Spider Monkey Optimization: a survey

Agrawal, Vani; Rastogi, Ratika; Tiwari, D. C.

doi:10.1007/s13198-017-0685-6

Cited by 43 publications

(21 citation statements)

References 39 publications

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“…SMO has found a good number of applications in different domains, especially real valued optimization tasks, within a short period of time [38]. It has been applied to automatic generation of control [39], designing electromagnetic antenna arrays [40], rule mining for f. Splits the group into subgroups or combine all the groups into one group if Global Leader is not updating for a specific time.…”

Section: Review Of Spider Monkey Optimization (Smo)mentioning

confidence: 99%

Discrete Spider Monkey Optimization for Travelling Salesman Problem

Akhand

Ayon

Shahriyar

et al. 2020

Applied Soft Computing

146

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Meta-heuristic algorithms inspired by biological species have become very popular in recent years. Collective intelligence of various social insects such as ants, bees, wasps, termites, birds, fish, has been investigated to develop a number of meta-heuristic algorithms in the general domain of swarm intelligence (SI). The developed SI algorithms are found effective in solving different optimization tasks. Traveling Salesman Problem (TSP) is the combinatorial optimization problem where a salesman starting from a home city travels all the other cities and returns to home city in the shortest possible path. TSP is a popular problem due to the fact that the instances of TSP can be applied to solve real-world problems, implication of which turns TSP into a standard test bench for performance evaluation of new algorithms. Spider Monkey Optimization (SMO) is a recent addition to SI algorithms based on the social behavior of spider monkeys. SMO implicitly adopts grouping and regrouping for the interactions to improve solutions; such multi-population approach is the motivation of this study to develop an effective method for TSP. This paper presents an effective variant of SMO to solve TSP called discrete SMO (DSMO). In DSMO, every spider monkey represents a TSP solution where Swap Sequence (SS) and Swap Operator (SO) based operations are employed, which enables interaction among monkeys in obtaining the optimal TSP solution. The SOs are generated using the experience of a specific spider monkey as well as the experience of other members (local leader, global leader, or a randomly selected spider monkey) of the group. The performance and effectiveness of the proposed method have been verified on a large set of TSP instances and the outcomes are compared to other well-known methods. Experimental results demonstrate the effectiveness of the proposed DSMO for solving TSP.

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Section: Review Of Spider Monkey Optimization (Smo)mentioning

confidence: 99%

Discrete Spider Monkey Optimization for Travelling Salesman Problem

Akhand

Ayon

Shahriyar

et al. 2020

Applied Soft Computing

146

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“…The spider monkey optimization (SMO) method depends on the intelligent way of behaving by the spider monkey [18]. The SMO algorithm is a population-based algorithm that is mainly inspired by the social actions which are commonly performed by spider monkeys.…”

Section: Related Workmentioning

confidence: 99%

SMO-DNN: Spider Monkey Optimization and Deep Neural Network Hybrid Classifier Model for Intrusion Detection

et al. 2020

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The enormous growth in internet usage has led to the development of different malicious software posing serious threats to computer security. The various computational activities carried out over the network have huge chances to be tampered and manipulated and this necessitates the emergence of efficient intrusion detection systems. The network attacks are also dynamic in nature, something which increases the importance of developing appropriate models for classification and predictions. Machine learning (ML) and deep learning algorithms have been prevalent choices in the analysis of intrusion detection systems (IDS) datasets. The issues pertaining to quality and quality of data and the handling of high dimensional data is managed by the use of nature inspired algorithms. The present study uses a NSL-KDD and KDD Cup 99 dataset collected from the Kaggle repository. The dataset was cleansed using the min-max normalization technique and passed through the 1-N encoding method for achieving homogeneity. A spider monkey optimization (SMO) algorithm was used for dimensionality reduction and the reduced dataset was fed into a deep neural network (DNN). The SMO based DNN model generated classification results with 99.4% and 92% accuracy, 99.5%and 92.7% of precision, 99.5% and 92.8% of recall and 99.6%and 92.7% of F1-score, utilizing minimal training time. The model was further compared with principal component analysis (PCA)-based DNN and the classical DNN models, wherein the results justified the advantage of implementing the proposed model over other approaches.

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“…Due to its high efficiency, the algorithm was used to solve complex problems. [14]. Spider monkey optimization has been applied in various fields like big data [19], image processing [20], cluster routing protocols for WSNs.…”

Section: Literature Surveymentioning

confidence: 99%

“…If there is a need, they separate (fission) from the group and reunite (fusion) later when needed. This method is widely used for clustering techniques [14].…”

Section: Introductionmentioning

confidence: 99%

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BOLD: Bio-Inspired Optimized Leader Election for Multiple Drones

Ganesan

Raajini

Nayyar

et al. 2020

Sensors

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Over the past few years, unmanned aerial vehicles (UAV) or drones have been used for many applications. In certain applications like surveillance and emergency rescue operations, multiple drones work as a network to achieve the target in which any one of the drones will act as the master or coordinator to communicate, monitor, and control other drones. Hence, drones are energy-constrained; there is a need for effective coordination among them in terms of decision making and communication between drones and base stations during these critical situations. This paper focuses on providing an efficient approach for the election of the cluster head dynamically, which heads the other drones in the network. The main objective of the paper is to provide an effective solution to elect the cluster head among multi drones at different periods based on the various physical constraints of drones. The elected cluster head acts as the decision-maker and assigns tasks to other drones. In a case where the cluster head fails, then the next eligible drone is re-elected as the leader. Hence, an optimally distributed solution proposed is called Bio-Inspired Optimized Leader Election for Multiple Drones (BOLD), which is based on two AI-based optimization techniques. The simulation results of BOLD compared with the existing Particle Swarm Optimization-Cluster head election (PSO-C) in terms of network lifetime and energy consumption, and from the results, it has been proven that the lifetime of drones with the BOLD algorithm is 15% higher than the drones with PSO-C algorithm.

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Spider Monkey Optimization: a survey

Cited by 43 publications

References 39 publications

Discrete Spider Monkey Optimization for Travelling Salesman Problem

Discrete Spider Monkey Optimization for Travelling Salesman Problem

SMO-DNN: Spider Monkey Optimization and Deep Neural Network Hybrid Classifier Model for Intrusion Detection

BOLD: Bio-Inspired Optimized Leader Election for Multiple Drones

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