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

Applied Nanoscience (Switzerland) 2023年第2期13卷 1389-1399页

作者： Mohamadwasel, Noorulden Basil Kurnaz, Sefer Institute of Graduate Studies Altinbas University Istanbul Turkey Engineering and Natural Sciences Altinbas University Istanbul Turkey

In this paper fractional order proportional integral derivative (FOPID) controller has been introduced with the fuzzy logic control (fuzzy type-2) which has been utilized with social spider optimization (SSO) algorithm to enhance the performance of the three links parallel robot. To achievement the best performance for the model robot planar, to compare with the fractional order proportional integral derivative controller it can be expressed as a general issue to solve the performance of the three-links parallel robot, and to create a trajectory circle that has been optimized. The fuzzy technique with fractional order proportional integral derivative is used to solve the quality working of robot planar model on torques to reduce the error of trajectory for end-effector, the error in x and y-axis positions, mean square error for theta 1, 2, 3, fuzzy type-2 with optimized SSO algorithm uses fractional order proportional integral derivative that has been reduced in thetas to 0% and more calculations for results which provide the suitable performance for the dynamic model that is based on the equation of Euler–Lagrange executed using MATLAB-Simulink. © 2021, King Abdulaziz City for Science and Technology.

关键词： FOPID controller Fuzzy type-2 Parallel 6-DOF robot Parallel-manipulator social spider optimization algorithm

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Toward text psychology analysis using social spider optimization algorithm

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第17期33卷 e6325-e6325页

作者： Hosseinalipour, Ali Gharehchopogh, Farhad Soleimanian Masdari, Mohammad Khademi, Ali Islamic Azad Univ Urmia Branch Dept Comp Engn Orumiyeh Iran Islamic Azad Univ Urmia Branch Dept Psychol Sci Orumiyeh Iran

Different nature-inspired meta-heuristic algorithms have been proposed to solve optimization problems. One of these algorithms is called social spider optimization (SSO) algorithm. spiders' natural behaviors have inspired them to find the bait position by detecting vibrations in their web. Although the SSO algorithm has good accuracy in achieving optimal solutions, it suffers from a low convergence rate. In this paper, we attempted to improve SSO by changing its motion and mating parameters. To provide a practical example of using the new proposed algorithm, we based it on multi-objective opposition-based SSO, named MOPSSO. We used this algorithm in a feature selection process for analyzing text psychology, which is a multi-objective problem. Textual psychology analysis is used in various fields, including collecting and analyzing people's views on various products, topics, social and political events. After selecting features, in order to classify the text, we used a new hybrid method that hybrids fuzzy C-MEANS data clustering technique, a decision tree (DT), and Naive Bayes (NB). Experimental results show that the improved SSO algorithm performs better than SSO, social spider algorithm, and CMA-ES algorithms. Additionally, the performance of the proposed hybrid classification method is better than those of NB and DT.

关键词： feature selection meta-heuristic algorithm multi-objective algorithms social spider optimization algorithm text psychology analysis

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Sensor Deployment Scheme Based on social spider optimization algorithm for Wireless Sensor Networks

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NEURAL PROCESSING LETTERS 2018年第1期48卷 71-94页

作者： Zhou, Yongquan Zhao, Ruxin Luo, Qifang Wen, Chunming Guangxi Univ Nationalities Coll Informat Sci & Engn Nanning 530006 Guangxi Peoples R China Key Labs Guangxi High Sch Complex Syst & Computat Nanning 530006 Guangxi Peoples R China

Wireless sensor networks (WSNs) consist of large numbers of micro-sensor nodes that have both communication and computing capabilities. The size of the coverage area of each sensor node determines the overall performance of the WSN. In this paper, a novel sensor deployment scheme based on the social spider optimization (SSO) algorithm is proposed to enable increased coverage for WSNs. In the SSO algorithm, the individual simulated social spiders' habits and their labor and cooperation efforts are divided according to gender differences. Simulations experiment are used to demonstrate the effectiveness of the proposed method.

关键词： Wireless sensor network social spider optimization algorithm Sensor deployment scheme

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

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EXPERT SYSTEMS 2025年第1期42卷

作者： Liu, Weiguang Alqhatani, Abdulmajeed Asiri, Fatima Salwana, Ely JiuZhou Polytech Xuzhou Peoples R China Najran Univ Coll Comp Sci & Informat Syst Dept Informat Syst Najran Saudi Arabia King Khalid Univ Coll Comp Sci Dept Informat Syst Abha Saudi Arabia Univ Kebangsaan Malaysia Inst Visual Informat Bangi Malaysia Jiuzhou Polytech Xuzhou 221116 Peoples R China

Online 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%.

关键词： customer preference online shopping principal component analysis social spider optimization algorithm

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Traceability of surface water pollution based on the SSO+DE algorithm

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ALEXANDRIA ENGINEERING JOURNAL 2025年 114卷 112-122页

作者： Jia, Dongyan Zhao, Liqiang Song, Jinling Guo, Dongliang Liu, Xiaoqing Hebei Normal Univ Sci & Technol Hebei Innovat Ctr Smart Percept & Appl Technol Agr Sch Math & Informat Technol Qinhuangdao 066004 Peoples R China Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066004 Peoples R China Hebei Normal Univ Sci & Technol Sch Math & Informat Technol Qinhuangdao 066004 Peoples R China

River water pollution incidents seriously threaten the ecological environment and people's health. Effective determination of the pollution source information contributes to minimizing the scope of water pollution incidents. A surface water pollution traceability model based on the social spider optimization algorithm and differential evolution algorithm was proposed to more accurately trace the pollution sources of surface water pollution events. The diffusion law of pollutants in rivers could be described by the migration and diffusion model. Relevant parameters (e.g., water flow velocity and river diffusion coefficient) varied depending on the season and weather. First, parameters in the migration and diffusion model were calibrated using the social spider optimization algorithm to coincide with the real hydrological environment. Then, the traceability of pollution source information was completed by simulating mutation, crossing, and selection between individuals based on a differential evolution algorithm, which obtained the optimal pollution source location, discharge volume, and discharge time. Finally, the relevant experiment data of the water pollution events between the Jingshi Section and the Puyang Section of the Waterproof River in Ref. [32] were used for simulation experiments. Results were compared with the probabilistic method and coupled density method in Ref. [32]. The average absolute errors of the two methods were 201.0 and 36.43, respectively. The error of the traceability results obtained by the traceability model in the work significantly decreased, with an average absolute error of 1.557. The SSO and DE algorithms had good convergence speed and global search capabilities. Besides, the DE algorithm is a multi-objective optimization algorithm solving multi-dimensional spatial data. Therefore, the surface water pollution traceability model had a high traceability accuracy and certain application value. It reduced the impact of water pollution on

关键词： Water pollution traceability Migration and diffusion model Parameter calibration social spider optimization algorithm Differential evolution algorithm

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An energy-aware service placement strategy using hybrid meta-heuristic algorithm in iot environments

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2023年第5期26卷 2913-2919页

作者： Hu, Yuanchao Huang, Tao Yu, Yang An, Yunzhu Cheng, Meng Zhou, Wen Xian, Wentao Shandong Univ Technol Sch Elect & Elect Engn Zibo 255000 Peoples R China State Grid Jiangsu Elect Power Engn Consulting Co Nanjing 210000 Peoples R China Zibo Power Supply Co State Grid Shandong Elect Power Co Zibo 255000 Peoples R China Huanglongtan Hydropower Plant State Grid Hubei El Shiyan 442000 Peoples R China

Today, increasing communication of Internet of Things (IoT) devices, wearable sensors, healthcare applications and cloud providers provides power and energy consumption for high processing personal data and healthcare records. Cloud-edge data centers are processing enormous amount of electrical power resulting in high performance computing. One of the important challenges in this problem is Virtual Machine (VM) service placement that attempts to enhance energy management of VMs to cloud-edge service providers dynamically to support Service Level Agreement (SLA) metrics in IoT systems. Service placement is a very important issue because if VMs are not confident in the SLA of their critical data, IoT applications will not want to use existing resources safety. Providing a way to increase the Quality of Service (QoS) factors and energy efficiency of the service placement in the IoT environment is an important and critical issue because the use of IoT devices and wearable sensors increases energy consumption in human life in smart contracts. Therefore, this paper presents a hybrid Genetic algorithm and social spider optimization (GA-SSO) algorithm for an energy-aware service placement model in the IoT to manage data congestion and system safety. After conducting studies and comparisons, the accuracy and superiority of the proposed model were established. Experimental results show that the energy consumption with the proposed GA-SSO algorithm can be reduced 24% and we can achieve to the performance of QoS factors with fitness function 88% with compare to the other meta-heuristic algorithms.

关键词： IoT Service placement Genetic algorithm social spider optimization algorithm Intelligent systems

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Forecasting stock price using integrated artificial neural network and metaheuristic algorithms compared to time series models

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SOFT COMPUTING 2021年第13期25卷 8483-8513页

作者： Shahvaroughi Farahani, Milad Razavi Hajiagha, Seyed Hossein Khatam Univ Fac Management & Finance Dept Finance Tehran Iran Khatam Univ Fac Management & Finance Dept Management Tehran Iran

Today, stock market has important function and it can be a place as a measure of economic position. People can earn a lot of money and return by investing their money in the stock exchange market. But it is not easy because many factors should be considered. So, there are many ways to predict the movement of share price. The main goal of this article is to predict stock price indices using artificial neural network (ANN) and train it with some new metaheuristic algorithms such as social spider optimization (SSO) and bat algorithm (BA). We used some technical indicators as input variables. Then, we used genetic algorithms (GA) as a heuristic algorithm for feature selection and choosing the best and most related indicators. We used some loss functions such as mean absolute error (MAE) as error evaluation criteria. On the other hand, we used some time series models forecasting like ARMA and ARIMA for prediction of stock price. Finally, we compared the results with each other means ANN-Metaheuristic algorithms and time series models. The statistical population of research have five most important and international indices which were S&P500, DAX, FTSE100, Nasdaq and DJI.

关键词： Genetic algorithm social spider optimization algorithm Artificial neural network Bat algorithm

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Fault detection and classification in solar based distribution systems in the presence of deep learning and social spider method

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SOLAR ENERGY 2023年第1期262卷

作者： Cao, Hanhua Zhang, Huanping Gu, Changle Zhou, Yuhuai He, Xiu Guangzhou Xinhua Univ Sch Informat & Intelligent Engn Guangzhou 510520 Peoples R China

This research proposes an intelligent method for fault detection and classification (FDC) in solar based distribution systems using Generative Adversarial Networks (GANs) and social spider method. The suggested method is constructed using the combination of GANs and social spider method, which is a hybrid system to increase its capability for the classification purposes. The GANs model is used to detect fault signatures in the system, while the Improved social spider method is used to reinforce its training process. The proposed method is evaluated on the big data gathered using the digital twin of a solar based distribution system for different situations of operation. The results show that GANs can detect fault signatures with high accuracy and the Improved social spider method can classify the fault types with high accuracy. The proposed method compared with other existing methods and the results show that the suggested method outperforms the existing methods considering accuracy, recall, precision and speed. The proposed method can be used for FDC in solar based distribution systems, and can be developed to other distribution systems.

关键词： Fault classification and detection Generative adversarial networks (GANs) social spider optimization algorithm Digital twin Solar based distribution systems

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Intrusion Detection Based on Parallel Intelligent optimization Feature Extraction and Distributed Fuzzy Clustering in WSNs

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IEEE ACCESS 2018年 6卷 72201-72211页

作者： Liu, Zhouzhou Wei, Wei Wang, Hao Zhang, Yangmei Zhang, Qianyun Li, Shining Xian Aeronaut Univ Sch Elect Engn Xian 710077 Shaanxi Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian 710072 Shaanxi Peoples R China Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Shaanxi Peoples R China Norwegian Univ Sci & Technol N-710077 Alesund Norway

Aiming at large-scale, high dimensional data, and variable intrusion behavior in wireless sensor networks (WSNs), an intrusion detection algorithm based on parallel intelligent optimization feature extraction and distributed fuzzy clustering for WSNs is proposed. First, in order to effectively reduce the data dimensionality and improve the robustness of the feature extraction process, the parallel intelligent optimization feature extraction framework is constructed on the basis of defining the optimal feature evaluation index, for which the theoretical analysis shows that the index can eliminate feature redundancy and maintain the diversity of original data. Second, the spider cluster optimization algorithm evolution rule is redefined by introducing local search and adaptive multi strategy update method, and it proves that the improved social spider optimization (ISSO) algorithm has global convergence. The ISSO is used to solve the feature extraction framework, and through the parallel feature subset selection process, the best feature combination is extracted. Finally, WSNs intrusion detection is carried out by using the best feature subset and the distributed fuzzy clustering technology, and intelligent iterative evolution method and adaptive clustering strategy are introduced in order to improve the fuzzy clustering algorithm performance. Experimental results show that the intrusion detection algorithm can effectively give the results of intrusion detection, and moreover, compared with the other detection algorithms, the intrusion detection rate is improved by about 13.1%, and the false detection rate is decreased by about 8.5%.

关键词： Feature extraction fuzzy clustering intrusion detection social spider optimization algorithm wireless sensor networks

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