Spider Algorithm Research Articles

A parallel social spider algorithm based on population mining

The social spider algorithm is a meta-heuristic algorithm inspired by the foraging behavior of biological social spiders, and is initially used to solve optimization problems with continuous functions. There exist many challenges when dealing with discrete combinatorial optimization problems. Therefore, a novel parallel social spider algorithm based on population mining is proposed. Its starting point is to mine dynamic heuristic information from the population of social spiders that cause vibrations to guide the spider’s next walk. It proposes the concept of vibration candidate sets and enhances the behavior of artificial spiders. Furthermore, it is implemented in parallel on the graphics processing unit. According to our investigation, it is the first parallel social spider algorithm. The classical traveling salesman problem is solved as an example. By solving 30 small-scale and medium-scale problems and comparing with other algorithms, it shows that its solution quality is significantly better. By solving 17 large-scale problems, compared with the parallel ant colony system and parallel iterative hill climbing algorithm on the GPU, it is found that both the solution quality and performance are also significantly better. Codes are available at https://github.com/BuptCIAGroup/P-SSA.

A Pareto-optimality based black widow spider algorithm for energy efficient flexible job shop scheduling problem considering new job insertion

The utilization of flexible job shops is on the rise, driven by the decreasing life cycle of consumer products. In dynamic flexible job shops, the occurrence of disruptive events like machine breakdown, tool wear, and new job arrivals is common, emphasizing the critical need for efficient scheduling to achieve productivity and cost-effectiveness. Additionally with the growing concerns about energy consumption and global warming, the imperative to contemplate workshops with lower energy usage is becoming increasingly significant. Therefore, in this study a multi-objective dynamic flexible job shop scheduling problem (MODFJSP) with insertion of new jobs for optimization objectives: makespan, total energy consumption and schedule instability is addressed. A novel multi-objective black widow spider algorithm (MOBWSA), inspired by the mating behavior of black widow spiders, is proposed. The MOBWSA ensures an optimal balance between minimizing makespan and energy consumption through its superior evolutionary processes of procreation, mutation and cannibalism. Furthermore, a strategic on/off strategy is implemented to enhance energy efficiency. The disruptions in schedule at rescheduling stage are addressed through the proposed sigmoidal weighted instability function. To handle the multi-objective nature of the problem this work incorporates Pareto-optimality approach in combination with novel hybrid crowding distance metric. The optimization is further aided by exploitation of search space through an ensemble of local search operators. Testing and evaluation of the MOBWSA with 30 benchmark problems is conducted, and its performance for multiple metrics is compared with recently published state-of-the-art algorithms. The results establish the effectiveness and efficiency of the proposed algorithm.

Comparison analysis of the prediction accuracy of GWO, GM and SSA model in the wind power

Wind power has been occupying the substantial proportion of energy structure since it occurs to people that dependence on fossil fuels with the aims of worldwide energy supply would provoke an avalanche of challenges concerning on environmental degradation like the increase of greenhouse gas emissions. Nevertheless, most of wind power resource cannot be utilized completely as wind power generation have high volatility, which also possess partly repercussion with aims of contributing to the operation of the power system. As the technology has burgeoned, numerous scientists have made great strides in the wind power prediction, which can facilitate the consistent functioning of the electrical grid. However, there are massive difference in the wind power predication effect due to the respective character of algorithm. This essay will introduce the category of multiple relative models and compares the consequence of the improved algorithm based in Grey Wolf Optimizer (GWO), Grey Model (GM) and Social Spider Algorithm (SSA) model by analyzing the relevant table Comparison results that GM model is capable of significantly enhancing the predictive performance to achieve heightened authenticity and decreased presage error in contrast to the previous algorithm.

Aerodynamic optimization method of intake grille of active clearance control system for turbines based on modified social spider algorithm (MSSA)

During the operation of an active clearance control (ACC) system of a turbine, the aerodynamic performance of the intake grille indirectly influences its control. To improve the performance, an aerodynamic optimization method is proposed, consisting of parameterization, an optimization algorithm, and a fitness evaluation. During parameterization, its geometry is represented by seven geometric variables. A modified social spider algorithm is used as the optimization algorithm. To evaluate the aerodynamic performance of the grille, a special fitness function is adopted, obtained using an adaptive topological multi-layer feedforward artificial neural network. To verify the feasibility of this method, experiments and numerical calculations are carried out on the original and optimized intake grilles. The results show that the average intake flow rate and average total pressure recovery coefficient of the optimized grille have increased by 17.3% and 4.9%, respectively.

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.

Satin bowerbird optimizer-neural network for approximating the capacity of CFST columns under compression

Concrete-filled steel tube columns (CFSTCs) are important elements in the construction sector and predictive analysis of their behavior is essential. Recent works have revealed the potential of metaheuristic-assisted approximators for this purpose. The main idea of this paper, therefore, is to introduce a novel integrative model for appraising the axial compression capacity (Pu) of CFSTCs. The proposed model represents an artificial neural network (ANN) supervised by satin bowerbird optimizer (SBO). In other words, this metaheuristic algorithm trains the ANN optimally to find the best contribution of input parameters to the Pu. In this sense, column length and the compressive strength of concrete, as well as the characteristics of the steel tube (i.e., diameter, thickness, yield stress, and ultimate stress), are considered input data. The prediction results are compared to five ANNs supervised by backtracking search algorithm (BSA), earthworm optimization algorithm (EWA), social spider algorithm (SOSA), salp swarm algorithm (SSA), and wind-driven optimization. Evaluating various accuracy indicators showed that the proposed model surpassed all of them in both learning and reproducing the Pu pattern. The obtained values of mean absolute percentage error of the SBO-ANN was 2.3082% versus 4.3821%, 17.4724%, 15.7898%, 4.2317%, and 3.6884% for the BSA-ANN, EWA-ANN, SOSA-ANN, SSA-ANN and WDA-ANN, respectively. The higher accuracy of the SBO-ANN against several hybrid models from earlier literature was also deduced. Moreover, the outcomes of principal component analysis on the dataset showed that the yield stress, diameter, and ultimate stress of the steel tube are the three most important factors in Pu prediction. A predictive formula is finally derived from the optimized SBO-ANN by extracting and organizing the weights and biases of the ANN. Owing to the accurate estimation shown by this model, the derived formula can reliably predict the Pu of concrete-filled steel tube columns.

Portia spider algorithm: an evolutionary computation approach for engineering application

The Portia spider, a notable member of the jumping spider family (Salticidae), is widely recognized for its intricate hunting strategies and remarkable problem-solving prowess. Several species fall under the “Portia” genus, with habitats spanning regions in Africa, Asia, and Australia. Demonstrating the ability to tackle new challenges, these spiders can learn and adapt their strategies based on prior experiences. This study introduces the Portia Spider Algorithm (PSA), a swarm-based technique inspired by the unique predatory strategies of the Portia spider. We conducted rigorous assessments of PSA performance against 23 classical test functions, 29 CEC2017 test cases, and 5 engineering optimization tasks. To demonstrate the effectiveness of the PSA, outcomes were juxtaposed with those of renowned algorithms. This paper explores the mechanics, advantages, and potential applications of PSA within the vast domain of computational optimization.Graphical

An innovative machine learning based on feed-forward artificial neural network and equilibrium optimization for predicting solar irradiance

As is known, having a reliable analysis of energy sources is an important task toward sustainable development. Solar energy is one of the most advantageous types of renewable energy. Compared to fossil fuels, it is cleaner, freely available, and can be directly exploited for electricity. Therefore, this study is concerned with suggesting novel hybrid models for improving the forecast of Solar Irradiance (IS). First, a predictive model, namely Feed-Forward Artificial Neural Network (FFANN) forms the non-linear contribution between the IS and dominant meteorological and temporal parameters (including humidity, temperature, pressure, cloud coverage, speed and direction of wind, month, day, and hour). Then, this framework is optimized using several metaheuristic algorithms to create hybrid models for predicting the IS. According to the accuracy assessments, metaheuristic algorithms attained satisfying training for the FFANN by using 80% of the data. Moreover, applying the trained models to the remaining 20% proved their high proficiency in forecasting the IS in unseen environmental circumstances. A comparison among the optimizers revealed that Equilibrium Optimization (EO) could achieve a higher accuracy than Wind-Driven Optimization (WDO), Optics Inspired Optimization (OIO), and Social Spider Algorithm (SOSA). In another phase of this study, Principal Component Analysis (PCA) is applied to identify the most contributive meteorological and temporal factors. The PCA results can be used to optimize the problem dimension, as well as to suggest effective real-world measures for improving solar energy production. Lastly, the EO-based solution is yielded in the form of an explicit formula for a more convenient estimation of the IS.

A Novel Crossover based Discrete Artificial Algae Algorithm for Solving Traveling Salesman Problem

The Artificial Algae Algorithm (AAA) is a newly proposed metaheuristic algorithm that is inspired by microalgae behaviors. This algorithm has been proposed for solving continuous optimization problems and achieved good results for the continuous problems. In addition, binary versions of AAA are proposed in the literature. This paper presents a discrete version of AAA, which is named Discrete Artificial Algae Algorithm (DAAA). For discretization of AAA, Crossover operators (one-point and uniform) are used in the processes (helical movement, evolutionary process, and adaptation). In this study, in addition to crossover operators, transformation operators such as swapping, insertion, symmetry, and reversion are also used. DAAA’s performance was analyzed on a well-known discrete optimization problem called the Traveling Salesman Problem (TSP). DAAA was tested on thirty-two Benchmark instances of the TSP. These instances were small-sized, medium-sized, and large-sized. Firstly, the AAA processes (evolutionary process, adaptation, and helical movement) with the combination of nearest neighbor and transformation operators were tested for selected benchmark instances and this testing was called Process Analysis. After this process Analysis the best processes with which to continue were selected, and after this decision comparisons with other algorithms were started. The main comparison is between discrete Social Spider Algorithm (DSSA) and DAAA, and DAAA outperformed DSSA on most of the problems. Further, DAAA’s performance on some of the benchmark instances was compared with some of the well-known algorithms for TSP. In this comparison, DAAA has achieved better results than many other algorithms. Experimental results show that DAAA has the capability of solving discrete optimization problems and outperforming other algorithms.

Deadlock control and hybrid social spider scheduling algorithm for two-stage assembly permutation flowshop with limited buffers

This paper focuses on the deadlock control and scheduling problem of two-stage assembly permutation flowshop with limited buffers. In such a system, all parts are first processed in a permutation flowshop, then assembled into final products. The objective is to find a feasible schedule, including a manufacturing subschedule and an assembly subschedule, to minimize makespan. Owing to limited buffers, not only may the blocking phenomenon appear in the system operation, but also some manufacturing subschedules may lead to deadlock states, making them unavailable. To obtain a feasible schedule, this work first proposes a deadlock control or an availability detection and amendment (ADA) algorithm to determine whether a manufacturing subschedule is available, and convert unavailable ones to available ones. Then by embedding ADA into a social spider algorithm (SSA), a hybrid social spider algorithm (HSSA) is developed for computing feasible schedules. In HSSA, an improved heuristic search algorithm is proposed to generate a better initial population, and a simplified variable neighborhood search algorithm is designed and incorporated into HSSA to enhance its searching ability. Finally, HSSA is tested on instances, showing its superiority over SSA in performance. The effect of buffer size on the scheduling objective is discussed.

Nature-inspired solutions for energy sustainability using novel optimization methods.

This research centres on developing a Home Electricity Management (HEM) system, a pivotal component within the modern supply chain for home electrical power. The system optimizes the scheduling of intelligent home gadgets through advanced meta-heuristics, specifically the Social Spider Algorithm (SSA) and Strawberry Algorithm (SWA), to efficiently manage home energy consumption. Within the supply chain context, HEM acts as a crucial link in the distribution and utilization of electricity within households, akin to optimizing resource allocation and demand balancing within a supply chain for efficient operation and cost-effectiveness. Simulations and comparisons demonstrate that SWA excels in cost savings, while SSA is more effective in reducing peak-to-average power ratios. The proposed solution reduces costs for residences by up to 3.5 percent, highlighting the potential for significant cost savings and efficiency improvements within the home electricity supply chain. It also surpasses existing cost and Peak Average (PAR) ratio meta-heuristics, indicating superior performance within the overall energy supply and consumption framework. Moreover, implementing the HEM system contributes to reducing carbon emissions, aligning with sustainability goals in the energy supply chain. It promotes energy efficiency, integrates renewable sources, and facilitates demand response, mirroring the emphasis on sustainability in supply chain practices. Overall, this research offers a practical and sustainable approach to home energy management, bringing substantial cost savings and environmental benefits to the modern supply chain for residential electricity.

Proposing a Algorithm for Optimizing Energy Consumption

The purpose of this paper is to use the social spider algorithm to improve energy consumption in wireless sensor networks. In this regard, finding a suitable place for routers was done with a social spider algorithm in the system by selecting and optimally placing elements in the network and revising the Objective function. To use it, the initial number was 12, and the maximum number of loop iterations in the algorithm was 100 replications. We achieved these values with several runs and errors. Besides, to achieve the best results, to solve the problem, they can be considered as the default algorithm. In this research, the MATLAB implementation language was used to implement the spider algorithm, and then NS 2.35 tool was used to simulate the network environment and use the algorithm. In this study, three standard gate placement algorithms were used for comparisons, including BRP, RDP, and RGP. The results showed that the proposed method could have the lowest energy consumption. It can cause fair and uniform energy consumption between all nodes. It will also increase the lifetime of the network. Optimization of routing algorithms for such networks by using the spider algorithm also reduced the number of errors (failures) in the system and increased the life of the network with the help of the mentioned method. The proposed method, compared to the techniques presented before this study, is acceptable and, while creating the desired capabilities in terms of security, does not impose much time overhead on the system

Stabilization of wheeled mobile robot by social spider algorithm based PID controller

Stabilization of wheeled mobile robot by social spider algorithm based PID controller

Optimal trajectory planning algorithm for autonomous flight of multiple UAVs in small areas

The development of science and technology requires UAV to improve the accuracy of path planning to better apply in the military field and serve the people. The research proposes to use the social spider algorithm to optimize the ant colony algorithm, and jointly build an IACA to deal with the optimal selection problem of UAV path planning. Firstly, the swarm spider algorithm is used to make a reasonable division and planning of the UAV’s flight field. Secondly, the AC is used to adjust and control the UAV’s state and path. Then, the IACA is formed to carry out performance simulation and comparison experiments on the optimal path planning of the UAV to verify the superiority of the research algorithm. The results show that the maximum number of iterations of the original AC and the IACA is 100, but the IACA under the route planning optimization reaches the convergence state in 32 generations; Moreover, when the number of iterations is about 20 generations, there will be a stable fitness value, which saves time for the experiment to find the optimal path. In the simulation experiment, it is assumed that three UAVs will form a formation to conduct the experiment, and the multiple UAVs will be subject to global track planning and repeated rolling time domain track planning. The autonomous operation time of multiple UAVs through the assembly point is (5.30 s, 5.79 s, 9.29 s). The distance between UAVs during flight is predicted. It is found that the nearest distance is 2.3309 m near t= 6.65 s, which is in line with the safety distance standard. Under the improved algorithm, the speed in all directions is also relatively gentle. All the above results show that the improved algorithm can effectively improve the iteration speed and save time.

A local binary social spider algorithm for feature selection in credit scoring model

Borrowers’ default is the main risk of online lending. Using credit scoring model to assess borrowers’ credit is an important means to reduce default risk. The existing related work focus on the improvement of assessment methods, and do not study the quality of credit data enough. Actually, there are usually some noisy, redundant or irrelevant features in online credit data, which increase the computational complexity and reduce the evaluation accuracy of the model. Well-performing feature selection method is the key premise to improve accuracy of the credit evaluation model. At present, the feature selection methods applied to online credit scoring generally have shortcomings such as subjectivity, time consuming, low accuracy, etc. So it is urgent to introduce new schemes. Among the feature selection schemes in many fields, heuristic algorithm is widely recognized because of its advantages of high efficiency and accuracy, and especially BinSSA is an outstanding representative. However, BinSSA has the defects that it is easy to form extreme distribution in initialization, and is inefficient because there is a high probability of falling into local optimum during iteration. So it is still not the best choice for feature selection in online lending credit scoring model. In this paper, we propose a local binary social spider algorithm (LBSA), which introduces two local optimization strategies into BinSSA: opposition-based learning (OBL) and improved local search algorithm (ILSA). These strategies can help to improve the above defects. We conduct comparative experiments based on three typical online credit data sets and different algorithms, to verify the superior performance of LBSA. The experimental results validate that LBSA greatly reduces the redundancy of returned feature subsets, improves the iterative stability, and makes the credit scoring model more accurate and effective.

Hybrid Optimization-Based Neural Network Classifier for Software Defect Prediction

The software is applied in various areas so the quality of the software is very important. The software defect prediction (SDP) is used to solve the software issues and enhance the quality. The robustness and reliability are the major concerns in the existing SDP approaches. Hence, in this paper, the hybrid optimization-based neural network (Optimized NN) is developed for the effective detection of the defects in the software. The two main steps involved in the Optimized NN-based SDP are feature selection and SDP utilizing Optimized NN. The data is fed forwarded to the feature selection module, where relief algorithm selects the significant features relating to the defect and no-defects. The features are fed to the SDP module, and the optimal tuning of NN classifier is obtained by the hybrid optimization developed by the integration of the social spider algorithm (SSA) and gray wolf optimizer (GWO). The comparative analysis of the developed prediction model reveals the effectiveness of the proposed method that attained the maximum accuracy of 93.64%, maximum sensitivity of 95.14%, maximum specificity of 99%, maximum [Formula: see text]-score of 93.53%, and maximum precision of 99% by considering the [Formula: see text]-fold.

Stochastic variable metric proximal gradient with variance reduction for non-convex composite optimization

This paper introduces a novel algorithm, the Perturbed Proximal Preconditioned SPIDER algorithm (3P-SPIDER), designed to solve finite sum non-convex composite optimization. It is a stochastic Variable Metric Forward–Backward algorithm, which allows approximate preconditioned forward operator and uses a variable metric proximity operator as the backward operator; it also proposes a mini-batch strategy with variance reduction to address the finite sum setting. We show that 3P-SPIDER extends some Stochastic preconditioned Gradient Descent-based algorithms and some Incremental Expectation Maximization algorithms to composite optimization and to the case the forward operator can not be computed in closed form. We also provide an explicit control of convergence in expectation of 3P-SPIDER, and study its complexity in order to satisfy the approximate epsilon-stationary condition. Our results are the first to combine the non-convex composite optimization setting, a variance reduction technique to tackle the finite sum setting by using a minibatch strategy and, to allow deterministic or random approximations of the preconditioned forward operator. Finally, through an application to inference in a logistic regression model with random effects, we numerically compare 3P-SPIDER to other stochastic forward–backward algorithms and discuss the role of some design parameters of 3P-SPIDER.

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.

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.