Title: Spider monkey optimisation: state of the art and advances
Authors: Janmenjoy Nayak; Kanithi Vakula; Paidi Dinesh; Bighnaraj Naik
Addresses: Department of Computer Science and Engineering, Aditya Institute of Technology and Management (AITAM), Tekkali, K Kotturu, Andhra Pradesh 532201, India ' Department of Computer Science and Engineering, Sri Sivani College of Engineering, Srikakulam, AP-532410, India ' Department of Computer Science and Engineering, Sri Sivani College of Engineering, Srikakulam, AP-532410, India ' Department of Computer Applications, Veer Surendra Sai University of Technology, Burla, Sambalpur-768018, Odisha, India
Abstract: Algorithm simulated by the social behaviour of understandable agents has become prominent amid the researchers in modern years. Researchers have advanced profuse algorithms by replicating the swarming behaviour of different creatures. Spider monkey optimisation (SMO) algorithm is a novel swarm intelligence based optimization which is a replica of spider monkey's foraging behaviour. Spider monkeys have been classified as animals with fusion-fission social structure, where they pursued to split themselves from huge to lesser hordes and vice-versa depends upon the accessibility of food. SMO and its variants have successful in dealing with difficult authentic world optimization problems due to its elevated effectiveness. This paper depicts a useful analysis of SMO, its variants, applications, advancements, usage levels and performance issues in various popular yet trending domains with a deep perspective. The key motto behind this analytical point of view is to inspire the practitioners and researchers to innovate new solutions.
Keywords: swarm intelligence; Spider monkey optimisation; SMO; data mining; classification.
International Journal of Swarm Intelligence, 2019 Vol.4 No.2, pp.175 - 198
Received: 25 Sep 2018
Accepted: 11 Apr 2019
Published online: 13 Dec 2019 *