Social Spider Optimization Algorithm Research Articles

Cost optimization of tall buildings having tube composite columns using social spider algorithm

SummaryThis study aims to develop an algorithmic approach to obtain optimum designs for tall buildings having composite columns and investigate the material cost advantages of these buildings over steel structures. The social spider optimization (SSO) algorithm, a new meta‐heuristic optimization method that has shown promising results in optimizing frame structures, was used to obtain the optimum designs. Concrete‐filled steel tube sections were chosen for composite columns. To define the optimization problem, we considered the material cost of the structure as the objective function, the size of columns (strength, deflection, drift, and geometric limitations) as the constraint functions, and ready steel sections as the design variables. We tested eight different frame structures of varying heights and irregularities to analyze how cost varied according to these parameters. Our results demonstrate that composite columns are a more cost‐effective option than steel structures, even for buildings that are not considered high rises. We found that the difference in cost between the two types of structures increases with building height and irregularity. Additionally, our optimization algorithm was unable to find feasible designs for steel structures taller than 180 m using ready steel profiles.

Optimizing support vector machine (SVM) by social spider optimization (SSO) for edge detection in colored images

Edge detection in images is a vital application of image processing in fields such as object detection and identification of lesion regions in medical images. This problem is more complex in the domain of color images due to the combination of color layer information and the need to achieve a unified edge boundary across these layers, which increases the complexity of the problem. In this paper, a simple and effective method for edge detection in color images is proposed using a combination of support vector machine (SVM) and the social spider optimization (SSO) algorithm. In the proposed method, the input color image is first converted to a grayscale image, and an initial estimation of the image edges is performed based on it. To this end, the proposed method utilizes an SVM with a Radial Basis Function (RBF) kernel, in which the model's hyperparameters are tuned using the SSO algorithm. After the formation of initial image edges, the resulting edges are compared with pairwise combinations of color layers, and an attempt is made to improve the edge localization using the SSO algorithm. In this step, the optimization algorithm's task is to refine the image edges in a way that maximizes the compatibility with pairwise combinations of color layers. This process leads to the formation of prominent image edges and reduces the adverse effects of noise on the final result. The performance of the proposed method in edge detection of various color images has been evaluated and compared with similar previous strategies. According to the obtained results, the proposed method can successfully identify image edges more accurately, as the edges identified by the proposed method have an average accuracy of 93.11% for the BSDS500 database, which is an increase of at least 0.74% compared to other methods.

WSSOA: whale social spider optimization algorithm for brain tumor classification using deep learning technique

WSSOA: whale social spider optimization algorithm for brain tumor classification using deep learning technique

Effective social spider optimization algorithms for distributed assembly permutation flowshop scheduling problem in automobile manufacturing supply chain

This paper presents a novel distributed assembly permutation flowshop scheduling problem (DAPFSP) based on practical problems in automobile production. Different from the existing research on DAPFSP, this study considers that each component of the final product is composed of more than one part. Components are processed in a set of identical components manufacturing factories and are assembled into products in the assembly factory. The integration of manufacturing processes is an important objective of Industry 4.0. For solving this problem with the minimum makespan criterion, we introduce a three-level representation and a novel initialization method. To enhance the search ability of the proposed algorithms, we design three local search methods and two restart procedures according to characteristics of the problem. Then, by incorporating the problem specific knowledge with the social spider optimization algorithm (SSO), we propose three SSO variants: the SSO with hybrid local search strategies (HSSO), the HSSO with restart procedures (HSSOR), and the HSSOR with self-adaptive selection probability (HSSORP). Finally, 810 extended instances based on the famous instances are used to test the proposed algorithms. In most cases, HSSOR performs the best, with an average comparison metric value of 0.158% across three termination conditions, while the average comparison metric value for the best comparison method is 2.446%, which is 15.481 times that of HSSOR. Numerical results demonstrate that the proposed algorithms can solve the problem efficiently.

Retraction Note: Implementation of the parallel robot using FOPID with fuzzy type-2 in use social spider optimization algorithm

Retraction Note: Implementation of the parallel robot using FOPID with fuzzy type-2 in use social spider optimization algorithm

Customer preference analysis towards online shopping decisions based on optimized feature extraction

AbstractOnline shopping has become an essential part of modern life, with food delivery being one of the most popular services. In this study, we investigate the factors influencing customers' preferences in online food ordering, such as easy payment, customization, and fast delivery. Analysing customer ratings and reviews is crucial in ensuring the quality of food services and promoting a healthy lifestyle. However, the trustworthiness of online reviews remains a challenge. Based on their rating, new customers can get help from online shoppers for their preferences or demands. Data are collected from customer reviews through online platforms to improve food service quality in online shopping. This work aims to predict the preferences of customers in online shopping based on optimized feature extraction using Principal Component Analysis with a Social Spider Optimization (PCA‐SSO) algorithm. Despite existing algorithms for analysing customer preferences, most may have inaccurate predictions or take time to analyse data. To overcome these challenges, this model of PCA‐SSO is proposed to predict customers' more accurate online shopping preferences. This model provides valuable implications for online shopping owners regarding customer satisfaction. Compared to the accuracy rates of KNN (78.22%), LDA (83.16%), and PCA (88.56%) algorithms, our proposed algorithm of PCA‐SSO achieved a higher accuracy of 93.54%.

Solving a job shop scheduling problem

ABSTRACT Job shop scheduling is a highly nondeterministic polynomial combinatorial issue. In this regard, optimization is essential for reducing the time consumed to perform a task. This research paper proposes an evolved social spider optimization method to deal with the job shop scheduling problem. The evolved social spider optimization method was developed by combining both the differential evolutionary and the social spider optimizations. The key proposal is to minimize the makespan time and solve Job shop scheduling problems to improve productivity. The differential evolutionary algorithm is integrated into the spider position update to boost the exploration capabilities of the social spider optimization algorithm. The time taken for ‘‘i’ jobs and ‘‘j’ machines to perform their tasks is studied. Consequently, 23 benchmark problems were prosperously studied utilizing the proposed techniques, and the computational results were compared with previous Meta heuristics methods. An all-inclusive comparison process was carried out to rate the efficiency of the existing optimization techniques in solving job shop scheduling problems. The proposed method of evolved social spider optimization has emerged as the most promising methodology in solving the job shop scheduling problem by consuming minimum makespan time.

Speed Control of Wheeled Mobile Robot by Nature-Inspired Social Spider Algorithm-Based PID Controller

Mobile robot is an automatic vehicle with wheels that can be moved automatically from one place to another. A motor is built in its wheels for mobility purposes, which is controlled using a controller. DC motor speed is controlled by the proportional integral derivative (PID) controller. Kinematic modeling is used in our work to understand the mechanical behavior of robots for designing the appropriate mobile robots. Right and left wheel velocity and direction are calculated by using the kinematic modeling, and the kinematic modeling is given to the PID controller to gain the output. Motor speed is controlled by the PID low-level controller for the robot mobility; the speed controlling is done using the constant values Kd, Kp, and Ki which depend on the past, future, and present errors. For better control performance, the integral gain, differential gain, and proportional gain are adjusted by the PID controller. Robot speed may vary by changing the direction of the vehicle, so to avoid this the Social Spider Optimization (SSO) algorithm is used in PID controllers. PID controller parameter tuning is hard by using separate algorithms, so the parameters are tuned by the SSO algorithm which is a novel nature-inspired algorithm. The main goal of this paper is to demonstrate the effectiveness of the proposed approach in achieving precise speed control of the robot, particularly in the presence of disturbances and uncertainties.

An Ensemble Model for the Diagnosis of Brain Tumors through MRIs.

Automatic brain tumor detection in MR Images is one of the basic applications of machine vision in medical image processing, which, despite much research, still needs further development. Using multiple machine learning techniques as an ensemble system is one of the solutions that can be effective in achieving this goal. In this paper, a novel method for diagnosing brain tumors by combining data mining and machine learning techniques has been proposed. In the proposed method, each image is initially pre-processed to eliminate its background region and identify brain tissue. The Social Spider Optimization (SSO) algorithm is then utilized to segment the MRI Images. The MRI Images segmentation allows for a more precise identification of the tumor region in the image. In the next step, the distinctive features of the image are extracted using the SVD technique. In addition to removing redundant information, this strategy boosts the speed of the processing at the classification stage. Finally, a combination of the algorithms Naïve Bayes, Support vector machine and K-nearest neighbor is used to classify the extracted features and detect brain tumors. Each of the three algorithms performs feature classification individually, and the final output of the proposed model is created by integrating the three independent outputs and voting the results. The results indicate that the proposed method can diagnose brain tumors in the BRATS 2014 dataset with an average accuracy of 98.61%, sensitivity of 95.79% and specificity of 99.71%. Additionally, the proposed method could diagnose brain tumors in the BTD20 database with an average accuracy of 99.13%, sensitivity of 99% and specificity of 99.26%. These results show a significant improvement compared to previous efforts. The findings confirm that using the image segmentation technique, as well as the ensemble learning, is effective in improving the efficiency of the proposed method.

Investigating the applicability of the social spider optimization for the inversion of magnetic anomalies caused by dykes

Dipping dykes are geologically important structures since they are very important structures for hydrogeological, geothermal and hydrocarbon research. Many methods have been introduced by researchers to interpret dykes from magnetic anomaly data, but each of these methods have limitations. Therefore, new techniques are constantly being developed to achieve better results. This study introduces a novel method based upon the Social Spider Optimization algorithm and tests the method using synthetic examples. The algorithm developed to decipher the source body properties, is presented in detail. The test data consists of synthetic anomalies corrupted by different levels of random noise and field anomalies from mining records in China and Turkey. The obtained results have showed that Social Spider Optimization is a reliable, stable and efficient tool for deciphering the physical properties of deep and shallow located dykes from magnetic data. In addition, the proposed method is recommended for inversion of other geophysics data such as self-potential and gravity data.

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.

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.

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.

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.

Novel methodology for the geophysical interpretation of magnetic anomalies due to simple geometrical bodies using social spider optimization (SSO) algorithm

The inefficiencies and uncertainties surrounding solutions from existing inversion methods have necessitated investigation for more efficient techniques for the inversion of ill-posed magnetic problems. In this study, the Social Spider Optimization (SSO) algorithm has been modified, adopted and successfully used in modelling physical characteristics of magnetic anomalies originating from simple-shaped geologic structures. The study, aimed at testing the capacity and efficiency of the SSO algorithm to model magnetic data of varying complexity, was successfully conducted on both synthetic data with varying levels of noise and real field data obtained from mining fields in Senegal and Egypt. To assess the mathematical nature of the inverse problem considered, error energy maps were produced for each model parameter pairs in the synthetic examples. These maps enabled the pre-assessment of the resolvability model parameter for the ill-posed problem. In addition, uncertainty analysis aimed at providing insight to the reliability of the obtained solutions was carried out using the Metropolis–Hastings (M–H) sampling algorithm. Results show that the procedure converges fast and generates accurate results even when confronted with constrained multi-parameter non-linear inversion problems. Its outstanding converging speed and accuracy of the results reveal it as an excellent procedure for overcoming agelong problems of local optimal solutions associated with pre-existing algorithms. The consistency of the results with actual values affirms the efficacy of the new procedure which is pioneering in geophysical literature. It is therefore a stable and efficient tool for performing geophysical data inversion and is therefore recommended for use in inverting geophysical data with higher complexities like seismic reflection and gravity data, that require many corrections to be performed before reliable geological interpretations can be made.

Predictive Analysis for Cancer and Diabetes Using Simplex Method Based Social Spider Optimization Algorithm

Early stage of prediction and diagnosis is the only way to solve the challenges due to medical data. Machine learning tools and techniques help in prediction and diagnosis of different types of medical data. In this paper, Simplex Method based Social Spider Optimization (SMSSO) method is used which modifies the Social Spider Optimization (SSO) method. Different types of datasets were used to validate the SMSSO-NN technique. SMSSO-NN shows 99.36%, 94%, 95.78%, and 98% accuracy in Wisconsin Breast Cancer (WBC), Lung cancer, Diabetic, and Cervical cancer datasets, respectively that is better than other methods. The accuracy of the SMSSO-NN algorithm is associated with different classification techniques like Structured Singular Value (SSV), Local Linear Wavelet Neural Network-Firefly Algorithm (LLWNN-FA), and Rough Set Theory.

Optimized Multivariate Adaptive Regression Splines for Predicting Crude Oil Demand in Saudi Arabia

This paper presents optimized linear regression with multivariate adaptive regression splines (LR-MARS) for predicting crude oil demand in Saudi Arabia based on social spider optimization (SSO) algorithm. The SSO algorithm is applied to optimize LR-MARS performance by fine-tuning its hyperparameters. The proposed prediction model was trained and tested using historical oil data gathered from different sources. The results suggest that the demand for crude oil in Saudi Arabia will continue to increase during the forecast period (1980–2015). A number of predicting accuracy metrics including Mean Absolute Error (MAE), Median Absolute Error (MedAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and coefficient of determination ( R 2 ) were used to examine and verify the predicting performance for various models. Analysis of variance (ANOVA) was also applied to reveal the predicting result of the crude oil demand in Saudi Arabia and also to compare the actual test data and predict results between different predicting models. The experimental results show that optimized LR-MARS model performs better than other models in predicting the crude oil demand.

Objective reduction in many-objective optimization with social spider algorithm for cloud detection in satellite images

An important difficulty with multi-objective algorithms to analyze many-objective optimization problems (MaOPs) is the visualization of large dimensional Pareto front. This article has alleviated this issue by utilizing objective reduction approach in order to remove non-conflicting objectives from original objective set. The present work proposed formulation of objective reduction technique with multi-objective social spider optimization (MOSSO) algorithm to provide decision regarding conflict objectives and generate approximate Pareto front of non-dominated solutions. A comprehensive analysis of objective reduction approach is carried out with existing multi-objective methods on many-objective DTLZ and WFG test suite which highlight the superiority of proposed technique. Further, the performance of the proposed approach is evaluated on satellite images to detect cloudy region against various types of earth’s surfaces. The performance of the proposed approach is compared against existing benchmark many-objective algorithm, NSGA-III in order to evaluate the potential of proposed method in clustering application. It is observed that obtained clustering results using reduced objective set of MOSSO algorithm provides almost equivalent accuracy with results obtained using NSGA-III with many-objective set.

Proportional-Integral-Derivative Parametric Autotuning by Novel Stable Particle Swarm Optimization (NSPSO)

To improve the performance, robustness and stability of autotuning the proportional integral and derivative (PID) parameter, the novel stable particle swarm optimization (NSPSO) is proposed in this paper. The NSPSO is the combination of the particle swarm and optimization algorithm with the new stable rule to reconsider the survival of the remaining particle in the search space for handling the instability of the system. The new rule is proposed based on proving the stability according to the Lypunov stability theorem. Additionally, to show the method’s superiority in performance and robustness, the proposed method is compared with the results of simulations with the particle swarm optimization (PSO), the hybrid particle swarm optimization-grey wolf optimization (PSO-GWO), the whale optimization algorithm (WOA) and the social spider optimization algorithm (SSO) based on a direct current (DC) motor control system. In the comparative performance, the various fitness functions are applied, while the comparative robustness and the changed operation point of the DC motor are applied. After comparing the methods, the proposed method obtains better results than the PSO, PSO-GWO, WOA and SSO in both performance and robustness.