International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 9, No. 12, 8 Dec. 2017
Cover page and Table of Contents: PDF (size: 784KB)
Author(s)
Index Terms
Swarm intelligence algorithm, Monkey search, Monkey algorithm, Spider monkey optimization
Abstract
Swarm intelligence algorithms (SIA) are bio-inspired techniques based on the intelligent behavior of various animals, birds, and insects. SIA are problem-independent and are efficient in solving real world complex optimization problems to arrive at the optimal solutions. Monkey behavior based algorithms are one among the SIAs first proposed in 2007. Since then, several variants such as Monkey search, Monkey algorithm, and Spider Monkey optimization algorithms have been proposed. These algorithms are based on the tree or mountain climbing and food searching behavior of monkeys either individually or in groups. They were designed with various representations, covering different behaviors of monkeys and hybridizing with the efficient operators and features of other SIAs and Genetic algorithm. They were explored for applications in several fields including bioinformatics, civil engineering, electrical engineering, networking, data mining etc. In this survey, we provide a comprehensive overview of monkey behavior based algorithms and their related literatures and discuss useful research directions to provide better insights for swarm intelligence researchers.
Cite This Paper
R. Vasundhara Devi, S. Siva Sathya, "Monkey Behavior Based Algorithms - A Survey", International Journal of Intelligent Systems and Applications(IJISA), Vol.9, No.12, pp.67-86, 2017. DOI: 10.5815/ijisa.2017.12.07
Reference
[1]A. Mucherino and O. Seref, “Monkey search: A novel metaheuristic search for global optimization,” AIP Conf. Proc., vol. 953, no. 1, pp. 162–173, 2007.
[2]R. Zhao, “Monkey algorithm for global numerical optimization,” J. Uncertain Syst., vol. 2, no. 3, pp. 165–176, 2008.
[3]J. C. Bansal, H. Sharma, S. S. Jadon, and M. Clerc, “Spider Monkey Optimization algorithm for numerical optimization,” Memetic Comput., vol. 6, no. 1, pp. 31–47, 2014.
[4]K. Sörensen, “Metaheuristics-the metaphor exposed,” Int. Trans. Oper. Res., vol. 22, no. 1, pp. 3–18, 2015.
[5]I. J. I. Systems, “Economic Load Dispatch by Hybrid Swarm Intelligence Based Gravitational Search Algorithm,” no. July, pp. 21–32, 2013.
[6]S. K. Sahana, “Application of Modified Ant Colony Optimization ( MACO ) for Multicast Routing Problem,” no. April, pp. 43–48, 2016.
[7]D. J. Persis and T. P. Robert, “Reliable Mobile Ad-Hoc Network Routing Using Firefly Algorithm,” no. May, pp. 10–18, 2016.
[8]E. Bonabeau, M. Dorigo, and G. Theraulaz, Swarm Intelligence: From Natural to Artificial Systems, vol. 4, no. 1. 1999.
[9]D. Karaboga, “An idea based on Honey Bee Swarm for Numerical Optimization,” Tech. Rep. TR06, Erciyes Univ., no. TR06, p. 10, 2005.
[10]D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, vol. Addison-We. 1989.
[11]M. Dorigo, M. Birattari, C. Blum, M. Clerc, T. Stützle, and A. Winfield, “Ant Colony Optimization and Swarm Intelligence,” in 6th International Conference, ANTS, 2008, pp. 22–24.
[12]Zong Woo Geem, Joong-Hoon Kim, and G. Loganathan, “A New Heuristic Optimization Algorithm: Harmony Search,” Simulation, vol. 76, no. 2, pp. 60–68, 2001.
[13]J. E. Lennard-Jones, “Cohesion,” Proc. Phys. Soc., vol. 43, no. 5, pp. 461–482, 1931.
[14]P. Morse, “Diatomic Molecules According to the Wave Mechanics. II. Vibrational Levels,” Phys. Rev., vol. 34, no. 1, pp. 57–64, 1929.
[15]A. R. Kammerdiner, A. Mucherino, and P. M. Pardalos, “Application of monkey search meta-heuristic to solving instances of the multidimensional assignment problem,” Lect. Notes Control Inf. Sci., vol. 381, pp. 385–397, 2009.
[16]W. P. Pierskalla, “The multidimensional assignment problem,” Oper. Res., pp. 422–431, 1968.
[17]F. G. Duque, L. W. de Oliveira, E. J. de Oliveira, A. L. M. Marcato, and I. C. Silva, “Allocation of capacitor banks in distribution systems through a modified monkey search optimization technique,” Int. J. Electr. Power Energy Syst., vol. 73, pp. 420–432, 2015.
[18]J. W. J. Wang, Y. Y. Y. Yu, Y. Z. Y. Zeng, and W. L. W. Luan, “Discrete monkey algorithm and its application in transmission network expansion planning,” Power Energy Soc. Gen. Meet. 2010 IEEE, no. 1, pp. 1–5, 2010.
[19]I. J. Silva, M. J. Rider, R. Romero, and C. A. F. Murari, “Transmission Network Expansion Planning Considering Uncertainty in Demand,” Power Syst. IEEE Trans., vol. 21, no. 4, pp. 1565–1573, 2006.
[20]T.-H. Yi, H.-N. Li, and X.-D. Zhang, “A modified monkey algorithm for optimal sensor placement in structural health monitoring,” Smart Mater. Struct., vol. 21, no. 10, p. 105033, 2012.
[21]Z. W. Geem, “State-of-the-art in the structure of harmony search algorithm,” Stud. Comput. Intell., vol. 270, pp. 1–10, 2010.
[22]T. Yoo and S. Lafortune, “NP-completeness of sensor selection problems arising in partially observed discrete-event systems,” IEEE Trans. Automat. Contr., vol. 47, no. 9, pp. 1495–1499, 2002.
[23]G. W. Housner, L. a. Bergman, T. K. Caughey, a. G. Chassiakos, R. O. Claus, S. F. Masri, R. E. Skelton, T. T.
Soong, B. F. Spencer, and J. T. P. Yao, “Structural Control: Past, Present, and Future,” J. Eng. Mech., vol. 123, no. 9, pp. 897–971, 1997.
[24]T. Carne and C. Dohrmann, “A modal test design strategy for model correlation,” Proceedings-Spie …, pp. 927–933, 1995.
[25]L. Zheng, “An improved monkey algorithm with dynamic adaptation,” Appl. Math. Comput., vol. 222, pp. 645–657, 2013.
[26]S. H. he Zhang Chun-kai, LI Xiao Feng, “Chaos Optimization Algorithm Based on Linear Search and Its Application to Nonlinear Constraint Optimization Problems,” Davis. Control, pp. 123–125, 2001.
[27]M. S. Tavazoei and M. Haeri, “Comparison of different one-dimensional maps as chaotic search pattern in chaos optimization algorithms,” Appl. Math. Comput., vol. 187, no. 2, pp. 1076–1085, 2007.
[28]Ting-Hua Yi, Hong-Nan Li, Gangbing Song and X. D. Zhong, “Optimal sensor placement for health monitoring of high-rise structure using adaptive monkey algorithm,” Struct. Control Heal. Monit., pp. 667–681, 2015.
[29]Ting-Hua Yi, Hong-Nan Li and X. D. Zhong, “Health monitoring sensor placement optimization for Canton Tower using immune monkey algorithm,” Struct. Control Heal. Monit., pp. 123–138, 2015.
[30]J. D. Farmer and N. H. Packard, “The Immune System, adaptation, and Machine Learning,” Physica, pp. 187–204, 1986.
[31]C. Papadimitriou and G. Lombaert, “The effect of prediction error correlation on optimal sensor placement in structural dynamics,” Mech. Syst. Signal Process., vol. 28, pp. 105–127, 2012.
[32]M. G. and X.-D. Z. Ting-Hua Yi, Hong-Nan Li, “Sensor Placement Optimization in Structural Health Monitoring Using Niching Monkey Algorithm,” Int. J. Struct. Stab. Dyn., vol. 17, no. 8, pp. 2559–2572, 2014.
[33]A. Will, J. Bustos, M. Bocco, J. Gotay, and C. Lamelas, “On the use of niching genetic algorithms for variable selection in solar radiation estimation,” Renew. Energy, vol. 50, pp. 168–176, Feb. 2013.
[34]X. Chen, Y. Zhou, and Q. Luo, “A Hybrid Monkey Search Algorithm for Clustering Analysis,” vol. 2014, 2014.
[35]D. Karaboga and C. Ozturk, “A novel clustering approach: Artificial Bee Colony (ABC) algorithm,” Appl. Soft Comput. J., vol. 11, no. 1, pp. 652–657, 2011.
[36]J. C. Spall, “An overview of the simultaneous perturbation method for efficient optimization,” John Hopkins APL Tech. Dig., no. 19, pp. 482–492, 1998.
[37]J. Jia, S. Feng, and W. Liu, “A triaxial accelerometer monkey algorithm for optimal sensor placement in structural health monitoring,” Meas. Sci. Technol., vol. 26, no. 6, p. 65104, 2015.
[38]D. C. Kammer and M. L. Tinker, “Optimal placement of triaxial accelerometers for modal vibration tests,” Mech. Syst. Signal Process., vol. 18, no. 1, pp. 29–41, 2004.
[39]D. Zou, L. Gao, S. Li, and J. Wu, “Solving 0-1 knapsack problem by a novel global harmony search algorithm,” in Applied Soft Computing Journal, 2011, vol. 11, no. 2, pp. 1556–1564.
[40]Y. Zhou, X. Chen, and G. Zhou, “An improved monkey algorithm for a 0-1 knapsack problem,” Appl. Soft Comput. J., vol. 38, pp. 817–830, 2016.
[41]G. Q. LIU Jian-qin, HE Yi-Chao, “Solving Knapsack problem based on discrete particle swarm optimization,” Comput. Eng. Des., pp. 3189–3191, 2007.
[42]A. Khalil, S.-E. Fateen, and A. Bonilla-Petriciolet, “MAKHA—A New Hybrid Swarm Intelligence Global Optimization Algorithm,” Algorithms, vol. 8, no. 2, pp. 336–366, 2015.
[43]A. H. Gandomi and A. H. Alavi, “Krill herd: A new bio-inspired optimization algorithm,” Commun. Nonlinear Sci. Numer. Simul., vol. 17, no. 12, pp. 4831–4845, 2012.
[44]Z. Peng, H. Yin, A. Pan, and Y. Zhao, “Chaotic Monkey Algorithm Based Optimal Sensor Placement,” vol. 9, no. 1, pp. 423–434, 2016.
[45]B. Li and W. Jiang, “Optimizing complex functions by chaos search,” Cybern. Syst., vol. 29, no. 4, pp. 409–419, 1998.
[46]C. M. Ituarte-Villarreal, N. Lopez, and J. F. Espiritu, “Using the Monkey Algorithm for hybrid power systems optimization,” Procedia Comput. Sci., vol. 12, no. 915, pp. 344–349, 2012.
[47]P. Arun, R. Banerjee, and S. Bandyopadhyay, “Optimum sizing of photovoltaic battery systems incorporating uncertainty through design space approach,” Sol. Energy, vol. 83, no. 7, pp. 1013–1025, 2009.
[48]D. Heckerman, A. Mamdani, and M. P. Wellman, “Real-world applications of Bayesian networks,” Commun. ACM, vol. 38, no. 3, pp. 24–26, 1995.
[49]S. Mittal, K. Gopal, and S. L. Maskara, “A novel Bayesian Belief Network structure learning algorithm based on bio-inspired monkey search meta-heuristic,” 2014 7th Int. Conf. Contemp. Comput. IC3 2014, pp. 141–147, 2014.
[50]G. Zhu and S. Kwong, “Gbest-guided artificial bee colony algorithm for numerical function optimization,” Appl. Math. Comput., vol. 217, no. 7, pp. 3166–3173, 2010.
[51]D. Karaboga and B. Akay, “A modified Artificial Bee Colony (ABC) algorithm for constrained optimization problems,” Appl. Soft Comput. J., vol. 11, no. 3, pp. 3021–3031, 2011.
[52]R. K. Sandeep Kumar, Vivek Kumar Sharma, “Self-Adaptive Spider Monkey Optimization Algorithm for Engineering Optimization Problems,” Int. J. Information, Commun. Comput. Technol., vol. II, no. Ii, pp. 96–107, 2014.
[53]R. K. Sandeep Kumar, Vivek Kumar Sharma, “Modified Position Update in Spider Monkey Optimization Algorithm,” Int. J. Emerg. Technol. Comput. Appl. Sci. ( IJETCAS ), no. 1961, pp. 198–204, 2013.
[54]S. Kumar, R. Kumari, and V. K. Sharma, “Fitness based position update in spider monkey optimization algorithm,” Procedia Comput. Sci., vol. 62, no. Scse, pp. 442–449, 2015.
[55]R. Reddy and S. Kalavathi, “Modified Monkey Optimization Algorithm for Solving Optimal Reactive Power Dispatch Problem,” vol. 1, no. 4, pp. 34–42, 2014.
[56]J. Kiefer, “Sequential Minimax Search for a Maximum,” Proc. Am. Math. Soc., vol. 4, no. 3, pp. 502–506, 1953.
[57]Kavita Gupta and Kusum Deep, “Tournament Selection Based Probability Scheme in Spider Monkey Optimization Algorithm,” Adv. Intell. Syst. Comput., vol. 382, pp. 239–250, 2016.
[58]B. L. Miller and D. E. Goldberg, “Genetic Algorithms, Tournament Selection, and the Effects of Noise,” Complex Syst., vol. 9, no. 3, pp. 193–212, 1995.
[59]Kavita Gupta, Kusum Deep and J. C. Bansal, “Improving the Local Search Ability of Spider Monkey Optimization Algorithm Using Quadratic Approximation for Unconstrained Optimization,” vol. 0, no. 0, 2016.
[60]K. Deep and K. N. Das, “Quadratic approximation based hybrid genetic algorithm for function optimization,” Appl. Math. Comput., vol. 203, no. 1, pp. 86–98, 2008.