K. Liu, “A Novel Particle Swarm Optimization Algorithm for Global Optimization”, Hindawi Publishing Corporation Computational Intelligence and Neuroscience, 2016.
[32] C. Cubukcuoglu, I. Chatzikonstantinou, M. Fatih Tasgetiren, S. Sariyildiz, Q-K. Pan, “A Multi-Objective Harmony Search Algorithm for Sustainable Design of Floating Settlements”, Algorithms 2016, 9, 51; doi:10.3390/a9030051, 2016.
[33] I. Obagbuwa and A. Philips Abidoye, “Binary Cockroach Swarm Optimization for Combinatorial Optimization Problem”, Algorithms 2016, 9, 59; doi:10.3390/a9030059, 2016.
[34] R M. Rizk Allah, “Hybridization of Fruit Fly Optimization Algorithm and Firefly Algorithm for Solving Nonlinear Programming Problems”, International Journal of Swarm Intelligence and  Evolutionary Computation, http://dx.doi.org/10.4172/2090-4908.1000134, 2016.
[35] J. Liang, B. Qu, P. Suganthan, “Problemdefinitions andevaluation criteria fortheCEC2014specialsessionandcompetitiononsingle objective real-parameternumericaloptimization”, Computational Intelligence Laboratory, 2013.
[36] Hall, Edward Twitchell, 1 9 1 4 -.” The hidden dimension” / Edward T. Hall, p. cm. Reprint. Originally published: Garden City,. N.Y.: Doubleday, 1966.
[37] H. Shah-Hosseini, “Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimization”, International Journal of Computational Science and Engineering, 132–140, 2011.
[38] Y. Zhang, L.Wu, Y. Zhang, J.Wang, “Immune gravitation inspired optimization algorithm”, in Advanced Intelligent Computing, pp. 178–185, Springer, Berlin,Germany, 2012.
[39] W. Li, Q. Yin, X. Zhang, “Continuous quantum ant colony optimization and its application to optimization and analysis of induction motor structure”, in Proceedings of the IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA ’10), 313–317, 2010.
[40] D. Ding, D. Qi, X. Luo, J. Chen, X. Wang, and P. Du, “Convergence analysis and performance of an extended central force optimization algorithm”,  Applied athematics and Computation, 2246–2259, 2012.
[41] محمد، امیرعباسیان، حسین، نظام‌آبادی پور، «الگوریتم جستجوی گرانشی چندهدفه مبتنی بر مرتب‌سازی چبهه‌های مغلوب‌نشده»، مجله مهندسی برق دانشگاه تبریز، شماره 1 جلد 41، ص61-81‌، 1391.
[42] شهرام، جمالی، سپیده، ملک تاجی، مرتضی، آنالویی، « مکان‌یابی ماشین‌های مجازی با استفاده از الگوریتم رقابت استعماری»، مجله مهندسی برق دانشگاه تبریز، شماره 1 جلد 46، ص75، 1395.
[43] مجید، محمدپور، حمید، پروین، « الگوریتم ژنتیک آشوب گونه مبتنی بر حافظه و خوشه‌بندی برای حل مسائل بهینه‌سازی پویا»، مجله مهندسی برق دانشگاه تبریز، شماره 3 جلد 46، ص77، 1395.
[44] P N. Suganthan, N. Hansen, J J. Liang, “Problem definitions and evaluation criteria for the CEC 2005 Special Session on Real Parameter Optimization”, Nanyang Technological University, Singapore, Tech. Rep, May. 2005[Online]. Available: http:// www3.ntu.edu.sg/home/EPNSugan/index f iles/CEC-05/Tech- Repot-May-30-05.pdf.

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