examples of rough drafts for spider monkeys

Gupta K, Deep K (2017) Investigation of suitable perturbation rate scheme for spider monkey optimization algorithm Hazrati G, Sharma H, Sharma N, Bansal JC (2016) Modified spider monkey optimization. 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). (Received, Revised, Accepted )Īgrawal V, Rastogi R, Tiwari DC (2018) Spider monkey optimization: a survey. Sharma A, Sharma A, Panigrahi BK, Kiran D, Kumar R (2016) Ageist spider monkey optimization. Int J Comput Sci Inf Technol (IJCSIT) 5(3):4519–4521 Soni N, Kumar T (2014) Study of various mutation operators in genetic algorithms. In: 2019 international conference on electrical, computer and communication engineering (ECCE), 7–9 February 2019 Accessed Īyon SI, Akhand MAH, Shahriyar SA, Siddique N (2019) Spider monkey optimization to solve traveling salesman problem. Singh U et al, A novel binary spider monkey optimization algorithm for thinning of concentric circular antenna arrays Int J RF Microw Comput Aided Eng 24(24):44350 IEEE Trans Antennas Propag 53(8):26749ĭib N, Sharaqa A (2014) Synthesis of thinned concentric circular antenna arrays using teaching learning based optimization. Khodier MM, Christodoulou CG (2005) Linear array geometry synthesis with minimum sidelobe level and null control using particle swarm optimization. Khodier M, Al-Aqeel M (2009) Linear and circular array optimization: a study using particle swarm intelligence. Liao WP, Chu FL (1999) Array pattern synthesis with null steering using genetic algorithms by controlling only the current amplitudes. Haupt L (1997) Phase-only adaptive nulling with a genetic algorithm. In: Proceedings of the second European congress on intelligent techniques and soft computing, pp 1270–1274 Schlierkamp-Voosen D (1994) Strategy adaptation by competition. Michalewics Z (1996) Genetic algorithms data structures of evolution programs Radclie NJ (1991) Equivalence class analysis of genetic algorithms. Īgrawal A, Farswan P, Agrawal V, Tiwari D, Bansal J (2017) On the hybridization of spider monkey optimization and genetic algorithms Kumar S, Sharma B, Sharma VK et al (2018) Evol Intel. Int J Inf Commun Comput Technol (IJICCT) II:96–107 Kumar Sandeep, Sharma Vivek, Kumari Rajani (2015) Self-adaptive spider monkey optimization algorithm for engineering optimization problems. Įhteram M, Karami H, Farzin S (2018) Reducing irrigation deficiencies based optimizing model for multi-reservoir systems utilizing spider monkey algorithm.

Keywordsīansal J, Sharma H, Jadon S, Clerc M (2014) Spider Monkey Optimization algorithm for numerical optimization. The chapter concludes by giving the reader an insight into the latest research trends in SMO. SMO is used for this optimization problem to find the least travel cost covering all the cities. It also comprises a case study of SMO where it is applied to Traveling Salesman Problem (TSP). 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. 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. 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. 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. This chapter focuses on detailing the nuances of SMO specifically the phases involved, namely the Leader Phase, Learning Phase, and Decision Phase. Spider Monkey Optimization Algorithm (SMO) is detailed which is modeled on the basis of the social behavior of spider monkeys. The results put forth are indeed extremely promising when applied to find the best-fit solution to a real-world optimization problem. 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. This chapter addresses an algorithm, namely Spider Monkey Optimization (SMO) for the researchers interested in optimization, swarm intelligence is an engaging realm.

Examples of rough drafts for spider monkeys