Spider Monkey Optimization Research Articles

Implementation of Enhanced Spider Monkey Optimization for D2D Communication through IoT

Implementation of Enhanced Spider Monkey Optimization for D2D Communication through IoT

Hybrid metaheuristic technique for optimal container resource allocation in cloud

Resource allocation in cloud is becoming more challenging and complex due to the intensifying requirements of cloud services. Efficient management of virtual resources in the cloud is of enormous importance, as it has a large impact on both the operational cost and scalability of the cloud’s surroundings. However, one of the most difficult aspects of cloud computing is optimal resource allocation. The resource allocation is done using the objective of reducing the costs connected with it. Hence, an attempt is made to optimize the resource allocation of containers using a new Combined Spider and Honey Bee Optimization (CS-HBO). In the proposed model, the optimal allocation relies on certain constraints like “Total Network Distance (TND), System Failure, Balanced Cluster Use, and threshold distance”. At last, the supremacy of the presented approach is examined over prevailing techniques regarding various metrics such as cost and convergence analysis. Accordingly, for cost analysis, the proposed method attains the least cost, which is 65.57%, 4.15%, 1.04%, 6.87% and 6.76% improved than existing Marriage in Honey Bee optimization (MHBO), Spider Monkey Optimization (SMO), Whale Random update assisted Lion Algorithm (WR-LA), Genetic Algorithms (GA), and Accelerated particle swarm optimization (APSO) models at the 100th iteration.

A discrete cuckoo search algorithm for traveling salesman problem and its application in cutting path optimization

The Traveling Salesman Problem (TSP) is a classic combinatorial optimization problem with various industrial applications. This paper proposes a discrete cuckoo search algorithm based on random walk and cluster analysis to solve the traveling salesman problem (TSP). Although the original cuckoo search algorithm is not suitable to solve the TSP, this algorithm is modified to solve the TSP. Lévy flights and random preference walk in the original cuckoo search algorithm are replaced with novel tools, including the local adjustment operator and discrete random walk. The former is utilized to preserve superior solutions found by the algorithm, while the latter is employed to maintain the diversity of the population. For large-scale TSP problems, the k-means algorithm is first utilized to divide cities into k categories for optimization, while random algorithms are adopted to combine them. A simple 2-opt operator is utilized as the local optimization operator to accelerate the algorithm's convergence rate. Multiple groups of standard TSPLIB datasets are chosen to compare the proposed method with state-of-the-art optimization algorithms that also use the 2-opt/k-opt optimization operator. According to the experimental results, the stability and accuracy of the proposed algorithm are superior to the state-of-the-art optimization algorithms. Regarding the solution accuracy, the algorithm improves 0.39%, 0.29%, 0.84%, 0.4%, 0.07% and 2.22% in the optimal solution over discrete cuckoo search (DCS), discrete bat algorithm (DBA), discrete sine–cosine algorithm (DSCA), random-key cuckoo search (RKCS) and discrete spider monkey optimization (DSMO) on the standard test cases, and 0.58%, 0.19%, 0.85%, 0.43%, 0.36% and 5.83% in the average optimal solution. Regarding stability, the variance of the optimal solutions of RKCS, DSMO, and novel discrete differential evolution (NDEE) for 30 independent runs is 38.5, 52.6, and 59.4 times higher than that of the algorithm in this paper. Finally, the proposed algorithm optimizes air knife migration during glass cutting. Accordingly, the path for air knife migration is reduced by 8556 mm.

UCAV Path Planning for Avoiding Obstacles using Cooperative Co-evolution Spider Monkey Optimization

The unmanned combat aerial vehicle (UCAV) path planning problem is a complex global optimization problem that aims to seek an optimal flight route that avoids the threats and constraints on the battlefield. However, as the number and degree of the path threats increases, the recent UCAV path planning algorithms all suffer from being trapped into local optima with a low convergence rate, which leads to the poor performance. Therefore, to resolve these problems, we propose a Cooperative co-Evolution (CE)-based Spider Monkey Optimization (SMO) algorithm, named CESMO to address UCAV path planning problem for avoiding obstacles. First, a cooperative co-evolution strategy is proposed to prevent the algorithms from falling into local optima easily, enhancing the coherence of the path. After that, a further division is applied to the algorithm for diversifying the search direction of each individual in the sub-groups. Finally, the spider monkey optimization-based search mechanism is designed to further strengthen the search efficiency and boost the convergence rate. Experimental results demonstrate that our proposed method is more competitive than other state-of-the-art evolutionary algorithms for UCAV path planning problem considering the quality and stability of the final paths.

Enhancement of Supply Chain Management through Educating Key Processes with the Hub and Spoke Model Based on Multi-Adaptive Spider Monkey Optimization Algorithm

Supply chain performance improvement has become a critical issue for gaining a competitive edge for companies. Over the last decade of the evolution of supply chain management (SCM), a steady stream of research and articles dealing with the theory and practice of SCM have been published, but the topic of performance measurement has not received adequate consideration. However, many critical drawbacks prevent the existing performance measurement systems from making a significant contribution to the development and improvement of SCM and thus, several researches is still needed in this area. So, we present a unique multi-adaptive spider monkey optimization algorithm (MASMOA) to enhance the ability of hub and spoke model to educate the key process throughout the SCM. Initially, for the pre-processing step, we use a normalizing technique to remove unwanted noises from the raw dataset, and then we use principal component analysis (PCA) to extract the unwanted features from the pre-processed data. The information was then encrypted and fed into the hub-and-spoke model for data protection purpose. The proposed method is then used to optimize the hub and spoke model's transport process over hubs. The optimized data is then decoded using a decryption technique to transform the encrypted information into actual format. The Rivest–Shamir–Adleman (RSA) algorithm was used for the encryption and decryption operations. Finally, performance measures such as encryption and decryption execution time, security rate, and scalability level are compared to comparable techniques. Using the MATLAB environment, the results are shown.

ARPS: An Autonomic Resource Provisioning and Scheduling Framework for Cloud Platforms

With Cloud computing becoming mainstream for the execution of various applications, the multi-objective scheduling algorithms for providing the most suitable services to users have gained much attention. As provisioning Cloud services that satisfy end-users quality of service (QoS) requirements is complex and challenging, scheduling algorithms for cloud computing tend to focus on optimizing the execution cost or the execution time within user-defined deadline constraints. This paper addresses the problem of efficiently allocating Cloud services among competing jobs to achieve multiple end-users QoS. We design and develop a framework called Autonomic Resource Provisioning and Scheduling (ARPS) framework. ARPS framework has the decision-making capability to schedule the jobs at the best resources within the deadline and optimizes both the execution time and the cost simultaneously. The ARPS framework is also integrated with the spider monkey optimization (SMO) algorithm based scheduling mechanism. Our proposed mechanism is intended to solve a multi-objective optimization problem, including minimizing processing time, cost, and energy consumption. We study the effectiveness of the proposed scheduling mechanism through extensive simulation analysis using Cloudsim To assess the relative performance of our method, we compare it against four existing mechanisms. Experimental results show that the proposed mechanism outperforms its counterparts.

Spider Taylor-ChOA: Optimized Deep Learning Based Sentiment Classification for Review Rating Prediction

The prediction of review rating is an imperative sentiment assessment task that aims to discover the intensity of users’ sentiment toward a target product from several reviews. This paper devises a technique based on sentiment classification for predicting the review rating. Here, the review data are taken from the database. The significant features, such as SentiWordNet-based statistical features, term frequency–inverse document frequency (TF-IDF), number of capitalized words, numerical words, punctuation marks, elongated words, hashtags, emoticons, and number of sentences are mined in feature extraction. The features are mined for sentiment classification, which is performed by random multimodal deep learning (RMDL). The training of RMDL is done using the proposed Spider Taylor-ChOA, which is devised by combining spider monkey optimization (SMO) and Taylor-based chimp optimization algorithm (Taylor-ChOA). Concurrently, the features are considered input for the review rating prediction, which determines positive and negative reviews using the hierarchical attention network (HAN), and training is done using proposed Spider Taylor-ChOA. The proposed Spider Taylor-ChOA-based RMDL performed best with the highest precision of 94.1%, recall of 96.5%, and highest F-measure of 95.3%. The proposed spider Taylor-ChOA-based HAN performed best with the highest precision of 93.1%, recall of 95.4% and highest F-measure of 94.3%.

An Optimized Energy Efficient Routing for Wireless Sensor Network using Improved Spider Monkey Optimization Algorithm

An Optimized Energy Efficient Routing for Wireless Sensor Network using Improved Spider Monkey Optimization Algorithm

Improved spider monkey optimization‐based multi‐objective software‐defined networking routing with block chain technology for Internet of Things security

AbstractInternet of Things (IoT) can be connected with remote smart devices to offer global connectivity, which consists of handling, sensing, and transmission of real‐time data gathered from different devices with human intervention. The conventional security schemes are also faced with limitations like collecting and monitoring data through the IoT network to provide defense against cyber‐attacks for delivering the optimal security. To address the current challenges, this proposed model plans for integrating the energy‐efficient software‐defined networking (SDN) and block chain‐based IoT networks. The proposed model adopts a new meta‐heuristic‐based clustering protocol for optimal communication. The architecture uses the private and public block chains for offering the peer to peer communication to eliminate the proof‐of‐work (POW) issue. The contribution of a deep learning algorithm called optimized deep neural network (ODNN) is employed for detecting the malicious IoT device based on the energy characteristics of each node. Once the malicious nodes are detected, the optimal cluster head selection and optimal routing is accomplished by the adoption of neighborhood‐based spider monkey optimization (N‐SMO). The experimentation results show that the proposed model ensures the efficient cluster head selection and optimal routing protocol on the cluster structure with the traditional meta‐heuristic‐based algorithms.

Extending lifetime of heterogeneous wireless sensor networks using spider monkey optimization routing protocol

The nodes of wireless sensor networks (WSN) are severely restricted in terms of computing capabilities, limited communications, and limited power supplies, as it is difficult or impossible to replace or recharge the sensor battery. Consequently, the energy of nodes is one of the most important resources to consider when designing of WSNs. So, most of the routing protocols in WSNs are to assure the saving of energy as a significant aim for improvement. Nevertheless, just providing power is not sufficient to extend the lifetime of WSN. Where unbalance energy depletion in WSNs is a challenging issue often leading to splits the network and reduces its lifetime, also retrogression of its performance. This paper, therefore, uses a powerful routing protocol named spider monkey optimization routing protocol (SMORP) to generate an optimal data routing of the pathway for heterogeneous WSNs (HWSNs). SMORP, here, can compute the best way from a sensor to the sink through the cluster head, inside the intra-cluster, and the inter-cluster respectively. For this purpose and the organization of heterogeneous nodes, this paper uses the clustering partition. The simulation results revealed that SMORP significantly improves in terms of data latency reduction, stabilizing depletion of energy, and maximizing the network lifetime for HWSNs.

Smart Spider Monkey Optimization (SSMO) for Energy-Based Cluster-Head Selection Adapted for Biomedical Engineering Applications.

Using energy efficiency to increase the life and sustainability of wireless sensor networks (WSNs) for biomedical applications is still a challenge. Clustering has boosted energy productivity by allowing cluster heads to be categorized, but its implementation is still a challenge. Existing cluster head selection criteria start with determining acceptable cluster head locations. The cluster heads are picked from the nodes that are most closely connected with these places. This location-based paradigm incorporates challenges such as faster processing, less precise selection, and redundant node selection. The development of the sampling-based smart spider monkey optimization (SSMO) approach is addressed in this paper. If the sample population's nodes are varied, network nodes are picked from among them. The problems with distributed nodes and cluster heads are no longer a concern. This article shows how to use an SSMO and smart CH selection to increase the lifetime and stability of WSNs. The goal of this study is to look at how cluster heads are chosen using standard SMO and sampling-based SMO for biomed applications. Low-energy adaptive clustering hierarchy centralized (LEACH-C), particle swarm optimization clustering protocol (PSO-C), and SSMO improved routing protocol measurements are compared to those obtained in homogeneous and heterogeneous settings using equivalent methodologies. In these implementations, SSMO boosts network longevity and stability periods by an estimated 12.22%, 6.92%, 32.652%, and 1.22%.

A novel hybrid Chi-Mo optimisation algorithm-based PV-fed STATCOM for performance improvement of power distribution system

The use of solar photovoltaic (PV) energy is increasing due to rising energy demand and environmental concerns, providing a sustainable energy supply in a clean environment and satisfying the need of large-scale industries. PV-fed power system causes various power quality problems in the system, which degrades its performance. The power quality problems are handled by one of FACTS devices such as STATCOM. In this paper, a novel optimisation method called hybrid Chi-Mo is implemented to optimise the parameters of the proportional–integral controller, which controls the STATCOM operation. The proposed hybrid Chi-Mo meta-heuristic algorithm combines standard characteristic features such as prognosticate and fission–fusion characteristics. The system's effectiveness is evaluated using comparative analysis with different optimisation methods such as Particle swarm optimisation (PSO), Chimp optimisation algorithm (COA), Spider Monkey Optimisation (SMO), and it is investigated that the proposed system gives better performance compared with other ones.

Nursing Value Analysis and Risk Assessment of Acute Gastrointestinal Bleeding Using Multiagent Reinforcement Learning Algorithm.

Gastrointestinal bleeding (GIB) indicates an issue in the digestive system. Blood can be found in feces or vomiting; however, it is not always visible, even if it makes the stool appear darkish or muddy. The bleeding can range in harshness from light to severe and can be dangerous. It is advised that nursing value analysis and risk assessment of patients with GIB is essential, but existing risk assessment techniques function inconsistently. Machine learning (ML) has the potential to increase risk evaluation. For evaluating risk in patients with GIB, scoring techniques are ineffective; a machine learning method would help. As a result, we present а unique machine learning-based nursing value analysis and risk assessment framework in this research to construct a model to evaluate the risk of hospital-based interventions or mortality in individuals with GIB and make a comparison to that of other rating systems. Initially, the dataset is collected, and preprocessing is done. Feature extraction is done using local binary patterns (LBP). Classification is performed using a fuzzy support vector machine (FSVM) classifier. For risk assessment and nursing value analysis, machine learning-based prediction using a multiagent reinforcement algorithm is employed. For improving the performance of the proposed system, we use spider monkey optimization (SMO) algorithm. The performance metrics like classification accuracy, area under the receiver-operating characteristic curve (AUROC), area under the curve (AUC), sensitivity, specificity, and precision are analyzed and compared with the traditional approaches. In individuals with GIB, the suggested technique had a good–excellent prognostic efficacy, and it outperformed other traditional models.

An Intelligent Cloud Service Composition Optimization Using Spider Monkey and Multistage Forward Search Algorithms

Web service composition allows developers to create and deploy applications that take advantage of the capabilities of service-oriented computing. Such applications provide the developers with reusability opportunities as well as seamless access to a wide range of services that provide simple and complex tasks to meet the clients’ requests in accordance with the service-level agreement (SLA) requirements. Web service composition issues have been addressed as a significant area of research to select the right web services that provide the expected quality of service (QoS) and attain the clients’ SLA. The proposed model enhances the processes of web service selection and composition by minimizing the number of integrated Web Services, using the Multistage Forward Search (MSF). In addition, the proposed model uses the Spider Monkey Optimization (SMO) algorithm, which improves the services provided with regards to fundamentals of service composition methods symmetry and variations. It achieves that by minimizing the response time of the service compositions by employing the Load Balancer to distribute the workload. It finds the right balance between the Virtual Machines (VM) resources, processing capacity, and the services composition capabilities. Furthermore, it enhances the resource utilization of Web Services and optimizes the resources’ reusability effectively and efficiently. The experimental results will be compared with the composition results of the Smart Multistage Forward Search (SMFS) technique to prove the superiority, robustness, and effectiveness of the proposed model. The experimental results show that the proposed SMO model decreases the service composition construction time by 40.4%, compared to the composition time required by the SMFS technique. The experimental results also show that SMO increases the number of integrated ted web services in the service composition by 11.7%, in comparison with the results of the SMFS technique. In addition, the dynamic behavior of the SMO improves the proposed model’s throughput where the average number of the requests that the service compositions processed successfully increased by 1.25% compared to the throughput of the SMFS technique. Furthermore, the proposed model decreases the service compositions’ response time by 0.25 s, 0.69 s, and 5.35 s for the Excellent, Good, and Poor classes respectively compared to the results of the SMFS Service composition response times related to the same classes.

A developed framework for multi-document summarization using softmax regression and spider monkey optimization methods

A developed framework for multi-document summarization using softmax regression and spider monkey optimization methods

Modified PNN classifier for diagnosing skin cancer severity condition using SMO optimization technique

<abstract> <p>Skin cancer is a pandemic disease now worldwide, and it is responsible for numerous deaths. Early phase detection is pre-eminent for controlling the spread of tumours throughout the body. However, existing algorithms for skin cancer severity detections still have some drawbacks, such as the analysis of skin lesions is not insignificant, slightly worse than that of dermatologists, and costly and time-consuming. Various machine learning algorithms have been used to detect the severity of the disease diagnosis. But it is more complex when detecting the disease. To overcome these issues, a modified Probabilistic Neural Network (MPNN) classifier has been proposed to determine the severity of skin cancer. The proposed method contains two phases such as training and testing the data. The collected features from the data of infected people are used as input to the modified PNN classifier in the current model. The neural network is also trained using Spider Monkey Optimization (SMO) approach. For analyzing the severity level, the classifier predicts four classes. The degree of skin cancer is determined depending on classifications. According to findings, the system achieved a 0.10% False Positive Rate (FPR), 0.03% error and 0.98% accuracy, while previous methods like KNN, NB, RF and SVM have accuracies of 0.90%, 0.70%, 0.803% and 0.86% correspondingly, which is lesser than the proposed approach.</p> </abstract>

Spider Monkey Optimization based Energy-Efficient Resource Allocation in Cloud Environment

The origin of Cloud computing is from the principle of utility computing, which is characterized as a broadband service providing storage and computational resources. It provides a large variety of processing options and heterogeneous tools, allowing it to meet the needs of a wide range of applications at different levels. As a result, resource allocation and management are critical in cloud computing. In this work, the Spider Monkey Optimization (SMO) is used for attaining an optimized resource allocation. The key parameters considered to regulate the performance of SMO are its application time, migration time, and resource utilization. Energy consumption is another key factor in cloud computation which is also considered in this work. The Green Cloud Scheduling Model (GCSM) is followed in this work for the energy utilization of the resources. This is done by scheduling the heterogeneity tasks with the support of a scheduler unit which schedules and allocates the tasks which are deadline-constrained enclosed to nodes which are only energy-conscious. Assessing these methods is formulated using the cloud simulator programming process. The parameter used to determine the energy efficiency of this method is its energy consumption. The simulated outcome of the proposed approach proves to be effective in response time, makespan, energy consumption along with resource utility corresponding to the existing algorithms.

Hybrid meta-heuristic-based inventory management using block chain technology in cloud sector

This paper plans to develop an inventory management model in the supply chain with cloud and block chain assistance. This paper considers the inventory management of a supply chain that involves multiple suppliers, manufacturers, and distributers. The data management is done by the block chain technology, where each distributer holds a hash function to store its data, which cannot be restored by the other distributers. The proposed model intends to reduce the multi-objective inventory cost involving transaction cost, inventory holding cost, shortage cost, transportation cost, and time cost. The hybridisation of two meta-heuristic algorithms like spider monkey optimisation (SMO) and sea lion optimisation (SLnO) termed as spider monkey local leader-based sea lion optimisation algorithm (SMLL-SLnO) is used to improve the inventory management model. Finally, the feasibility and effectiveness of the proposed optimisation model are validated by comparing over the other traditional models.

Spider Monkey Optimization with Statistical Analysis for Robust Rainfall Prediction

Rainfall prediction becomes popular in real time environment due to the developments of recent technologies. Accurate and fast rainfall predictive models can be designed by the use of machine learning (ML), statistical models, etc. Besides, feature selection approaches can be derived for eliminating the curse of dimensionality problems. In this aspect, this paper presents a novel chaotic spider money optimization with optimal kernel ridge regression (CSMO-OKRR) model for accurate rainfall prediction. The goal of the CSMO-OKRR technique is to properly predict the rainfall using the weather data. The proposed CSMO-OKRR technique encompasses three major processes namely feature selection, prediction, and parameter tuning. Initially, the CSMO algorithm is employed to derive a useful subset of features and reduce the computational complexity. In addition, the KRR model is used for the prediction of rainfall based on weather data. Lastly, the symbiotic organism search (SOS) algorithm is employed to properly tune the parameters involved in it. A series of simulations are performed to demonstrate the better performance of the CSMO-OKRR technique with respect to different measures. The simulation results reported the enhanced outcomes of the CSMO-OKRR technique with existing techniques.

Diabetic macular oedema classification using gradient adaptive thresholding integrated active contour and ant lion spider monkey optimisation-based generative adversarial network

Diabetic macular oedema (DME) is an eye disease, which can highly affect the visual activity for the diabetic patients. The imaging tool, optical coherence tomography (OCT) is used for diagnosis by the ophthalmologists for retinal disease identification. A novel gradient-based adaptive thresholding integrated active contour ant lion spider monkey optimisation driven general adversarial network (G-AT_AC+ALSMO-GAN) is introduced for the DME detection. Here, G-AT_AC scheme is applied for layer segmentation process. In addition, texture features, layer specific features, and image level features are mined for effective classification. The DME classification is carried out using GAN classifier, which is trained by developed ALSMO algorithm, which is the integration of the ant lion optimisation (ALO) and spider monkey optimisation (SMO). During the classification process, 12 layers and 13 boundaries are used for the segmentation process. The DME affected region is classified by the GAN classifier and the classified output is normal or affected one. The proposed method obtained the maximal accuracy, specificity and sensitivity of 94.12%, 92.77% and 98% respectively.