Social Spider Research Articles

Metaheuristic Optimization with Deep Learning Enabled Smart Grid Stability Prediction

Due to the drastic increase in global population as well as economy, electricity demand becomes considerably high. The recently developed smart grid (SG) technology has the ability to minimize power loss at the time of power distribution. Machine learning (ML) and deep learning (DL) models can be effectually developed for the design of SG stability techniques. This article introduces a new Social Spider Optimization with Deep Learning Enabled Statistical Analysis for Smart Grid Stability (SSODLSA-SGS) prediction model. Primarily, class imbalance data handling process is performed using Synthetic minority oversampling technique (SMOTE) technique. The SSODLSA-SGS model involves two stages of pre-processing namely data normalization and transformation. Besides, the SSODLSA-SGS model derives a deep belief-back propagation neural network (DBN-BN) model for the prediction of SG stability. Finally, social spider optimization (SSO) algorithm can be applied for determining the optimal hyperparameter values of the DBN-BN model. The design of SSO algorithm helps to appropriately modify the hyperparameter values of the DBN-BN model. A series of simulation analyses are carried out to highlight the enhanced outcomes of the SSODLSA-SGS model. The extensive comparative study reported the enhanced performance of the SSODLSA-SGS algorithm over the other recent techniques interms of several measures.

EVMPCSA: Efficient VM Packing mechanism in cloud computing using Chaotic Social Spider Algorithm

VM packing is one of prodigious challenge in cloud computing as it depends on the incoming workload onto virtual resources. Workload in the cloud is highly dynamic and it is difficult to predict the upcoming tasks and place them properly onto an appropriate VM is a challenging task. While scheduling workload onto VMs it is important to pack them in an appropriate physical machine because there is a chance of wastage of virtual resources and which leads to high energy consumption. Therefore, it is necessary to pack VMs into an appropriate Physical machines based on utilization of CPU. Many of authors proposed various consolidation techniques to assess parameters makespan, energy consumption, Througput but still there is a research gap and we can minimize energy consumption based on utilization of CPU in proposed approach i.e. EVMPCSA. Consolidation of VMs and chososing of VM for migration onto a Physical host is based on utilization of CPU. Chaotic Social Spider algorithm is used as a methodology for VM packing mechanism. EVMPCSA uses cpu utilization as constraint and used Chaotic Social Spider algorithm as methodology in this work to solve VM packing problem. It is simulated on Cloudsim and evaluated aganist existing algorithms named as PSO, CS and ACO. When it is compared with PSO, CS and ACO makespan is greatly minimizes by 30.25, 23.5% and 17.31% respectively and energy consumption is minimized for PSO, CS and ACO by 27.6%, 24.78% and 10.09% respectively.

An IoT-Enabled Stochastic Operation Management Framework for Smart Grids

The idea of Internet of Things (IoT) has revolutionized the smart grids by providing the opportunity for remote control and monitoring of the distributed generations (DGs), switches, renewable sources and electrical loads. This study investigates the smart grid concept along with IoT in the automation and optimal energy management of these grids incorporating the high uncertainties of the renewable energy sources and plug-in hybrid electric vehicles (PHEVs) charging demand. A private blockchain technology is developed to make secure data transactions. A novel stochastic structure according to the scenario generation is developed to capture the power randomness of these sources. It is tried to assess the switching effect on the energy management based on remote control in the IoT bed. Moreover, a new optimizer using the social spider algorithm (SSA) is proposed which helps in scheduling the smart grid in an economic way. In addition, a modified method is developed which can suitably enhance the SSA convergence and thus get into more optimal switching and scheduling programs. The proposed model is investigated using an IEEE test system.

Encryption with User Authentication Model for Internet of Medical Things Environment

Internet of Medical Things (IoMT) enabled e-healthcare has the potential to greately improve conventional healthcare services significantly. However, security and privacy become major issues of IoMT because of the restricted processing abilities, storage, and energy constraints of the sensors. Therefore, it leads to infeasibility of developing traditional cryptographic solutions to the IoMT sensors. In order to ensure security on sensitive medical data, effective encryption and authentication techniques need to be designed to assure security of the patients and healthcare service providers. In this view, this study designs an effective metaheuristic optimization based encryption with user authentication (EMOE-UA) technique for IoMT environment. This work proposes an EMOE-UA technique aims to accomplish mutual authentication for addressing the security issues and reducing the computational complexity. Moreover, the EMOE-UA technique employs optimal multikey homomorphic encryption (OMKHE) technique to encrypt the IoMT data. Furthermore, the improved social spider optimization algorithm (ISSOA) was employed for the optimal multikey generation of the MKHE technique. The experimental result analysis of the EMOE-UA technique takes place using benchmark data and the results are examined under various aspects. The simulation results reported the considerably better performance of the EMOE-UA technique over the existing techniques.

Discovery and Genetic Characterization of Single Cohort Adult Colonies With Male Aggregations, and Preliminary Evidence for Lekking in a Malagasy Kite Spider (Isoxya, Gasteracanthinae)

AbstractSpiders are notoriously solitary and cannibalistic, with instances of colonial or social lifestyles in only about 50-60, or ~0.1% of 50,000 described species. Population analyses indicate that most colonies consist of multiple cohorts formed by close relatives. Territorial social spiders facultatively form colonies by interlinking individual webs, but further cooperation is infrequent, and only among juveniles or (rarely) females. In spiders therefore, aggregations of males outside of the male-male competition context has been unknown. Here, we report on a discovery of a kite spider from Madagascar that exhibits unique colonies. We found colonies of the newly described araneid Isoxya manangonan. sp. formed by up to 41 interconnected, single-cohort adult female webs with up to 38 adult males aggregating on a central, single, nonsticky line. With males resting tightly together, we found no evidence for male-male aggression. Genetic analyses from RAD sequencing suggest that most colonies consist of unrelated individuals. Furthermore, genetic variability of males was somewhat less than that of females. Single cohort colonies made up purely of adults, and peaceful male aggregations, have not previously been observed in spiders. Although direct behavioral observations are preliminary, we speculate based on the available evidence that these colonies may represent a novel and first case of lekking in spiders.

Improved Hybrid Swarm Intelligence for Optimizing the Energy in WSN

In this current century, most industries are moving towards automation, where human intervention is dramatically reduced. This revolution leads to industrial revolution 4.0, which uses the Internet of Things (IoT) and wireless sensor networks (WSN). With its associated applications, this IoT device is used to compute the received WSN data from devices and transfer it to remote locations for assistance. In general, WSNs, the gateways are a long distance from the base station (BS) and are communicated through the gateways nearer to the BS. At the gateway, which is closer to the BS, energy drains faster because of the heavy load, which leads to energy issues around the BS. Since the sensors are battery-operated, either replacement or recharging of those sensor node batteries is not possible after it is deployed to their corresponding areas. In that situation, energy plays a vital role in sensor survival. Concerning reducing the network energy consumption and increasing the network lifetime, this paper proposed an efficient cluster head selection using Improved Social spider Optimization with a Rough Set (ISSRS) and routing path selection to reduce the network load using the Improved Grey wolf optimization (IGWO) approach. (i) Using ISSRS, the initial clusters are formed with the local nodes, and the cluster head is chosen. (ii) Load balancing through routing path selection using IGWO. The simulation results prove that the proposed optimization-based approaches efficiently reduce the energy through load balancing compared to existing systems in terms of energy efficiency, packet delivery ratio, network throughput, and packet loss percentage.

An Ontology Based Knowledge Representation for Coordinated SS-bots

Collective behaviour empowers biological species to achieve remarkable swarm level results through distributed actions. For example, social spiders achieve coordinated activities that can lead to plausible outcomes such as preying, mating, or building webs. Developers of simulated swarm systems are increasingly moving away from insinuating individualistic robotic devices, gravitating towards collective swarms to achieve common goals. However, for this to happen, it is imperative to understand the principles that underpin successful simulated coordination of robotic devices in swarms. In this article, we investigate the principles behind artificial swarm systems built on the instinctive behaviours of simulated social spider-like devices (SS-bots). We classify these principles into six interconnected knowledge domains, including (a) environment, (b) SS-bot architecture, (c) SS-bot mission planning, (d) SS-bot communication, (e) SS-bot operators, and (f) metadata and swarm-level data. The key features of each domain are discussed, and an SS-bot ontology is proposed.

Job scheduling problem in fog-cloud-based environment using reinforced social spider optimization

Fog computing is an emerging research domain to provide computational services such as data transmission, application processing and storage mechanism. Fog computing consists of a set of fog server machines used to communicate with the mobile user in the edge network. Fog is introduced in cloud computing to meet data and communication needs for Internet of Things (IoT) devices. However, the vital challenges in this system are job scheduling, which is solved by examining the makespan, minimizing energy depletion and proper resource allocation. In this paper, we introduced a reinforced strategy Dynamic Opposition Learning based Social Spider Optimization (DOLSSO) Algorithm to enhance individual superiority and schedule workflow in Fog computing. The extensive experiments were conducted using the FogSim simulator to generate the dataset and an energy-efficient open-source tool utilized to model and simulate resource management in fog computing. The performance of the formulated model is ratified using two test cases. The proposed algorithm attained the optimized schedule with minimized cost function concerning the CPU processing period and assigned memory. Our simulation outcomes show the efficacy of the introduced technique in handling job scheduling issues, and the results are contrasted with five existing metaheuristic techniques. The results show that the proposed method achieves 10% - 15% better CPU utilization and 5%-10% less energy consumption than the other techniques.

Determining approximate solution of partial differential equations using radial basis function model trained with parallel symbiotic organisms search algorithm

SummaryPartial differential equations (PDEs) find extensive applications in geophysics (weather and climate modeling), astrophysics, and quantum mechanics. Solving PDEs by the conventional methods at times becomes difficult due to the complexity involved in these real‐world problems. Thus instead of an exact solution determining an approximate solution is also helpful for solving the problems. Here a model based on radial basis function (RBF) trained with parallel symbiotic organism search (PSOS) optimization is proposed to find out an approximate solution. The PSOS is a nature‐inspired optimization algorithm that ensures better accuracy due to the potential exploration of agents in the search space. Simultaneously, the computational complexity of this algorithm is low due to inbuilt parallelism. Simulation studies are reported for five real applications of PDEs: 2D modified Helmholtz equation, Poisson problem with Dirichlet boundary condition, elliptic PDE equation, and two convection‐diffusion equations. Comparison is carried out with the same RBF model parameters trained with parallel social spider optimization, original symbiotic organisms search algorithm, real coded genetic algorithm, and particle swarm optimization. The proposed model achieves minimum root mean square errors in most of the cases and achieves it with lower run time compared to the existing algorithms.

Impaired immune function accompanies social evolution in spiders.

An efficient immune system is essential to the survival of many animals. Sociality increases risk of pathogen transmission, which should select for enhanced immune function. However, two hypotheses instead predict a weakened immune function: relaxed selection caused by social immunity/protection, and reduced efficacy of selection due to inbreeding, reproductive skew and female bias in social species that reduce effective population size and accelerate genetic drift. We assessed the effect of social evolution on immune function in a comparative study of two social spider species and their closely related subsocial sister species (genus Stegodyphus). The haemolymph of social species was less efficient in inhibiting bacterial growth of the potentially pathogenic bacteria Bacillus subtilis than that of subsocial species. Reduced efficacy of selection in social species was supported by comparative genomic analysis showing substantially elevated non-synonymous substitutions in immune genes in one of the social species. We propose that impaired immune function results from reduced efficacy of selection because the evolution of sociality in spiders is accompanied by demographic processes that elevate genetic drift. Positive feedback between pathogen-induced local extinctions and the resulting elevation of genetic drift may further weaken responses to selection by pathogens, and threaten species persistence.

Comparative evaluation of optimal Weibull parameters for wind power predictions using numerical and metaheuristic optimization methods for different Indian terrains.

The accurate selection of the wind speed distributions is crucial for a better utilisation of wind energy. The Weibull distribution is most commonly used distribution and hence its parameters need to be optimized. In this study five numerical methods, namely, maximum likelihood method (MLM), graphical method (GM), empirical method of Justus (EMJ), modified maximum likelihood method (MMLM) and wind atlas analysis and application program (WAsP) and three metaheuristic optimization algorithms, namely, social spider optimization (SSO), particle swarm optimization (PSO) and genetic algorithm (GA) are applied for estimating Weibull distribution parameters at three different locations (onshore-Kayathar, nearshore-Jafrabad and offshore-Gulf of Khambhat (GOK) in India and also comparison of numerical and optimization methods are employed to tune the optimal parameters. The accuracy of the methods was evaluated using three different statistical analysis techniques. As per the results, GOK has the maximum wind power density of 450.2W/m2 compared to Jafrabad and Kayathar. It was observed that among the five methods used for Weibull parameters estimation, WAsP method presented a better curve fit with the histogram of the wind speed. The results shows that SSO and PSO presents a comparably better performance than GA in the term of accuracy on the basis of closeness to converged solution.

SSO-RBNN driven brain tumor classification with Saliency-K-means segmentation technique

Early-stage diagnosis of Brain Tumor leads to better chance of cure from this deadliest disease across the globe. Existing schemes on brain tumor classification use machine learning, convolutional neural networks, Generative Adversarial Networks, and deep learning schemes. However, more execution time and uncertain predictions leading additional process to cross-check the obtained results. In this paper, a classification model Saliency-K-mean-SSO-RBNN is formulated including a new hybrid salience-K-mean segmentation technique along with utilizing the advantage of social spider optimization (SSO) algorithm in Radial Basis Neural Network (RBNN). Hybrid Saliency Map with K-means cluster-based segmentation approach is formulated to segment the tumor region. As Saliency map spotlights on eye catching region within target image, segmented image is fetched to feature extraction phase by considering multiresolution wavelet transform, Principal Component, Kurtosis, Skewness, Inverse Difference Moment (IDM), Cosine transform. Feature vector is then processed for an efficient classification using RBNN by optimizing the cluster center through SSO. RBNN with Gaussian kernel depicts a low complex model for classification. Saliency-K-mean-SSO-RBNN and new hybrid Saliency-K-mean segmentation are validated on standard datasets and compared with existing schemes with regard to specificity, precision, sensitivity, F1 score, MCC, Kappa coefficient and complexity. Saliency-K-mean-SSO-RBNN, yielding a classification accuracy in three datasets as 96%, 92%, and 94%.

Improved energy-efficient routing architecture for traffic management system using a hybrid meta-heuristic algorithm in Internet of vehicles

Today, Internet of Vehicles (IoV) applications communicate with smart devices and deploy Intelligent Transportation Systems (ITS) to recognize traffic congestion problems. One of the main challenges of smart cities and IoV platforms is data routing methods for smart traffic congestion problems to navigate the individual information of data transmission as a critical issue in the ITS. Providing a way to extend the Quality of Service (QoS) variables and energy efficiency methods for directing data transmission in routing-based traffic management systems within the IoV environment is an important issue since the energy consumption of IoV devices is a critical issue in low-power saving storage. This paper presents a hybrid Genetic Algorithm and Social Spider Optimization (GA-SSO) algorithm for an energy-aware routing schema for optimizing traffic congestion and smart devices in the IoV environment. After conducting some studies and comparisons, the exactness and prevalence of the proposed schema were received. Experimental results confirmed that the GA-SSO algorithm calculation decides optimal and ideal arrangement for energy-aware routing schema demonstrate by essentially making strides the execution of QoS variables can oversee information blockage comes from data transmission between IoV nodes.

A predictive vampire attack detection by social spider optimized Gaussian mixture model clustering

SummaryA sensor node carries out a specific function in a wireless sensor network. Wireless sensor networks are less bearable but more vital to the industry and the people because of threats. As a result, a node's battery life will be drastically reduced, and the node will be rendered completely inoperable, which is the most extreme form of denial of service attack. A vampire attack, one of the denials of service attacks, may inflict extensive harm to the network, making it harder to detect and using more energy than necessary. This article has proposed a novel vampire attack detection and prevention by the energy consumption prediction in the data path with the least error as low as . In combination with the social spider optimized Gaussian mixture model, the gray prediction model is used to detect and prevent the attack. The energy prediction scheme calculates a cooperative trust score and is categorized by the optimized Gaussian mixture model. The algorithm is validated with recent state‐of‐the‐art schemes and the detection accuracy improvement of up to 35.27% is achieved.

Extended phenotypes can underlie trade-offs: a case of social spiders

Extended phenotypes engineered by animals can potentially improve safety and/or foraging. Whether the well-known trade-off between safety and foraging applies for extended phenotypes, and if so, how it is resolved has not been determined. Spiders build elaborate silk structures that serve as traps for their insect prey and often attach silken retreats (nests) to their capture webs. These extended phenotypes of spiders are made of silk that is considered costly since it is made of protein. Using the Indian social spider, Stegodyphus sarasinorum, we examined how simple proximal factors, like colony hunger state and group size, shape trade-offs in collectively built extended phenotypes that offer shelter and food. We found that well-fed colonies showed greater investment in retreat silk than starved colonies. However, the two groups did not differ in their investment in capture webs. Hence, our findings validate the starvation-risk taking hypothesis in an extended phenotypic paradigm by showing that hungry colonies trade-off retreat size for capture web, irrespective of group size.

Kleptoparasites of social spider colonies do not track hosts' subdivided population structure.

Organisms with lower dispersal abilities tend to have more genetically dissimilar populations. The same is true for parasites, whose transmission frequency may depend on the population structure of the host. This should be especially true when hosts and parasites face similar barriers to dispersal. Here, we considered the similarities between host and parasite population structure in a social spider system. In this system, host colonies are typified by rapid growth via internal recruitment followed by budding or fission events when colonies grow too large, with each colony representing a distinct population. Host colonies provide habitat for kleptoparasitic spiders, which steal prey from and may also feed directly on host individuals. We asked whether kleptoparasites exhibit a similar degree of population subdivision as their host. Under the free-mixing hypothesis (i.e., horizontal transmission), kleptoparasites would have well-mixed populations across broader regions than a single host nest, whereas host populations would be strongly genetically structured. Under the host-tracking hypothesis (i.e., vertical transmission), kleptoparasites would have a population structure that parallels that of the host. We conducted a genotype-by-sequencing study to assess the population structure of both hosts and kleptoparasites within three nearby regions in eastern Ecuador. We found strong signatures of population differentiation and bottlenecks in the host species, which is congruent with past studies. However, we found that kleptoparasite populations were well mixed across host nests, with no evidence of recent bottlenecks. These results support our free-mixing hypothesis, suggesting that kleptoparasites follow patterns of horizontal transmission in this social spider system.

Aggression in a western Amazonian colonial spider, Philoponella republicana (Araneae: Uloboridae)

Group-living spiders are rare, and can be divided into multiple subcategories based on their tolerance of group mates. While social spiders are cooperative, colonial spiders are often antagonistic towards conspecifics. We examined colony dynamics in a colonial species, Philoponella republicana (Simon, 1891), focusing on aggressive behaviors to further understand this understudied species. We studied whether web region, sex ratio, web size, or spider size affected aggression. We also tested whether colony members discriminate against conspecific intruders, since this behavior, known as group closure, is prevalent in many other group-living animals but had not yet been tested in colonial spiders. Colony mates were often aggressive due to competition for limited resources, such as mates and orb webs, yet several characteristics of this species may reduce these competitive forces. First, female-biased secondary sex ratios appear to reduce male-male and female-male competition. Moreover, although some individuals defended orb webs, other areas in the communal web were not defended. Philoponella republicana also did not exhibit group closure. Our results further confirm that aggression between males decreases in colonies with more female-biased secondary sex ratios, and larger individuals correlate with a higher frequency of aggressive interactions. Moreover, we raise new questions concerning the evolutionary pressures that shape coloniality in spiders.

A novel metaheuristic optimisation approach for text sentiment analysis

Automated sentiment analysis is considered an area in natural language processing research that seeks to understand a text author's mood, thoughts, and feelings. New opportunities and challenges have arisen in this field due to the popularity and accessibility of a variety of resources of ideas, such as online review websites, personal blogs, and social media. Feature selection, which can be conducted using metaheuristic algorithms, is one of the steps of sentiment analysis. It is crucial to use high-performing algorithms for feature selection. This paper applies the Horse herd Optimisation Algorithm (HOA) for feature selection in text sentiment analysis. HOA is a metaheuristic algorithm and uses six key behaviours to simulate the social performance of horses of various ages, to solve high-dimensional optimisation problems. In order to improve HOA, this paper adds another behaviour of horses to the basic algorithm; thus, the new algorithm uses seven key behaviours of horses of different ages to imitate their social performance. It is then discretised and converted to a multi-objective algorithm. The improved algorithm's performance is evaluated using 15 CEC benchmark functions, and the results are compared to the Binary Social Spider Algorithm, the Binary Grey Wolf Optimizer, and the Binary Butterfly Optimization Algorithm. The new algorithm, the Multi-objective Binary Horse herd Optimisation Algorithm (MBHOA), excels at solving high-dimensional complex problems. To evaluate the algorithm's performance in feature selection, as a practical example, it is employed in text sentiment analysis and examined on various data sets. The simulation results indicate that MBHOA has a better performance in analysing sentiment compared to similar approaches.

Adapting to climate with limited genetic diversity: Nucleotide, DNA methylation and microbiome variation among populations of the social spider Stegodyphus dumicola.

Understanding the role of genetic and nongenetic variants in modulating phenotypes is central to our knowledge of adaptive responses to local conditions and environmental change, particularly in species with such low population genetic diversity that it is likely to limit their evolutionary potential. A first step towards uncovering the molecular mechanisms underlying population-specific responses to the environment is to carry out environmental association studies. We associated climatic variation with genetic, epigenetic and microbiome variation in populations of a social spider with extremely low standing genetic diversity. We identified genetic variants that are associated strongly with environmental variation, particularly with average temperature, a pattern consistent with local adaptation. Variation in DNA methylation in many genes was strongly correlated with a wide set of climate parameters, thereby revealing a different pattern of associations than that of genetic variants, which show strong correlations to a more restricted range of climate parameters. DNA methylation levels were largely independent of cis-genetic variation and of overall genetic population structure, suggesting that DNA methylation can work as an independent mechanism. Microbiome composition also correlated with environmental variation, but most strong associations were with precipitation-related climatic factors. Our results suggest a role for both genetic and nongenetic mechanisms in shaping phenotypic responses to local environments.

Brain tumor segmentation of MR images using SVM and fuzzy classifier in machine learning

Medical image processing is a rapidly growing and concentrating topic today. Medical image analysis techniques are used to diagnose and cure illnesses. One such fundamental and potentially fatal illness is brain tumor, which is an abnormal growth of brain cells within the brain. Due to the complexity of the brain's anatomy. To improve efficiency and reduce the complexity of the picture segmentation process, this work investigated computer tomography (CT)-based brain tumor segmentation. CT scans are often used to diagnose head traumas, malignancies, and skull fractures. The images from the brain tumor database are evaluated in this study effort, and a preprocessing approach called adaptive median filter is used to increase the image's clarity. The preprocessing stage eliminates noise and high-frequency artifacts from the pictures. The median filter is a type of nonlinear digital filter commonly used to reduce noise in a picture or signal. Regardless of the preprocessing technique used, feature extraction techniques are updated, and then classification procedures such as the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM) classifier are applied to the picture to classify it as normal or abnormal. Following classification, aberrant images are tracked and selected for segmentation using the Fuzzy C-Means (FCM) clustering technique and associated optimization techniques. In the suggested technique, centroid optimizations such as Grey Wolf Optimization (GWO) and Social Spider Optimization (SSO) combined with a Genetic Algorithm (GA) are utilized to improve the accuracy of the FCM centroid. The suggested work produces the most extreme execution in tumour picture segmentation evaluation appears differently from other works. The conclusion indicates that the hybrid technique (SSO-GA) obtains the highest accuracy of 99.24% compared to other individual algorithms. MATLAB 2014 is utilized to implement the brain tumor classification and segmentation algorithms in this research effort.