Social Spider Optimization Algorithm Research Articles

A Novel Social Spider Optimization Algorithm for Large-Scale Economic Load Dispatch Problem

The paper develops an improved social spider optimization algorithm (ISSO) for finding optimal solutions of economic load dispatch (ELD) problems. Different ELD problem study cases can bring huge challenges for testing the robustness and effectiveness of the proposed ISSO method since discontinuous objective functions as well as complicated constraints are taken into account. The improved method is different from original social spider optimization algorithm (SSSO) by performing several modifications directly related to three processes of new solution generation. Namely, the proposed method keeps one formula for the first and the second generations and modify them effectively while SSSO has two different formulas for each generation. In the third generation, the proposed method applies a new formula for determining the mating radius of dominant males and females with the intent to expand search space and avoid falling into local zones. The modifications can support the proposed ISSO method find better solutions with faster manner than SSSO while the number of control parameters and the number of computational processes can be reduced. As a result, the proposed method can find much less generation cost and achieve faster search speeds than SSSO for all considered systems. On the other hand, the search ability evaluation of the proposed method is also given by comparing results with other existing methods available in previous studies. The proposed method can obtain approximate or better results and faster convergence than nearly all compared methods excluding for the last system. Consequently, the proposed ISSO method can be recommended to be a strong method for ELD problem and it can be tried for other mathematical problems in engineering.

An opposition-based social spider optimization for feature selection

In machine learning and data mining, feature selection (FS) is one of the most important tasks required to select the most relevant instances from a dataset. In other words, FS is used to reduce the amount of information, creating a subset that represents the entire pool of data. The accuracy of the FS is reflected in a good classification of the information. This article presents an improved version of the social spider optimization (SSO) algorithm. The SSO tends to fail in local optima during the iterative process and is not possible to avoid this situation in the standard form. The proposed version avoids selecting the irrelevant features that demerit the performance of the FS. To achieve this goal, the opposition-based learning is used, in which there is a rule used to increase the exploration of the search space and the prominent zones in a determined neighborhood. The proposed algorithm is called opposition-based social spider optimization (OBSSO), and it has been tested over different mathematical problems. Moreover, the OBSSO, also, has been tested and compared with similar approaches using different datasets with specific information selected from UCI repository. The experimental results provide the evidence of the capabilities of the OBSSO for solving complex optimization problems.

Improved social spider optimization algorithm for optimal reactive power dispatch problem with different objectives

This paper proposes an improved social spider optimization (ISSO) for achieving different objectives of optimal reactive power dispatch (ORPD). The proposed ISSO method is developed by applying two modifications on new solution generation process. The proposed method uses only one modified equation for producing the first new solution generation and the second new solution generation while the standard SSO uses two equations for each process. The improvement in the proposed method is confirmed by solving benchmark optimization functions, IEEE 30-bus system and IEEE 118-bus system. Obtained results from ISSO are compared to those from other existing methods available in other studies together with other popular and state-of-the-art methods, which are implemented in the work. As compared to standard SSO for application to ORPD problem, ISSO can reduce the number of computation steps and one control parameter, and shorten simulation time. About the result comparisons with SSO and other remaining methods, ISSO can find more favorable solutions with higher quality and ISSO can stabilize solution search function with approximately all trial runs finding lower value of fitness. Furthermore, the strong search ability of ISSO is also indicated because it uses less value for control parameters. As a result, the proposed ISSO method can be a very effective optimization tool for dealing with the ORPD problem.

A high performance social spider optimization algorithm for optimal power flow solution with single objective optimization

The paper proposes a novel improved social spider optimization algorithm (NISSO) for solving optimal power flow (OPF) problem to independently optimize electricity generation fuel cost, power loss, polluted emission, voltage deviation and L index. The proposed NISSO method is first developed in the paper by performing three modifications with intent to improve optimal solution quality and speed up convergence of conventional social spider optimization (SSO). The first and the second modifications are to focus on new solution generation by changing the movement strategy of female spiders and male spiders while the third modification is to fix the female spider rate to an appropriate ratio. The performance of the proposed method is evaluated by testing on three IEEE systems with 30, 57 and 118 buses. As a result, the proposed method has advantages over SSO such as simpler application, fewer number of control parameters, spend less time tuning control parameter values, faster convergence to optimal solutions and more stable search ability. In addition, the proposed method's results are also compared to other existing methods and the indications are that the proposed method can find better optimal solutions, use lower number of generated solutions and faster convergence.

A Novel Method for Multispectral Image Classification by Using Social Spider Optimization Algorithm Integrated to Fuzzy C-Mean Clustering

In remote sensing, Fuzzy C-Means clustering (FCM) is a robust method in determining membership grades of a pixel belonging to 1 or more classes. This paper proposes a novel approach by using the social spider optimization (SSO) algorithm in solving the search for optimal cluster centers in FCM. Hanoi, the capital of Vietnam, was chosen as a case study because of its spatial complexity. Multispectral satellite datasets of Landsat 8, Sentinel 2A and SPOT 7 were used. The experiment started with the segmentation process, followed by an examination of the model, then the results were compared with several conventional clustering methods. For accuracy assessment, the FCM minimizing objective functions, user and producer accuracies and overall accuracy were used. The results showed that SSO significantly improved the performance of FCM and outperformed the benchmarked classifiers or other common optimization algorithms. It could be concluded that the model was successfully deployed in the study area and could be suggested as an alternative solution for urban pattern detection. In a broader sense, classification methods will be enriched with the active and fast-growing contribution of nature-inspired algorithms.

Optimal operation of energy storage units in distributed system using social spider optimization algorithm

Optimal placement and performance of energy storage units are two important issues for power systems. Energy storage units help in energy supply and elevated reliability in the network. Energy storage system (ESS) cause many of the variables of planning, designing, and exploiting the network to alter, and thus planning and operational methods for the network will change as well. These units also assist in reducing the peak load with the aim of profit maximization. In this paper, a method is presented to optimize the performance of energy storage units in the power market. To solve the problem, a new optimization algorithm called social spider optimization (SSO) algorithm is employed. Cost reduction, losses reduction and voltage profile improvement are three main scenarios which are considered. The proposed method is applied to a 33-bus standard distribution system and results show that this novel method is efficient to use for ESSs optimal operation.

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

A Real-Coded Optimal Sensor Deployment Scheme for Wireless Sensor Networks Based on the Social Spider Optimization Algorithm

Wireless sensor networks (WSNs) involves a set of wireless sensor nodes located within a region of interest (ROI) to acquire and/or transmit specific information from their surroundings. A common problem in the operation of WSNs is sensor coverage, which is related to the distribution of sensor nodes within their ROI. Several approaches have been proposed to solve this problem; however, most of these methods consider a simplified arrangement scheme based on sensor placement over a set of fixed discrete locations defined by a grid. This fact severally limits the ability of these methods to find potentially better solutions as they are conditioned to select a limited number of candidate solutions. In this paper, a real-coded sensor deployment approach based on the Social Spider Optimization (SSO) algorithm is proposed to solve the problem of optimal sensor deployment (OSD) in WSNs. The performance of our proposed approach (referred in this paper as real-coded SSO [R-SSO]) was also compared against other metaheuristics-based methods used in the literature. Experimental results demonstrate its ability to solve the problem of OSD in terms of accuracy and robustness.

A Social Spider Optimization Algorithm for Hybrid Flow Shop Scheduling with Multiprocessor Task

Hybrid flow shop scheduling with multiprocessor task (HFSPMT) is a strongly NP-hard problem that has a wide range of applications in several real-life industrial environments. Due to its essential complexity and practical relevance, HFSPMT has been of an increasing interest for researchers and industrialists in the last two decades. Social spider optimization (SSO) is a recent swarm intelligence algorithm that has been rarely applied on combinatorial optimization problems. In order to make a small step toward having an idea about its effectiveness in solving combinatorial optimization, in this work, a new SSO algorithm is proposed to solve HFSPMT problem with the objective of makespan minimization. The proposed algorithm is experimented on benchmark instances and compared with other existing swarm intelligence algorithms in the literature. The obtained results and the comparisons show that the performance of the proposed algorithm is highly competitive in terms of solution quality.

Social Spider Optimization Algorithm: Modifications, Applications, and Perspectives

Swarm intelligence (SI) is a research field which has recently attracted the attention of several scientific communities. An SI approach tries to characterize the collective behavior of animal or insect groups to build a search strategy. These methods consider biological systems, which can be modeled as optimization processes to a certain extent. The Social Spider Optimization (SSO) is a novel swarm algorithm that is based on the cooperative characteristics of the social spider. In SSO, search agents represent a set of spiders which collectively move according to the biological behavior of the colony. In most of SI algorithms, all individuals are modeled considering the same properties and behavior. In contrast, SSO defines two different search agents: male and female. Therefore, according to the gender, each individual is conducted by using a different evolutionary operation which emulates its biological role in the colony. This individual categorization allows reducing critical flaws present in several SI approaches such as incorrect exploration-exploitation balance and premature convergence. After its introduction, SSO has been modified and applied in several engineering domains. In this paper, the state of the art, improvements, and applications of the SSO are reviewed.

A Hybridization Approach for Optimal Feature Subset Selection in High Dimensional Data

Feature subset selection assumes an essential part in the fields of data mining and machine learning. A good feature subset selection algorithm can adequately expel unimportant and repetitive elements and consider feature interaction. This not just paves the way to an understanding comprehension of the information, additionally enhances the execution of a learner by improving the generalization capacity and the interpretability of the learning model. Initially, the input micro array dataset is selected from the medical database. Then preprocessing step is done in the input micro array dataset. The resultant output is fed to the second step; here the features are optimally selected using clustering and optimization process. In our proposed technique, the optimal hybrid fuzzy c-means clustering algorithm with artificial bee colony algorithm is applied on the high dimensional micro array dataset to select the important features. Here the proposed method is optimally select the features with the help of social spider optimization algorithm. After that, the classification is done through improved support vector machine classifier. At last, the experimentation is performed by means of different micro array dataset. Experimental results indicate that the proposed classification framework has outperformed by having better accuracy of 93.19% for GLA-BRA-180 dataset when compared existing SVM and neuro fuzzy classifier only achieved 90.69% and 89%.

Computational intelligence modelling of pharmaceutical tabletting processes using bio-inspired optimization algorithms

In pharmaceutical development, it is very useful to exploit the knowledge of the causal relationship between product quality and critical material attributes (CMA) in developing new formulations and products, and optimizing manufacturing processes. With the big data captured in the pharmaceutical industry, computational intelligence (CI) models could potentially be used to identify critical quality attributes (CQA), CMA and critical process parameters (CPP). The objective of this study was to develop computational intelligence models for pharmaceutical tabletting processes, for which bio-inspired feature selection algorithms were developed and implemented for optimisation while artificial neural network (ANN) was employed to predict the tablet characteristics such as porosity and tensile strength. Various pharmaceutical excipients (MCC PH 101, MCC PH 102, MCC DG, Mannitol Pearlitol 200SD, Lactose, and binary mixtures) were considered. Granules were also produced with dry granulation using roll compaction. The feed powders and granules were then compressed at various compression pressures to produce tablets with different porosities, and the corresponding tensile strengths were measured. For the CI modelling, the efficiency of seven bio-inspired optimization algorithms were explored: grey wolf optimization (GWO), bat optimization (BAT), cuckoo search (CS), flower pollination algorithm (FPA), genetic algorithm (GA), particle swarm optimization (PSO), and social spider optimization (SSO). Two-thirds of the experimental dataset was randomly chosen as the training set, and the remaining was used to validate the model prediction. The model efficiency was evaluated in terms of the average reduction (representing the fraction of selected input variables) and the mean square error (MSE). It was found that the CI models can well predict the tablet characteristics (i.e. porosity and tensile strength). It was also shown that the GWO algorithm was the most accurate in predicting porosity. While the most accurate prediction for the tensile strength was achieved using the SSO algorithm. In terms of the average reduction, the GA algorithm resulted in the highest reduction of inputs (i.e. 60%) for predicting both the porosity and the tensile strength.

An Efficient Feature Selection and Classification Using Optimal Radial Basis Function Neural Network

Feature selection is the process of identifying and removing many irrelevant and redundant features. Irrelevant features, along with redundant features, severely affect the accuracy of the learning machines. In high dimensional space finding clusters of data objects is challenging due to the curse of dimensionality. When the dimensionality increases, data in the irrelevant dimensions may produce much noise. And also, time complexity is the major issues in existing approach. In order to rectify these issues our proposed method made use of efficient feature subset selection in high dimensional data. Here we are considering the input dataset is the high dimensional micro array dataset. Initially, we have to select the optimal features so that our proposed technique employed Modified Social Spider Optimization (MSSO) algorithm. Here the traditional Social Spider Optimization is modified with the help of fruit fly optimization algorithm. Next the selected features are the input for the classifier. Here the classification is performed using Optimized Radial basis Function based neural network (ORBFNN) technique to classify the micro array data as normal or abnormal data. The effectiveness of RBFNN is optimized by means of artificial bee colony algorithm (ABC). Experimental results indicate that the proposed classification framework have outperformed by having better accuracy for five benchmark dataset 93.66%, 97.09%, 98.66%, 98.28% and 98.93% which is minimum value when compared to the existing technique. The proposed method is executed in MATLAB platform.

Memetic social spider optimization algorithm for scheduling two-stage assembly flowshop in a distributed environment

Abstract This paper studies the distributed two-stage assembly flowshop problem with separate setup times, which is a generalisation for the regular two-stage assembly flowshop problem in the distributed manufacturing environment. The optimization objective is to find a suitable job schedule such that the criterion of total completion time is minimized. To deal with such a problem, we propose a novel memetic algorithm (MA) based on a recently developed social spider optimization (SSO). To the best of our knowledge, it is the first effort to explore the SSO-based MA (MSSO) and to apply SSO in the field of combinational optimization. In the proposed MSSO algorithm, we first modify the original version of SSO to adapt to the distributed problems, and then integrate two improvement techniques, problem-special local search and self-adaptive restart strategy, within MA framework. In the numerical experiment, the parameters used in MSSO are calibrated and suitable parameter values are suggested based on the Taguchi method. Experimental results and comparisons with the existing algorithms validate the effectiveness and efficiency of the proposed MSSO for addressing the considered problem. In addition, the effect of problem scale parameters on MSSO and the effectiveness of the proposed improvement techniques are also investigated and demonstrated.

Automatic acute lymphoblastic leukemia classification model using social spider optimization algorithm

The main purpose of this paper is to identify and segment each white blood cells (WBCs) from microscopic images and then classify it to affected or non-affected by acute lymphoblastic leukemia (ALL). The proposed model started by, firstly, detection and isolation of (WBCs). This was performed by conversion of cell images from RGB to CMYK color space. Then histogram equalization followed by thresholding estimation by Zack algorithm was performed for segmentation of cells from the surrounding blood contents. Secondly, some features were extracted from the segmented cells, and they included color, shape, texture and hybrid features. Thirdly, social spider optimization algorithm (SSOA) was applied to select the most appropriate features. Finally, several classifiers were used to validate the performance of the proposed algorithms. The proposed model was applied to the well-known ALL-IDB2 dataset. The results show: firstly, the segmentation (identification and isolations) results were 99.23, 100 and 97.1% of segmentation accuracy, sensitivity and specificity, respectively, which is the highest among other published papers. Secondly, the classification accuracy of the proposed model was higher than the non-SSOA in most experiments. The proposed system achieved remarkable results; 95.23% of classification accuracy with a feature reduction ratio of about 50%, which is the highest among the most recent works on the same dataset.

An Efficient Social Spider Optimization for Flexible Job Shop Scheduling Problem

The job shop scheduling problem (JSSP) is a standout amongst the most difficult combinatorial improvement issues. Flexible job shop scheduling is an augmentation of the JSSP that permits an action to be processed by any machine from a given set along with various courses. This paper exhibits another method in view of a social insect method to explain the single goal flexible JSSP (FJSSP). This advancement focuses on the distinction between the two diverse search specialists (insects): males and females. In the proposed methodology, a few earlier guidelines are displayed to develop the underlying population with an abnormal state of value. In the proposed investigation, a few earlier standards are exhibited to build the underlying population with an abnormal state of value. Imitation comes about on the standard test cases demonstrate that social spider optimization (SSO) has a superior merging execution contrasted and single-goal existing roused optimization process. For the examination of twenty benchmark problems, the result demonstrates that the minimum make span achieves in the benchmark problem LA03 as 524 in SSO algorithm. This proposed work accomplished 92.33% exactness in SSO strategy contrasted with other optimization process and this algorithm reduces the computational time and less expensive.

A Novel Pilot Decontamination Method for Massive MIMO Systems Using Social Spider Optimization Algorithm

A Novel Pilot Decontamination Method for Massive MIMO Systems Using Social Spider Optimization Algorithm

A Social Spider Optimization Algorithm with Chaotic Initialization for Robust Clustering

Several real-life datasets at times contain few observations whose characteristics are completely different from the rest of the patterns. Such patterns occur due to faulty readings taken or due to presence of noise are termed as outliers. In this paper a recently developed algorithm Social Spider Optimization (SSO) is employed for robust clustering. The robust clustering takes care of the outliers with the use of Robust Distance (RD) as the cost function. The SSO is a swarm intelligence algorithm inspired by the cooperative behavior of spiders on a web. In this paper the initialization of spiders are carried out by Chaotic sequence which enhances its performance compared to the original SSO which uses random sequence. The simulation studies are carried out on two synthetic and three real life datasets. Comparative analysis reveals superior performance of proposed Chaotic SSO (C-SSO) over Original SSO, Chaotic PSO, PSO, Chaotic K-means and K-means algorithms. It is observed that the Chaotic versions of the algorithms have better accuracy ( here represented by lower mis-classification index) over the regular algorithms.

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

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%.

A Binary Social Spider Optimization algorithm for unsupervised band selection in compressed hyperspectral images

The presence of high spectral correlation in hyperspectral images has demanded dimensionality reduction. Selection of bands based on supervised techniques has been already developed for dimensionality reduction. In this manuscript, the selection of an optimal set of bands is formulated as an unsupervised problem. The images are initially compressed using discrete wavelet transform (DWT). Then, the details of the image are extracted in the transform domain using entropy which accounts for spatial information content and first spectral derivative (FSD) that exploits the temporal redundancy. These details are optimized using meta-heuristic algorithms to obtain the final set of bands. A new Binary Social Spider Optimization algorithm (BSSO) is proposed which maintains the exploration–exploitation balance and avoids premature convergence of solutions. The performance of BSSO is compared with other benchmark algorithms: Clonal algorithm (BCLONAL), Time-varying transfer function Binary Particle Swarm Optimization (TV-BPSO), Binary Genetic Algorithm (BGA), Improved Gravitational Search Algorithm (IBGSA) and New Binary Bat Algorithm (NBBA). A simulation study is carried on four standard hyperspectral images. The proposed BSSO have provided with 83% of overall accuracy for 24% bands in Salinas image set, 75% for 33% in Pavia University, 75% against 26% for Pavia Center and finally 91% in KSC with 32% bands retention. The achievement of higher accuracy at less than 50% bands is appreciable.