Spider Algorithm Research Articles

A novel methodology for optimal land allocation for agricultural crops using Social Spider Algorithm.

In the current agricultural scenario, availability of suitable land for cultivation is less and profitable allocation of the land for cultivating crops seems to be a cumbersome task. Crop planning optimization is a major research field in agriculture, in which land optimization is a significant challenge, which falls under the category of combinatorial optimization problems. The main objective of the present research is to maximize the net income from agriculture through optimal land allocation. Bio-inspired algorithms are quite popular in solving combinatorial optimization problems. Social Spider Algorithm (SSA), a new bio-inspired algorithm, is used to solve land optimization problem in this research based on the simulation of cooperative behaviour of social spiders. The agricultural area chosen for case study is the Coimbatore region, located in Tamilnadu state, India and the relevant data for the crops are collected from Tamilnadu Agricultural University Coimbatore, India. The optimal planting area, crop productivity for various land holdings and the water requirements are computed by SSA and the results have shown better directions for agricultural planning to improve the profit with constrained land area and water limitations.

Hybrid Starling Social Spider Algorithm for Energy and Load Aware Task Scheduling in Cloud Computing

The efficiency of the cloud-based systems is greatly relying on the task scheduling algorithm which affects the performance parameters such as makespan, response time, degree of imbalance and cost. In recent years, the energy efficiency is also considered as another challenging issue which affects the efficiency of cloud computing systems. This paper proposes a Hybrid Starling Social Spider Algorithm (Starling-SSA) for Energy and Load Aware Task Scheduling in cloud computing. The Starling-SSA is designed as a hybrid algorithm inspired by the intelligent behavior of social spider and the collective response behavior of starling birds. The foraging behavior of spider is implemented to identify the best VMs for the given task with minimum makespan and degree of imbalance. In addition to this, the distance factor is incorporated inspired by starling flock distance in order identify the closeness of VM pairs and avoids the VMs that are far away, thereby VMs can be limited during the searching process. This will greatly reduce energy consumption by taking only VMs that are belongs to the distance factor. The performance metrics such as makespan, degree of imbalance and energy efficiency are evaluated against the existing algorithms such as EATS, CBAT and HC-ACO. The results presents a significant improvements when comparing to the existing algorithms.

Multi-level image thresholding based on social spider algorithm for global optimization

Thresholding is one of the simplest and popular technique for segmenting images. Maximum between-class variance (Otsu’s) method is one of the well-known and widely used method in case of segmentation. Not only Otsu could be used for bi-level thresholding but also it could be extended to multi-level image thresholding. Finding the optimum threshold values in multi-level case is very time consuming process, thus optimization algorithm can deal with this problem. In this paper social spider algorithm for global optimization has been used for maximizing the between-class variance to carry out multi-level image thresholding. Experimental outcomes have demonstrated that the proposed method is capable of estimating threshold values and yield satisfying outcome.

Miniaturization of a Microstrip Patch Antenna with a Koch Fractal Contour Using a Social Spider Algorithm to Optimize Shorting Post Position and Inset Feeding

This paper presents a social spider optimization (SSO) design of a small-size microstrip antenna. Two antenna miniaturization techniques, based on the use of a Koch fractal contour and a shorting post (connecting the patch to the ground plane), are combined to enable a major size reduction. The antenna is inset fed by a microstrip line. The developed SSO algorithm is used to find out the best radius and position of the shorting post and the length of the inset feed, to achieve the desired resonant frequency with good impedance matching. Antenna prototypes have been fabricated and measured. The good agreement obtained between numerical simulation and experimental results has validated the design procedure. Compared with a conventional rectangular patch, the antenna resonance frequency is reduced from 2.45 GHz to 730 MHz, which corresponds to a remarkable miniaturization of about 70%. The proposed antenna is suitable for applications in the 700-800 MHz frequency range, such as 4G mobile communication systems.

Improvement of Neural Networks with Swarm Intelligence Algorithm for Credit Scoring

Neural networks are widely used in automatic credit scoring systems with high accuracy and outstanding efficiency. However, in the absence of prior knowledge, it is difficult to determine the combination of parameters, which makes its application limited in practice. This paper presents a higher accurate and robust credit scoring model based on neural networks that have been trained with the optimal swarm intelligence algorithm. Specifically, we trained neural network with seven different swarm intelligence algorithm (bat algorithm, chicken swarm optimization, cuckoo search optimization, firefly algorithm, particle swarm optimization, social spider algorithm, and whale swarm algorithm) to find out the superior combination of parameters in the neural network and to identify the swarm intelligence algorithm seeking the superior solution most efficiency. It shows that the neural networks trained with swarm intelligence algorithm outperforms competing models (logistic regression, naive Bayesian, determinant analysis, K nearest neighbor, decision tree, and support vector machine), inter alia, the neural network trained with social spider algorithm performs the best. Better performance of the neural network is particularly salient with larger dataset, thus making it amenable for real-time implementation.

Application of Social Spider Algorithm to Optimize Train Energy

This study incorporated the social spider algorithm (SSA), which simulates the preying behaviors of a group of spiders, to explore energy-efficient train operations. In this SSA, the magnitude of the vibration signal transmitted by the prey on a simulated spider web was used to determine the location with the most abundant food, which was equivalent to the optimal solution for train acceleration. In addition, a random movement model was implemented in the SSA to facilitate its capability for both local and global search and thereby increase its probability in identifying the optimal solution. A railroad route in Taiwan was simulated to verify the feasibility of this approach; the simulated data were then compared with the measured data from the actual test. According to the measured data, the SSA can formulate feasible railroad energy conservation plans and can assist locomotive engineers in planning their train operations. The results of the SSA were also compared to those of the teaching–learning-based optimization, to validate the superiority of the SSA.

A novel hybrid PSO–GWO algorithm for optimization problems

In this study, we propose a new hybrid algorithm fusing the exploitation ability of the particle swarm optimization (PSO) with the exploration ability of the grey wolf optimizer (GWO). Our approach combines two methods by replacing a particle of the PSO with small possibility by a particle partially improved with the GWO. We have evaluated our approach on five different benchmark functions and on three different real-world problems, namely parameter estimation for frequency-modulated sound waves, process flowsheeting problem, and leather nesting problem (LNP). The LNP is one of the hard industrial problems, where two-dimensional irregular patterns are placed on two-dimensional irregular-shaped leather material such that a minimum amount of the material is wasted. In our evaluations, we compared our approach with the conventional PSO and GWO algorithms, artificial bee colony and social spider algorithm, and as well as with three different hybrid approaches of the PSO and GWO algorithms. Our experimental results reveal that our hybrid approach successfully merges the two algorithms and performs better than all methods employed in the comparisons. The results also indicate that our approach converges to more optimal solutions with fewer iterations.

Dynamic clustering with binary social spider algorithm for streaming dataset

Technical advancement in various fields like social network, health instruments and astronomical devices poses massive capturing and sensing capacity that enables huge data generations. This demands substantial storage space and voluminous data processing capacity. Streaming data clustering imparts an efficient method for handling this dataset by extracting significant information. In this article, dynamic estimation of clusters in evolving data stream is designed by incorporating swarm optimization technique. One of the recently reported algorithms inspired from the social behavior of spiders residing in huge colonies is reformulated in binary domain. The main contribution is to use the binary social spider optimization (BSSO) for dynamic data clustering of evolving dataset (DSC-BSSO). The proposed work is able to prove efficiency and efficacy as compared to the other recent existing algorithms. BSSO is well tested on various benchmark unimodal, multimodal and binary optimization functions. Results are reported in terms of parametric and nonparametric. The testing of DSC-BSSO is also done on various streaming datasets in terms of time and memory complexity. The proposed work is able to obtain compact and well-separated clusters in less than one-fourth of a minute for about 10,000 samples.

Designing of a Risk Assessment Model for Issuing Credit Card Using Parallel Social Spider Algorithm

ABSTRACTThe financial creditability of the customer needs to be verified by the lender/bank before issuing a credit card. This involves assessment of factors like the economic, social or social-economic background of the person. Thus, the features incorporated into the analysis are mixed data type ex. Income (numerical) and Property Owned (Categorical). In this manuscript, a credit card lending model is designed using a recently proposed parallel social spider algorithm by Shukla and Nanda in 2016. Suitable modifications have been introduced in the coding scheme and mating procedure to efficiently solve the credit assessment problem. Experiments are carried out on various standard credit card data available like German, Australian and Japanese credit card datasets. The superior performance of proposed algorithm is reported as compared to that achieved by K-means, parallel real genetic algorithm and parallel particle swarm optimization (PPSO). The Silhouette Index obtained by various algorithms specifically for Germen dataset are 0.56% by K-means, 0.86% by parallel Real Coded Genetic algorithm, 0.71% by PPSO and 0.84% by proposed method.

Multi‐objective social spider optimisation algorithm

Considering that the social spider algorithm is still unable to solve the multi-objective optimisation problem, this study presents a multi-objective social spider optimisation algorithm. Firstly, a new normalised fitness value formula is proposed based on the multi-objective optimisation purposes, which is able to trade off the non-dominated rankings and crowded distances and evaluate individual strengths and weaknesses effectively; secondly, the gravitational factor is used in order to balance the impact of individual fitness and distance to individual performance, which improves the vibration perception ability of the calculation method as well; once again, the renewal pattern of the female and male population is improved in order to balance the convergence rate and population diversity of the algorithm; lastly, the environmental selection strategy which is based on cosine distance is proposed for female and male population renewal. Testing on the ZDT test set, experimental results show that, compared with the six representative multi-objective evolutionary algorithms, the proposed algorithm in this study has better distribution and better convergence performance.

A Heuristic Approach to Design Linear and Circular Antenna Array for Side Lobe Reduction

This paper presents a heuristic approach-based design of a symmetric linear antenna array (LAA) and a non-uniformly excited single-ring circular antenna array (CAA) to improve the far-field radiation characteristics. The far-field radiation pattern improves with lower side lobe level (SLL) which is essential for the reduction in interference in the entire side lobe regions. A recently proposed evolutionary algorithm called social spider algorithm (SSA) is applied to determine the optimum sets of current excitation weights and the inter-element spacing among the array elements to reduce the SLL. In this context, 10-element and 16-element LAA and 8-element, 10-element and 12-element CAA design examples are presented by optimizing the array parameters. The simulation results obtained by using SSA-based approach confirm a considerable improvement in SLL and some restricted improvement in half-power beam width with respect to the uniform array pattern and those of the recently reported literature.

Selecting Best Software Reliability Growth Models: A Social Spider Algorithm based Approach

Software Reliability is considered to be an essential part of software systems; it involves measuring the system's probability of having failures; therefore, it is strongly related to Software Quality. Software Reliability Growth Models are used to indicate the expected number of failures encountered after the software has been completed, it is also an indicator of the software readiness to be delivered. This paper presents a study of selecting the best Software Reliability Growth Model according to the dataset at hand. Several Comparison Criteria are used to yield a ranking methodology to be used in pointing out best models. The Social Spider Algorithm SSA, one of the newly introduced Swarm Intelligent Algorithms, is used for estimating the parameters of the SRGMs for two datasets. Results indicate that the use of SSA was efficient in assisting the process of criteria weighting to find the optimal model and the best overall ranking of employed models.

Transmission Line Outage Detection and Identification by Communal Spider Optimization Algorithm

The load on the power system network is proliferating along with massive inter-area transfers. Hence it is substantial to have reliable real-time information about the network parameters of the electric power system. To monitor real-time operational reliability, the network model of the system has to be updated continuously in a timely manner to reflect current system conditions. For this continuous data updating, the Phasor Measurement Units (PMUs) are deployed. PMUs measure the phase at very high speed at different locations by different devices and synchronize them with time. In manipulating the arithmetical possessions of voltage phase angle capacities attained from PMUs, this method is formulated. The proposed method detects and identifies the line outages in near real-time. In this method, outage in an unknown line is detected by using Communal Spider Algorithm (CSA) and is verified on Indian utility- 62 bus test system. Using CSA, the system performance of the power network model is optimized and the line outage is reduced.

ENHANCED SPIDER ALGORITHM FOR MINIMIZATION OF REAL POWER LOSS

In this paper Enhanced Spider (ES) algorithm is proposed to solve reactive power Problem. Enthused by the spiders, a new Enhanced Spider (ES) algorithm is utilized to solve reactive power problem. The composition is primarily based on the foraging approach of social spiders, which make use of of the vibrations spread over the spider web to choose the position of prey. The simulation results demonstrate high-quality performance of Enhanced Spider (ES) algorithm in solving reactive power problem. The projected Enhanced Spider (ES) algorithm has been tested in standard IEEE 57,118 bus systems and compared to other reported standard algorithms. Results show that Enhanced Spider (ES) algorithm is more efficient than other algorithms in reducing the real power loss.

Secure Key Agreement with Rekeying Using FLSO Routing Protocol in Wireless Sensor Network

A challenge in wireless sensor network (WSN) is to design an energy efficient and secure network protocol because sensors are the wireless device with limited resources. To overcome the problem of insecure transmission in WSNs, efficient and light weight key agreement protocol (LARK) with rekeying is designed in which sensed data is carried out through the gateway node. In this paper, a novel construction is largely deliberated to abolish the security issues. The novel method called fuzzy C means cluster based light weight social spider optimization (FLSO) protocol for improving secure transmission with less energy consumption. In FLSO protocol, fuzzy C means clustering is used for grouping the nodes based on individual cluster’s membership and cluster head selection is associated with the lowest ID of the corresponding nodes. Next to clustering, secure transmission is done with the help of light weight key agreement protocol (LARK) with rekeying technique. In addition to the symmetric key, another one session key is generated to improve security using the Diffie Hellman cryptographic algorithm. Then the secure routing is done with the help of social spider algorithm. Finally, the simulation is performed on the platform of MATLAB simulator. In which results are taken in terms of memory, communication overhead, end to end delay, transmission delay, packet delivery ratio, authentication analysis, computational time and energy consumption. Results indicate that FLSO protocol provides efficient transmission by reducing energy consumption and providing security over the path of the transmission.

Designing multi-layer quantum neural network controller for chaos control of rod-type plasma torch system using improved particle swarm optimization

Due to outstanding learning capabilities, neural networks have attracted a great deal of research interests and various structures for them have been presented. Quantum neural network is one these structures used in quantum calculations to improve learning capabilities of neural networks. In this paper, using a quantum neural network, a chaos controller for rod-type plasma torch system is developed. This system is extensively nonlinear and its dynamics shows considerable sensitivity to its parameters that makes it a good challenge for investigation of controllers’ performances. It is very common to utilize particle swarm optimization (PSO) algorithm to determine the parameters of neural networks. The novelty of this paper is changing the search space of the parameters in each particle based on that parameter’s value. Also, weaker particles will be deleted and new particles will be substituted. These modifications have enhanced the proposed algorithm performance by adding the ability of global search and also searching in a reasonable space. To highlight the superiority of the proposed algorithm, the results of eight meta-heuristics optimization algorithms such as artificial bee colony, social spider algorithm and imperialist competitive algorithm are presented for comparison. Simulation results show that the improved PSO (IPSO) proposed in this paper is superior to other algorithms from the viewpoint of convergence speed and accuracy. Moreover, in chaos control of a rod-type plasma torch system, the proposed method outperforms a PID controller and also a neural network controller using perceptron.

Chaotic social spider algorithm for load balance aware task scheduling in cloud computing

In recent years, the revolution of cloud computing has taken the IT business to greater heights with the rapid sharing of vast web resources over the internet. Proficient task scheduling and balanced task distribution is still exists as a major challenging issue in cloud computing system due to dynamic heterogeneous nature of resources and tasks. It is a NP-hard problem where the scheduler needs to find the best optimal virtual machines with minimum makespan and proper resource utilization. The major part of this problem is to design an efficient intelligent searching pattern to schedule the tasks in best virtual available machines. In this paper we propose a meta heuristic algorithm called chaotic social spider algorithm inspired by social spider to tackle the problem of task scheduling in various heterogeneous virtual machines. This paper focus on minimizing overall makespan with effective load balancing by modelling the swarm intelligence of social spider with chaotic inertia weight based random selection. The proposed algorithm prevents the local convergence and explores the global intelligent searching in finding the best optimized virtual machine for the user task among the set of virtual machines with minimum makespan and balanced resource utilization. We have made the simulation and performance evaluation using cloudsim toolkit and compared the results with other swarm intelligent based algorithms such as GA, PSO and ABC. The evaluation results show that there is a major improvement in minimizing the makespan with balanced task distribution.

An algorithm inspired by social spiders for truss optimisation problems

PurposeThe purpose of this paper is to evaluate the performance of social spider algorithm (SSA) to solve constrained structural optimisation problems and to compare its results with others algorithms such as genetic algorithm, particle swarm optimisation, differential evolution and artificial bee colony.Design/methodology/approachTo handle the constraints of the problems, this paper couples to the SSA an efficient selection criteria proposed in the literature that promotes a tournament between two solutions in which the feasible or less infeasible solution wins. The discussion is conducted on the competitiveness of the SSA with other algorithms as well as its performance in constrained problems.FindingsSSA is a population algorithm proposed for global optimisation inspired by the foraging of social spiders. A spider moves on the web towards the position of the prey, guided by vibrations that occur around it in different frequencies. The SSA was proposed to solve problems without constraints, but these are present in most of practical problems. This paper evaluates the performance of SSA to solve constrained structural optimisation problems and compares its results with other algorithms such as genetic algorithm, particle swarm optimisation, differential evolution and artificial bee colony.Research limitations/implicationsThe proposed algorithm has no limitations, and it can be applied in other classes of constrained optimisation problems.Practical implicationsThis paper evaluated the proposed algorithm with a benchmark of constrained structural optimisation problems intensely used in the literature, but it can be applied to solve real constrained optimisation problems in engineering and others areas.Originality/valueThis is the first paper to evaluate the performance of SSA in constrained problems and to compare its results with other algorithms traditional in the literature.

An improved social spider optimization algorithm based on rough sets for solving minimum number attribute reduction problem

The minimum number attribute reduction problem is an important issue when dealing with huge amounts of data. The problem of minimum attribute reduction is formally known to be as an NP complete nonlinearly constrained optimization problem. Social spider optimization algorithm is a new meta-heuristic algorithm of the swarm intelligence field to global solution. The social spider optimization algorithm is emulates the behavior of cooperation between spiders based on the biological laws of the cooperative colony. Inspired by the social spiders, in this paper, an improved social spider algorithm for the minimal reduction problem was proposed. In the proposed algorithm, the fitness function depends on the rough sets dependency degree and it takes into a consideration the number of selected features. For each spider, the fitness function is computed and compared with the global best fitness value. If the current value is better, then the global best fitness is replaced with it and its position became the reduct set. Then, the position of each spider is updated according to its type. This process is repeated until the stopping criterion is satisfied. To validate the proposed algorithm, several real clinical medical datasets which are available from the UCI Machine Learning Repository were used to compute the performance of the proposed algorithm. The experimental results illustrate that the proposed algorithm is superior to state-of-the-art swarm-based in terms of classification accuracy while limiting number of features.

Security Aware Fuzzy C-Means Cluster-based Social Spider Elliptic Curve Cryptography (FSER) Routing Protocol in FSO MANET

Objective: To design an FSER (Fuzzy _means cluster-based Social spider Elliptic Curve Cryptography Routing) protocol to enhance the security and prevent the loss of data while routing in FSO MANET. Methods/Statistical Analysis: Our proposed protocol scheme is based on three stages: (i) Cluster formation stage, (ii) Network routing stage and (iii) Secure communication stage. In the first stage, an efficient Fuzzy c-means (FCM) clustering process is used to form clusters in FSO MANET. In the second stage, Social Spider Algorithm (SSA) is used to create the routing table through vibration to other spiders based on the distance from the members and efficient routing through vibration intensity of spiders in the web. In the third phase, we suggest an Elliptic Curve Cryptography (ECC) Algorithm for MANET to provide secure data delivery from source to destination. Findings: A hierarchal secure FSER routing protocol for FSO MANET is proposed. Nowadays, MANETs are demanding higher bandwidths, smarter and faster. In this paper, the higher demands on growing application can be supported using FSO which has the advantage of low power consumption and higher bandwidth to support huge number of nodes. First, a temporary topology is created by forming clusters with cluster head in order to consume the energy in which each node having multiple transceivers. Then a quality aware routing is performed by checking the path for QoS demand from source to destination. A secure data delivery from source to destination is provided by encrypting the packets using the ECC authentication scheme. Improvements/Application: Experimental simulations on FSO MANETs are implemented in MATLAB tool by finding an optimal path to demonstrate the effectiveness of the proposed FSER protocol that fulfills several Quality of Service (QoS) constraints. The designed proposed protocol in FSO is observed to be performing well with high speed optical modulation for bandwidth intensive applications like multimedia.