Abstract
This chapter addresses an algorithm, namely Spider Monkey Optimization (SMO) for the researchers interested in optimization, swarm intelligence is an engaging realm. Several nature-inspired computing techniques and algorithms have been evolved by simulating the swarming traits of creatures such as ants, fishes, honey-bees, fireflies, etc. The results put forth are indeed extremely promising when applied to find the best-fit solution to a real-world optimization problem. Spider Monkey Optimization Algorithm (SMO) is detailed which is modeled on the basis of the social behavior of spider monkeys. This chapter focuses on detailing the nuances of SMO specifically the phases involved, namely the Leader Phase, Learning Phase, and Decision Phase. It also aims to give an introduction to the basic mathematical jargon and fundamentals that are required to model an SMO algorithm for finding the best-fit solution to an in-hand problem. Various variants of SMO are also covered in this chapter with a detailed overview of the pros and cons of each of the variants focusing on the research gaps. A wealth of information in the form of practical use-cases and implementation nuances is provided in this section, which will be a ready reckoner for a researcher reader aiming to flatten the learning curve associated with experimenting on basic SMO as well as trying out its variants for optimization problems in their specific realm of research. Further, the chapter gives an overview of a transformed version of SMO, Binary SMO with a real-world application and its comparison against other optimization techniques. It also comprises a case study of SMO where it is applied to Traveling Salesman Problem (TSP). SMO is used for this optimization problem to find the least travel cost covering all the cities. The chapter concludes by giving the reader an insight into the latest research trends in SMO.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bansal J, Sharma H, Jadon S, Clerc M (2014) Spider Monkey Optimization algorithm for numerical optimization. J Memetic Comput 6. https://doi.org/10.1007/s12293-013-0128-0
Ehteram M, Karami H, Farzin S (2018) Reducing irrigation deficiencies based optimizing model for multi-reservoir systems utilizing spider monkey algorithm. Water Resour Manag 32:2315–2334
Kumar Sandeep, Sharma Vivek, Kumari Rajani (2015) Self-adaptive spider monkey optimization algorithm for engineering optimization problems. Int J Inf Commun Comput Technol (IJICCT) II:96–107
Kumar S, Sharma B, Sharma VK et al (2018) Evol Intel. https://doi.org/10.1007/s12065-018-0186-9
Agrawal A, Farswan P, Agrawal V, Tiwari D, Bansal J (2017) On the hybridization of spider monkey optimization and genetic algorithms
Radclie NJ (1991) Equivalence class analysis of genetic algorithms. Complex Syst 5(2):183–205
Michalewics Z (1996) Genetic algorithms data structures of evolution programs
Schlierkamp-Voosen D (1994) Strategy adaptation by competition. In: Proceedings of the second European congress on intelligent techniques and soft computing, pp 1270–1274
Haupt L (1997) Phase-only adaptive nulling with a genetic algorithm. IEEE Trans Antennas Propag 45(6):100915
Liao WP, Chu FL (1999) Array pattern synthesis with null steering using genetic algorithms by controlling only the current amplitudes. Int J Electron 86(4):44557
Khodier M, Al-Aqeel M (2009) Linear and circular array optimization: a study using particle swarm intelligence. Prog Electromagn Res 15:34773
Khodier MM, Christodoulou CG (2005) Linear array geometry synthesis with minimum sidelobe level and null control using particle swarm optimization. IEEE Trans Antennas Propag 53(8):26749
Dib N, Sharaqa A (2014) Synthesis of thinned concentric circular antenna arrays using teaching learning based optimization. Int J RF Microw Comput Aided Eng 24(24):44350
Singh U et al, A novel binary spider monkey optimization algorithm for thinning of concentric circular antenna arrays
The traveling salesman problem. https://www.csd.uoc.gr/~hy583/papers/ch11.pdf. Accessed 25 Apr 2019
Ayon SI, Akhand MAH, Shahriyar SA, Siddique N (2019) Spider monkey optimization to solve traveling salesman problem. In: 2019 international conference on electrical, computer and communication engineering (ECCE), 7–9 February 2019
Soni N, Kumar T (2014) Study of various mutation operators in genetic algorithms. Int J Comput Sci Inf Technol (IJCSIT) 5(3):4519–4521
Sharma A, Sharma A, Panigrahi BK, Kiran D, Kumar R (2016) Ageist spider monkey optimization. https://doi.org/10.1016/j.swevo.2016.01.002 (Received 15 Jul 2015, Revised 12 Dec 2015, Accepted 18 Jan 2016)
Agrawal V, Rastogi R, Tiwari DC (2018) Spider monkey optimization: a survey. Int J Syst Assur Eng Manag 9(4):929–941. https://doi.org/10.1007/s13198-017-0685-6
Kumar S, Sharma B, Sharma VK, Poonia RC (2018) Automated soil prediction using bag-of-features and chaotic spider monkey optimization algorithm (Special Issue, VK et al). Evol Intel. https://doi.org/10.1007/s12065-018-0186-9
Hazrati G, Sharma H, Sharma N, Bansal JC (2016) Modified spider monkey optimization. https://doi.org/10.1109/iwci.2016.7860367
Gupta K, Deep K (2017) Investigation of suitable perturbation rate scheme for spider monkey optimization algorithm
Singh D, Salgotra R, Singh U (2017) A novel modified bat algorithm for global optimization. https://doi.org/10.1109/ICIIECS.2017.8275904. 17–18 March 2017
SMO to solve TSP. https://github.com/SafialIslam302/Thesis—Modified-SMO-to-Solve-TSP/blob/master/1407041%20-%20Modified%20SMO%20to%20Solve%20TSP.pptx. Accessed 25 Apr 2019
Das H, Jena AK, Nayak J, Naik B, Behera HS (2015) A novel PSO based back propagation learning-MLP (PSO-BP-MLP) for classification. In: Computational intelligence in data mining, vol 2. Springer, New Delhi, pp 461–471
Rout M, Jena AK, Rout JK, Das H (2020) Teaching–learning optimization based cascaded low-complexity neural network model for exchange rates forecasting. In: Smart intelligent computing and applications. Springer, Singapore, pp 635–645
Sneha V, Shrinidhi KR, Sunitha RS, Nair MK (2019) Collaborative filtering based recommender system using regression and grey wolf optimization algorithm for sparse data. In: 2019 International conference on communication and electronics systems (ICCES) (in press)
Nayak J, Naik B, Jena AK, Barik RK, Das H (2018) Nature inspired optimizations in cloud computing: applications and challenges. In: Cloud computing for optimization: foundations, applications, and challenges. Springer, Cham, pp 1–26
Sahani R, Rout C, Badajena JC, Jena AK, Das H (2018) Classification of intrusion detection using data mining techniques. In: Progress in computing, analytics and networking. Springer, Singapore, pp 753–764
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Kumar, H.H., Sabherwal, T., Bongale, N., Nair, M.K. (2020). Spider Monkey Optimization Algorithm in Data Science: A Quantifiable Objective Study. In: Rout, J., Rout, M., Das, H. (eds) Machine Learning for Intelligent Decision Science. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-3689-2_6
Download citation
DOI: https://doi.org/10.1007/978-981-15-3689-2_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-3688-5
Online ISBN: 978-981-15-3689-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)