Social Spider Optimization Research Articles

Optimal hydropower station dispatch using quantum social spider optimization algorithm

SummaryIn this article, a new quantum social spider optimization (QSSO) algorithm is proposed. In the QSSO algorithm, we introduce an encoding approach based on bits described on social spider optimization (SSO) and serves as the evolution method of the population space. For the encoding of individuals, the probability amplitude expression of quantum bit is applied to describe the position of individuals, by which one individual's position can be expressed as the superposition of multistates. In such a way, the population diversity and the global searching capability of the SSO algorithm are enhanced. The QSSO algorithm was used to optimize the hydropower station dispatch, and the calculation results show that QSSO algorithm has fast convergence, small number of tuning parameters, high calculation accuracy, stability, simple, and is easy to be implemented with strong global search capability.

An Efficient Social Spider Optimization for Data Clustering using Data Vector Representation

In this article, we propose a new clustering algorithm namely an efficient social spider optimization for data clustering using data vector representation (ESSODCDI). It uses a data vector representation for each spider so that its memory requirements can be reduced. Unlike other nature-inspired algorithms, it requires lesser memory requirements. We find that its clustering results are by far better than those of other nature-inspired algorithms.

Design and Implementation of a Fuzzy Logic Controller for Inverted Pendulum System Based on Evolutionary Optimization Algorithms

The inverted pendulum is a standard classical problem in the branch of control and systems. If a cart is bushed by force then its position and angle of the pendulum will be changed. Several controllers may employed, keeping the pendulum arm upright by controlling at the cart location. In this search paper, the fuzzy-like PID (FPID) controller has been used to control the inverted pendulum, and the parameters of the controller are tuned with several evolutionary optimization algorithms like a genetic algorithm (GA), ant colony optimization (ACO), and social spider optimization (SSO.) The result of tuned FPID with evolutionary optimization is compared with conventional PID, and it shows that FPID with SSO has been given the best result.

Optimal Fuzzy Logic Control for Temperature Control Based on Social Spider Optimization

Fuzzy Logic Controller (FLC) is useful for many applications like industrial, home, military and medical applications, so one of main application is temperature control of A proposed controller combines the advantages of controller structure and fuzzy inference for air heater temperature control applications. The input variables of the PDFLC-SSO are error and change of error, while its output is the control signal. PDFLC-SSO has seven membership functions. These functions are triangular shaped. Centroid method (CM) is selected for the defuzzification process. The universe of discourse range is used from (-1 to 1) for each input and output. Temperature heater model was utilized to test the controller; the gain factors was tuned using trial and error method and SSO algorithm to reach the optimum response with minimum settling time, minimum peak overshoot and minimum Integral Time Absolute Error (ITAE). Carried out simulations showed an average improvement in measurements using ITAE. ITAE is 7.46% for a constant set-value using PDFLC-SSO, while it is 9.55 for a constant set-value using PDFLC.

An improved volleyball premier league algorithm based on sine cosine algorithm for global optimization problem

Volleyball premier league (VPL) simulating some phenomena of volleyball game has been presented recently. This powerful algorithm uses such racing and interplays between teams within a season. Furthermore, the algorithm imitates the coaching procedure within a game. Therefore, some volleyball metaphors, including substitution, coaching, and learning, are used to find a better solution prepared by the VPL algorithm. However, the learning phase has the largest effect on the performance of the VPL algorithm, in which this phase can lead to making the VPL stuck in optimal local solution. Therefore, this paper proposed a modified VPL using sine cosine algorithm (SCA). In which the SCA operators have been applied in the learning phase to obtain a more accurate solution. So, we have used SCA operators in VPL to grasp their advantages resulting in a more efficient approach for finding the optimal solution of the optimization problem and avoid the limitations of the traditional VPL algorithm. The propounded VPLSCA algorithm is tested on the 25 functions. The results captured by the VPLSCA have been compared with other metaheuristic algorithms such as cuckoo search, social-spider optimization algorithm, ant lion optimizer, grey wolf optimizer, salp swarm algorithm, whale optimization algorithm, moth flame optimization, artificial bee colony, SCA, and VPL. Furthermore, the three typical optimization problems in the field of designing engineering have been solved using the VPLSCA. According to the obtained results, the proposed algorithm shows very reasonable and promising results compared to others.

Enhanced Social Spider Optimization Algorithm for Increasing Performance of Multiple Pursuer Drones in Neutralizing Attacks From Multiple Evader Drones

We propose an optimization algorithm for reducing execution time needed by multiple pursuers in solving a variant of the Multiple-Pursuer Multiple-Evader (MPME) problem where each evader tries to attack an area defended by pursuers. This problem is a variant of the Multi-Agent Pursuit Evasion problem. In our discussed problem, a group of pursuers tries to defend an area from a group of evaders’ attacks. The main task given in this problem is how pursuers can capture or immobilize as soon as possible any evader trying to get closer to the defended area (evaders’ target). We use Social Spider Optimization (SSO) algorithm as the basis of our proposed method. In SSO, there are female spiders, dominant-male spiders, and non-dominant-male spiders collaborating to catch their prey. In SSO, there are three main procedures usually exist: calculation of fitness value, the vibrational summons of surrounding spiders, and mating procedure. In this paper, we develop an enhanced SSO algorithm where excludes the mating procedure and propose a practical calculation process for solving our discussed problem. SSO is one of the recent optimization algorithms developed in the computer science field. Developing this algorithm for solving dynamic problem like the MPME variant surely brings a novelty in the computer science research area. We test our proposed method in a 3D simulation environment where we manifest all pursuers and evaders as drones. Based on our experiment result, our algorithm performs better than commonly used methods for solving the MPME problem.

Magnetic Train System by Interval Type-2 Fuzzy Logic Control Based on Social Spider Optimization

Magnetic Train System by Interval Type-2 Fuzzy Logic Control Based on Social Spider Optimization

A vibrant crossbreed social spider optimization with genetic algorithm tactic for flexible job shop scheduling problem

Job shop scheduling is one of the major issues in which the scheduling process is associated with the real-time manufacturing industry. A flexible job shop scheduling problem is one of the most important issues among the hardest combinatorial advancement issues. Flexible job shop scheduling is extremely a nondeterministic polynomial combinatorial problem. In this paper, it is proposed that a mixture of improvement demonstrates to make makespan minimization in the flexible job shop scheduling problem issue. This paper includes the hybridization of social spider optimization and genetic algorithm that is effectively controlled by the calculation via optimization techniques. Most of the part in this method is given as the scavenging methodology of social insects, which use the vibrations spread over the bug-catching network to decide the position of the target. These hybridization approaches after arachnid upgrading process hereditary calculation chromosomes are chosen to produce new arrangements nearer to the minimum makespan time. The main objective of this paper is to minimize the makespan time of “ n” jobs and “ m” machines. The proposed algorithms have effectively investigated many benchmark problems and the computational results were compared with existing metaheuristic, including progressive calculations and algorithms for the swarm intelligence in the flexible job shop scheduling problem.

A Novel Fault Tolerance Energy-Aware Clustering Method via Social Spider Optimization (SSO) and Fuzzy Logic and Mobile Sink in Wireless Sensor Networks (WSNs)

A Novel Fault Tolerance Energy-Aware Clustering Method via Social Spider Optimization (SSO) and Fuzzy Logic and Mobile Sink in Wireless Sensor Networks (WSNs)

Memetic Algorithm With Meta-Lamarckian Learning and Simplex Search for Distributed Flexible Assembly Permutation Flowshop Scheduling Problem

This paper studies a novel and practical distributed flexible assembly permutation flowshop scheduling problem with makespan criterion, which has attracted wide attention due to important applications in modern manufacturing. The problem integrates two machine environments of distributed production and flexible assembly, which can process and assemble the jobs into customized products. We first present a mixed integer linear programming model to characterize the problem essence and to solve small-size problems. Due to the NP-hard, we further propose an efficient memetic algorithm, which consists of a global exploration optimizer designed based on improved social spider optimization and two local exploitation optimizers designed based on meta-Lamarckian learning and simplex search, respectively. To implement the algorithm, a problem-specific encoding scheme is presented. Algorithmic parameters are calibrated by a design of experiments, and a comprehensive computational campaign is conducted to evaluate the performance of the mathematical model and algorithms. Statistical results show that their problem-solving abilities are effective, and especially the proposed memetic algorithm outperforms the existing algorithms significantly.

Metaheuristic optimization algorithms to estimate statistical distribution parameters for characterizing wind speeds

An accurate analysis of wind speeds is vital to justify wind energy projects. Statistical distributions can be used to characterize wind speeds through considering uncertainty in wind resources. However, the selection of the most suitable probability density function (PDF) is still a challenging task. Therefore, this study aims at developing a framework to accurately evaluate the performance of different PDFs to fit wind speeds, as well as presenting a new metaheuristic optimization algorithm method, called Social Spider Optimization (SSO), for wind characterization purposes. Seven sites in Saudi Arabia are used as case studies. Results indicate that combined PDFs outperform single PDFs in representing the observed wind speeds frequencies at all considered sites. Weibull distribution appears to be the most prevalent single distribution while no combined PDF dominates the others. In addition, the proposed SSO method is found to be the most efficient method for estimating PDFs parameters in Saudi Arabia. Overall, this proposed framework can be used to evaluate different wind PDFs in other countries.

Optimum Design of Nano-Scaled Beam Using the Social Spider Optimization (SSO) Algorithm

In this research study, the optimum cross-sectional dimensions of nano-scale beam elements are investigated under different load conditions. Euler-Bernoulli beam model based on nonlocal elasticity theory is utilized for the analysis of the beam. Two types of nano-scaled beams are modeled; carbon nanotubes (CNTs) and Boron nitride nanotubes (BNNTs). The novel meta-heuristic based optimization algorithm called Social Spider Optimization (SSO) algorithm is employed to find the beam designs with the objective of minimizing the cross-sectional area. Furthermore, the obtained optimum cross-sectional dimensions, critical stress and displacement values of the beams are compared according to the material type, beam length, and load conditions.

Medical image DENOISING scheme using discrete wavelet transform and optimization with different noises

SummaryIn Medical Image Processing (MIP), the images are influenced by various types of noises. Hence, reducing such noises becomes a crucial issue. The main objective is to denoise the noisy images effectively with reduced computational cost. This paper proposes a denoising technique using Discrete Wavelet Transform (DWT) and Social Spider Optimization (SSO) algorithm. Initially, DWT is applied over various noises over the input medical images. Then, the wavelet coefficients are optimized by applying the SSO algorithm. Finally, the inverse of DWT (IDWT) is applied over the optimized coefficients. The denoised image is obtained, and then, the PSNR evaluation is utilized for finding the superior performance. Experimental results using MATLAB show that the proposed algorithm has better performance when compared with the existing approaches.

Hybridization of a Social Spider Optimization Algorithm with Differential Evolution for Text Document Clustering Using Single Cluster Approach

Hybridization of a Social Spider Optimization Algorithm with Differential Evolution for Text Document Clustering Using Single Cluster Approach

Hybrid social spider optimization algorithm with differential mutation operator for the job-shop scheduling problem

<p style='text-indent:20px;'>The job-shop scheduling problem is one of the well-known hardest combinatorial optimization problems. The problem has captured the interest of a significant number of researchers, but no efficient solution algorithm has been found yet for solving it to optimality in polynomial time. In this paper, a hybrid social-spider optimization algorithm with differential mutation operator is presented to solve the job-shop scheduling problem. To improve the exploration capabilities of the social spider optimization algorithm (SSO), we incorporate the DM operator (a mutation operator taken from the deferential evolutionary (DE) algorithm) into the framework of the female cooperative operator. The experimental results show that the proposed method effectiveness in solving job-shop scheduling compared to other optimization algorithms in the literature.

Social Spider Optimization with Tumbling Effect Based Data Classification Model for Stock Price Prediction

In last decade, data classification become more famous which aims to classify the data to a fixed number of classes. The data classification problem is treated as an NP hard problem and different optimization models are presented to resolve it. This paper introduces a social spider optimization (SSO) algorithm with tumbling effect called SSO-T algorithm to solve the data classification problem. First, the SSO algorithm is derived to solve the classification process which is considered as a NP hard problem. Next, to further enhance the exploration process of SSO algorithm, it is modified by the inclusion of tumbling effect, called SSO-T algorithm. For validating the results of the SSO and SSO-T algorithm, a real time problem of stock price prediction (SSP) is employed. For experimentation, the results are validated by testing the SSO and SSO-T algorithms against four datasets such as Dow Jones Index (DJI) dataset, three own datasets gathered from Yahoo finance on the basis of daily, weekly and yearly. The empirical result states that the proposed algorithms perform well and it is noted that the classification performance of the SSO algorithm is increased by the inclusion of tumbling effect.

Social Spider Optimization Algorithm for Effective Data Classification: An Application of Stock Price Prediction

Presently, data classification has become a hot research topic, intends to categorize the data into a predefined number of classes. The data classification problem has been considered as a NP hard problem and various optimization algorithms are introduced to solve it. Social spider optimization (SSO) algorithm is originally developed to resolve continuous and optimization problems. In line with, it has been altered to manage different kinds of optimization process and also to be employed for data investigation. And, some other studies are also investigated the use of SSO algorithm in different domains. In this paper, we introduce new SSO algorithm particularly applicable for data classification process. In order to validate the performance of the SSO algorithm, a real time problem of stock price prediction (SPP) is employed. For experimentation, the results are validated by testing the SSO algorithm against four datasets such as Dow Jones Index (DJI) dataset, three own datasets gathered from Yahoo finance on the basis of daily, weekly and yearly. The empirical result states that the proposed algorithms perform well and it is noted that the classification performance of the SSO algorithm better than the compared methods.

Optimal Operation of Energy Storage Units in Distributed System Using Social Spider Optimization Algorithm

Optimal Operation of Energy Storage Units in Distributed System Using Social Spider Optimization Algorithm

Social Spider Optimization Algorithm: Theory and its Applications

An extensive variety of optimization problems are solved by swarm intelligence algorithms that are modelled based on the animal or insect behaviour while living in groups. One such recent swarm intelligence algorithm is Social Spider Optimization (SSO). This paper thoroughly reviews and analyses the characteristics of this meta-heuristic algorithm. Since the existing literature of this algorithm is comparatively limited, the paper discusses the research ideas presented in such existing works and classifies the literature on basis of the application areas like image processing, optical flow, electric circuits, neural networks and basic sciences. It also sets a basis for research applications of the algorithm in order to tap the complete potential of the algorithm in other areas to achieve desired results.

An Intelligent Data-Driven Model to Secure Intravehicle Communications Based on Machine Learning

The high relying of electric vehicles on either in-vehicle or between-vehicle communications can cause big issues in the system. This paper is going to mainly address the cyberattack in electric vehicles and propose a secured and reliable intelligent framework to avoid hackers from penetration into the vehicles. The proposed model is constructed based on an improved support vector machine model for anomaly detection based on the controller area network bus protocol. In order to improve the capabilities of the model for fast malicious attack detection and avoidance, a new optimization algorithm based on social spider optimization algorithm is developed, which will reinforce the training process offline. Also, a two-stage modification method is proposed to increase the search ability of the algorithm and avoid premature convergence. Last but not least, the simulation results on the real datasets reveal the high performance, reliability, and security of the proposed model against denial-of-service hacking in the electric vehicles.