Artificial Fish Swarm Optimization Research Articles

An artificial fish swarm optimization algorithm for the urban transit routing problem

The Urban Transit Routing Problem (UTRP) is an NP-hard discrete problem that deals with the design of routes for public transport systems. It is a highly complex, multiply constrained problem, while the evaluation of candidate route sets can prove both challenging and time-consuming, with many potential solutions rejected on the grounds of infeasibility. Due to its difficulty, metaheuristic methods, such us swarm intelligence algorithms, are considered highly suitable for the UTRP. The suitability of these methods heavily relies on the correct adaptation of the chosen method for a discrete-space problem, the initialization procedure, and the solution evaluation method. In this context, this study proposes an artificial fish swarm optimization algorithm for the efficient solution of the UTRP, presenting a novel discrete-space adaptation of the former. The results are subsequently compared to 14 other algorithms, including evolutionary, swarm intelligence and hyper-heuristic implementations, using Mandl’s widely used and accepted benchmark. Comparison of the produced solutions with published results on Mandl’s benchmark network, shows that the developed algorithm yields superior results to the existing techniques, yielding very high shares of direct trip coverage, which is vital for transit systems to attract riders and contribute to urban sustainability. A new indicator for operator cost calculation is also developed and integrated into our analysis, offering insights on the trade-offs between user and operator costs. Differences in generated solutions, influenced by the weighting factor value, can result in variations of up to 13% in direct trip coverage and 1.5 minutes in average travel time.

A new deep-learning with swarm based feature selection for intelligent intrusion detection for the Internet of things

Humanity can benefit from the Internet of Things (IoT) paradigm in many different ways. IoT devices are nonetheless susceptible to different cyber-attacks launched by the attacker owing to their constrained resources. Using MLTs (Machine Learning Techniques) in IDSs (Intrusion Detection Systems) network data is classified as either benign or harmful depending on its characteristics. However, the growth in IoT devices and the massive, high-dimensional data they produce need the development of more effective searches and learning algorithms. By implementing optimization algorithms on the BoT-IoT dataset, an intruder recognition method for IoT based on DLTs (Deep Learning Techniques). An advanced DLT namely FR-CNN (Faster Recurrent Convolution Neural Network) is used in this framework to categorize incursions. In this paper, a novel GA-FR-CNN (Genetic Algorithm and Faster Recurrent Convolution Neural Network) has been proposed. Additionally, this paper introduces a brand-new feature selection method for IDSs. Network dataset's complexities are greatly reduced and the usage of AAFSO (Assimilated Artificial Fish Swarm Optimization) method to improve recommended systems assisted in identifying characteristics that were important to the problem. After gathering the features, they were sent into GA-FR-CNN algorithms for processing. The experimental result shows the suggested method for an intruder detecting systems, which is based on DLTs, while using UNSW-NB 15 dataset, the AAFSO with GA-FR-CNN provides high accuracy of 94.4880% when compared to the existing methods. When using BOT-IoT dataset, the AAFSO with GA-FR-CNN provides high accuracy of 93.7756%. The proposed method achieves better performance in UNSW-NB 15 dataset than BOT-IoT dataset.

Artificial Fish Swarm Optimization with Deep Learning Enabled Opinion Mining Approach

Sentiment analysis or opinion mining (OM) concepts become familiar due to advances in networking technologies and social media. Recently, massive amount of text has been generated over Internet daily which makes the pattern recognition and decision making process difficult. Since OM find useful in business sectors to improve the quality of the product as well as services, machine learning (ML) and deep learning (DL) models can be considered into account. Besides, the hyperparameters involved in the DL models necessitate proper adjustment process to boost the classification process. Therefore, in this paper, a new Artificial Fish Swarm Optimization with Bidirectional Long Short Term Memory (AFSO-BLSTM) model has been developed for OM process. The major intention of the AFSO-BLSTM model is to effectively mine the opinions present in the textual data. In addition, the AFSO-BLSTM model undergoes pre-processing and TF-IFD based feature extraction process. Besides, BLSTM model is employed for the effectual detection and classification of opinions. Finally, the AFSO algorithm is utilized for effective hyperparameter adjustment process of the BLSTM model, shows the novelty of the work. A complete simulation study of the AFSO-BLSTM model is validated using benchmark dataset and the obtained experimental values revealed the high potential of the AFSO-BLSTM model on mining opinions.

Efficient Hybrid Energy Optimization Method in Location Aware Unmanned WSN

The growth of Wireless Sensor Networks (WSNs) has revolutionized the field of technology and it is used in different application frameworks. Unmanned edges and other critical locations can be monitored using the navigation sensor node. The WSN required low energy consumption to provide a high network and guarantee the ultimate goal. The main objective of this work is to propose hybrid energy optimization in local aware environments. The hybrid proposed work consists of clustering, optimization, direct and indirect communication and routing. The aim of this research work is to provide and framework for reduced energy and trusted communication with the shortest path to reach source to destination in WSN and an extending lifetime of wireless sensors. The proposed Artificial Fish Swarm Optimization algorithm is used for energy optimization in military applications which is simulated using Network Simulator(NS) tool. This work optimizes the energy level and the same is compared with various genetic algorithms (GA) and also the cluster selection process was compared with the fission-fusion (FF) selection method. The results of the proposed work show, improvement in energy optimization, throughput and time delay.

Optimal Double Layer Secret Sharing Scheme for Biometrics

Visual secret sharing (VSS) method is an encryption method to ensure the Security of secret data, that just performs the partitioning of ‘n’ shares and distributes among ‘n’ users in an ideal way so that exposing of less than ‘n’ shares is of no utilization. However, some dishonest members or hackers team up and tries to cheat other members in the group. Therefore, we have developed a dual layer secret sharing scheme based on universal share based secret sharing scheme. The dual layer is composed of threshold based secret sharing and then followed by the universal share based secret sharing. Here, the universal share is maintained by the trusted party avoids the contribution of dishonest participants. Moreover, to ensure additional security, the proposed approach employs Oppositional Artificial Fish Swarm Optimization (OAFSO) based Stream Cipher encryption technique to encrypt the shares, shows the novelty of the work. Furthermore, the confidentiality is enhanced with Biometric fingerprint authentication step, where the acknowledged users are alone allowed to get the decrypt shares with that the user can retrieve the secret data. The proposed fingerprint authentication method also makes use of Secure Hash Algorithm (SHA 1) to store the fingerprints and the matching is also done with hashed fingerprints only. So that the attackers cannot take and corrupt the stored fingerprints and the misuse of fingerprints is not possible. Finally the performance analysis is made with existing approaches in terms of PSNR and MSE. Maximum PSNR is 58.9802 and minimum MSE value is 0.6232, while the existing methods provide very less PSNR and greater MSE values than the proposed methods.

Design and Implementation of Group Animation Based on Multimedia Intelligent Algorithm

In order to improve the autonomy and intelligence of group animation behavior, this paper proposes a particle swarm optimization algorithm for automatic control of group animation behavior. After the user inputs the group animation behavior construction target, the individual path is planned according to the fixed behavior rules, and the particle swarm optimization algorithm and the improved artificial fish swarm optimization algorithm are used to select the optimal action for the individual in the group animation according to the top-down control process. The experimental results show that after the PSO converges to the global optimal solution, the evolution times of the PSO converge to the global optimal solution again when the surrounding environment changes 10 times. The population diversity of particle swarm optimization algorithm is obviously better than those of the other two algorithms in the process of automatically controlling the movement of individuals to the circle and to the stand in the crowd animation. Compared with the original algorithm AFSA, the improved artificial fish swarm algorithm proposed in this paper is easier to obtain the global optimal value, and the optimization speed is also improved. In conclusion, the example analysis verifies that the algorithm can effectively realize the automatic control of group animation behavior and has high convergence speed and high autonomy.

A hybrid optimization with ensemble learning to ensure VANET network stability based on performance analysis

High vehicle mobility, changing vehicle density and dynamic inter-vehicle spacing are all important issues in the VANET environment. As a result, a better routing protocol improves VANET overall performance by permitting frequent service availability. Therefore, an ensemble-based machine-learning technique is used to forecast VANET mobility. Effective routing based on a hybrid metaheuristic algorithm combined with Ensemble Learning yields significantly improved results. Based on information collected from the Road Side Unit (RSU) or the Base Station, a hybrid metaheuristic (Seagull optimization and Artificial Fish Swarm Optimization) method is used to estimate (BS). The suggested approach incorporates an ensemble machine learning and hybrid metaheuristic method to reduce the latency. The current model's execution is calculated using a variety of Machine Learning techniques, including SVM, Nave Bayes, ANN, and Decision Tree. As a result, the performance of machine learning algorithms may be studied and used to achieve the best results. Comparative analysis between the proposed method (HFSA-VANET) and (CRSM-VANET was done on different performance parameters like throughput, delay, drop, network lifetime, and energy consumption to assess system performance on two factors Speed and Nodes. The HFSA-VANET method shows an overall drop in the delay of 33% and a decrease in the energy consumption of 81% and an increase of 8% in the throughput as compared with the CRSM-VANET method at 80 node. The proposed method that is HFSA-VANET has been implemented in the MATLAB and NS2 environment.

A decision support system for diagnosis of hepatitis disease using an integrated rough set and fish swarm algorithm

AbstractHealthcare data analysis is a primary concern. It leads to multiple levels of knowledge extraction for decision support systems because of the presence of uncertainties. Therefore, this paper integrates the rough set and artificial fish swarm optimization to develop a decision support system that handles uncertainties present in an information system. In the initial stage, the artificial fish swarm—the rough set procedure is implemented in finding vital features. Further, in the second phase, the rough set uses these vital features to develop a decision support system. The above model is analyzed over hepatitis B disease. The proposed model attains an accuracy of 92.4%. Further, the proposed model is compared with the classical rough set, decision tree, and artificial fish swarm‐decision tree model. The accuracy obtained is 88.9%, 83.3%, and 90.8%, respectively. The proposed model has a greater accuracy of 3.5% than the rough set model and has a greater accuracy of 9.1% than the decision tree model. Simultaneously, the proposed model has 1.6% greater accuracy than the artificial fish swarm‐decision tree model. Therefore, it is believed that the projected decision support system may be used to prevent and detect hepatitis B diseases.

Multi-Objective Aerodynamic Optimization of a High-Speed Train Head Shape Based on an Optimal Kriging Model

An optimal Kriging surrogate model based on a 5-fold cross-validation method and improved artificial fish swarm optimization is developed for improving the aerodynamic optimization efficiency of a high-speed train running in the open air. The developed optimal Kriging model is compared with the original Kriging model in two test sample points, and the prediction errors are all reduced to within 5%. Thus, the optimal Kriging model is selected for use in each iteration to approximate the CFD simulation model of a high-speed train in subsequent optimization. After that, the strong Pareto evolutionary algorithm II (SPEA2) is adopted to obtain a series of Pareto-optimal solutions. Based on the above work, a multi-objective aerodynamic optimization design for the head shape of a high-speed train is performed using a free-form deformation (FFD) parameterization approach. After optimization, the aerodynamic drag coefficient of the head car and the aerodynamic lift coefficient of the tail car are reduced by 5.2% and 32.6%, respectively. The results demonstrate that the optimization framework developed in this paper can effectively improve optimization efficiency.

Research on ultrasonic defect identification method of well control manifold pipeline based on IAFSA-SVM

In view of the classification of corrosion defects of well controlled manifold pipelines, an ultrasonic defect recognition method based on the combination of support vector machine(SVM) and improved artificial fish swarm algorithm (IAFSA) is proposed. Firstly, perform wavelet packet decomposition on the ultrasonic defect signal waveform to obtain the characteristic vector of characterizes the defect type; Then establish the support vector machine defect classification model, and use the improved artificial fish swarm algorithm to optimize the support vector machine parameters. Finally, a software and hardware experimental platform for the classification of pipeline corrosion defects of the well control manifold is built to carry out software simulation and experimental analysis. The experimental results show that the recognition rate of the defect classification model based on improved artificial fish swarm optimization support vector machine parameters is 94.67% for ultrasonic defect signals at different depths.

Adaptive wavelet transform based on artificial fish swarm optimization and fuzzy C-means method for noisy image segmentation

Aiming at the problem that traditional fuzzy C-means (FCM) clustering algorithm is susceptible to noise in processing noisy images, a noisy image segmentation method based on FCM wavelet domain feature enhancement is proposed. Firstly, the noise image is decomposed by two-dimensional wavelet. Secondly, the edge enhancement of the approximate coefficient is carried out, and the artificial fish swarm (AFS) optimization algorithm is used to process the threshold value of the detail coefficient, and the processed coefficient is reconstructed by wavelet transform. Finally, the reconstructed image is segmented by FCM algorithm. Five typical gray-scale images are selected by adding Gaussian noise and Salt& pepper noise, respectively, and segmented by various methods. The peak signal-to-noise ratio (PSNR) and error rate (MR) of segmented images are used as performance indexes. Experimental results show that compared with traditional FCM clustering algorithm segmentation method, particle swarm optimization (PSO) segmentation method and other methods, the indexes of image segmentation by the proposed method is greatly improved. It can be seen that the proposed segmentation method retains the texture information of image edge well, and its anti-noise performance and segmentation performance are improved.

Assessment of risk factors in medical data using improved Binary Artificial Fish Swarm Algorithm with Classification upon Evaluation from F-Test

The objective of this research work is to effectively deploy improved Binary Artificial Fish Swarm optimization Algorithm (BAFSA) with the data classification techniques. The improvement has been made with accordance to the condition of visual scope and the movement of fish to update towards the central position and chasing behavior towards best point of movement has been modified among the given population. The experimental results show that feature selection by BAFSA and classification by Decision trees and Gaussian Naïve bayes algorithm provides an improved accuracy of about 89.6% for Pima Indian diabetic dataset, 91.1% for lenses dataset and 94.4% for heart disease dataset. Statistical analysis has also been made using Fisher’s F-Test for two sample variance and the selected risk factors such as glucose, insulin level, blood pressure for diabetics datasets, spectacle prescription, tear production rate for lenses dataset and trestbps, cholesterol level, thalach, chest pain type for heart disease dataset are found to be significant with R2<0.001 respectively.

Intelligent Chimp Metaheuristics Optimization with Data Encryption Protocol for WSN

Recent developments in low power electronic devices integrated into wireless communication technologies resulted in the domain of wireless sensor networks (WSN), which finds in applications in diverse data gathering and tracking applications. Since WSN is mostly deployed in harsh and inaccessible environments, it is necessary to design energy efficient and security solutions. The clustering technique is an effective way to lengthen the lifetime of WSN. But most of the clustering techniques elect cluster heads (CHs) irrespective of clusters. To resolve this issue, this paper presents a new intelligent metaheuristics based energy aware clustering with data encryption protocol (IMEAC-DEP) for WSN. The goal of the IMEAC-DEP technique is to elect an appropriate set of CHs and CHs to encrypt the data prior to intercluster communication. The proposed IMEAC-DEP model involves two major phases namely clustering and data encryption. At the first stage, a new chimp optimization algorithm based clustering (COA-C) technique is derived with four fitness parameters. Next, in the second stage, signcryption with artificial fish swarm optimization algorithm (SC-AFSA) based data encryption technique is derived. In order to validate the performance of the proposed IMEAC-DEP model, a series of experiments were performed and the results are investigated under different aspects. The resultant experimental values highlighted the superior performance of the IMEAC-DEP model over the recent stage of art methods interms of energy efficiency as well as security.

Assessment of Risk Factors in Medical Data Using Improved Binary Artificial Fish Swarm Algorithm With Classification Upon Evaluation From F-Test

The objective of this research work is to effectively deploy improved Binary Artificial Fish Swarm optimization Algorithm (BAFSA) with the data classification techniques. The improvement has been made with accordance to the condition of visual scope and the movement of fish to update towards the central position and chasing behavior towards best point of movement has been modified among the given population. The experimental results show that feature selection by BAFSA and classification by Decision trees and Gaussian Naïve bayes algorithm provides an improved accuracy of about 89.6% for Pima Indian diabetic dataset, 91.1% for lenses dataset and 94.4% for heart disease dataset. Statistical analysis has also been made using Fisher’s F-Test for two sample variance and the selected risk factors such as glucose, insulin level, blood pressure for diabetics datasets, spectacle prescription, tear production rate for lenses dataset and trestbps, cholesterol level, thalach, chest pain type for heart disease dataset are found to be significant with R2<0.001 respectively.

A Holistic Performance Comparison for Lung Cancer Classification Using Swarm Intelligence Techniques.

In the field of bioinformatics, feature selection in classification of cancer is a primary area of research and utilized to select the most informative genes from thousands of genes in the microarray. Microarray data is generally noisy, is highly redundant, and has an extremely asymmetric dimensionality, as the majority of the genes present here are believed to be uninformative. The paper adopts a methodology of classification of high dimensional lung cancer microarray data utilizing feature selection and optimization techniques. The methodology is divided into two stages; firstly, the ranking of each gene is done based on the standard gene selection techniques like Information Gain, Relief–F test, Chi-square statistic, and T-statistic test. As a result, the gathering of top scored genes is assimilated, and a new feature subset is obtained. In the second stage, the new feature subset is further optimized by using swarm intelligence techniques like Grasshopper Optimization (GO), Moth Flame Optimization (MFO), Bacterial Foraging Optimization (BFO), Krill Herd Optimization (KHO), and Artificial Fish Swarm Optimization (AFSO), and finally, an optimized subset is utilized. The selected genes are used for classification, and the classifiers used here are Naïve Bayesian Classifier (NBC), Decision Trees (DT), Support Vector Machines (SVM), and K-Nearest Neighbour (KNN). The best results are shown when Relief-F test is computed with AFSO and classified with Decision Trees classifier for hundred genes, and the highest classification accuracy of 99.10% is obtained.

Bingham deep neural and oppositional fish swarm optimized protein structure prediction

It is familiar that essential proteins take part in managing cellular activities in living organisms. Moreover, protein structure prediction from its amino acid sequence is advantageous to the comprehending of cellular functions. Formerly, several essential protein prediction methods have been proposed. However, those existing prediction methods were not satisfactory because to low sensitivity to imbalance characteristics. To address this issue, this paper presents a novel secondary protein structure prediction method, called, Bingham Deep Convolutional-based Oppositional Artificial Fish Optimized (BDC-OAFO). First, a protein structure identification framework, called, Bingham Distributed Deep Convolutional (BDDC) is designed to identify the essential proteins by eliminating the imbalanced learning issue. Next, secondary structure prediction framework, called, Oppositional Artificial Fish Swarm Optimization is proposed that obtain precise prediction results. Then, predicting secondary protein structure by emulating three biological behaviors of artificial fishes, including foraging behavior, following behavior, swarming behavior in which process, proximal count, oppositional function and Gaussian function are utilized. To evaluate the performance of BDC-OAFO method, we conduct experiments on Protein Data Bank dataset the experimental results show that our method BDC-OAFO achieves a better performance for identifying essential proteins and precise prediction in comparison with several other well-known prediction methods, which confirms the significance of BDC-OAFO. Communicated by Ramaswamy H. Sarma

Quantile Regressive Fish Swarm Optimized Deep Convolutional Neural Learning for Reliable Data Transmission in IoV

Route path identification on the Internet of Vehicles (IoV) is complicated due to the nature of high dynamic mobility, bandwidth constraints, and traffic load. A vehicle present on the IoV communicates with each other to find the status of the road and location of other vehicles for reliable data transmission. However, the existing routing algorithm does not effectively improve the packet delivery ratio and reduce the delay. To resolve these issues, A Quantile Regressive Fish Swarm Optimized Deep Convolutional Neural Learning (QRFSODCNL) technique is introduced reliable data transmission with minimum end to end delay in IoV. The Do Convolutional Neural Learning uses multiple layers such as one input layer, three hidden layers, and one output layer for vehicle location identification and optimal route path discovery. The different node characteristics of vehicle nodes are analyzed in the hidden layers using the quantile regression function. Depends on the regression analysis, the neighbouring node is identified with minimal time. To improve the throughput and reduce the packet loss rate, the artificial fish swarm optimization technique is applied to choose the best route among the population based on the fitness function. Simulation is carried out to analyze the performance of QRFSODCNL technique and existing methods with different metrics such as packet delivery ratio, packet loss rate, average end to end delay, and throughput. The discussed outcome proves that the QRFSODCNL technique outperforms well as compared to the stateof-the-art methods.

Intelligent Machine Learning With Evolutionary Algorithm Based Short Term Load Forecasting in Power Systems

Electricity demand forecasting remains a challenging issue for power system scheduling at varying stages of energy sectors. Short Term load forecasting (STLF) plays a vital part in regulated power systems and electricity markets, which is commonly employed to predict the outcomes power failures. This paper presents an intelligent machine learning with evolutionary algorithm based STLF model, called (IMLEA-STLF) for power systems which involves different stages of operations such as data decomposition, data preprocessing, feature selection, prediction, and parameter tuning. Wavelet transform (WT) is used for the decomposition of the time series and Oppositional Artificial Fish Swarm Optimization algorithm (OAFSA) based feature selection technique to elect an optimal set of features. In order to improvise the convergence rate of AFSA, oppositional based learning (OBL) concept is integrated into it. Then, the water wave optimization (WWO) with Elman neural networks (ENN) model is employed for the predictive process. Finally, inverse WT is applied and obtained the hourly load forecasting data. To validate the effective predictive outcome of the IMLEA-STLF model, an extensive set of simulations take place on benchmark dataset. The resultant values ensured the promising results of the IMLEA-STLF model over the other compared methods.

Maximizing Throughput in Wireless Multimedia Sensor Network using Soft Computing Techniques

Wireless Multimedia Sensor Networks (WMSN) provides valuable information for scalar data, images, audio, and video processing in monitoring and surveillance applications. Multimedia streaming, however, is highly challenging for networks as energy restriction sensor nodes limit the potential data transmission bandwidth and lead to reduced throughput. WMSN’s two key design challenges, which can be achieved by the clustering process, are energy efficiency and throughput maximization. The use of the clustering technique helps to organise the sensor nodes into clusters, and between each cluster a cluster head (CH) will be chosen. This paper introduces a new Artificial Fish Swarm Optimization Algorithm (AFSA) with a Clustering Technique for Throughput Maximization in WMSN, called AFSA-HC, based on Hill Climbing (HC). The proposed AFSA-HC algorithm includes four key processes to optimise network throughput, namely node initialization, node clustering based on AFSA-HC, data aggregation based on the deflate algorithm, and transmission of hybrid data. To check the adequate performance of the presented AFSA-HC technique, a thorough experimental review will be carried out. The results of the simulation showed that the AFSA-HC approach achieved optimum results for various steps, namely energy consumption, throughput, network life, network stability and packet loss.

Review on Prediction Methods of Remaining Useful Life of Lithium Ion Batteries

Compared with other types of ion batteries, lithium-ion batteries have great advantages in specific capacity, self-discharge rate, performance and price. Therefore, lithium-ion batteries have developed rapidly and are widely used in aerospace, portable equipment and so on.The rul prediction method of lithium-ion battery is developing from experimental reliability to practical reliability, expanding the application scope of the prediction method and ensuring reliable ex ante maintenance, which can not only ensure the safety of personnel, property and equipment in civil use. There are two main methods for rul prediction of lithium-ion battery: one is to build a capacity decline model based on the internal electrochemical mechanism of the battery, Due to the complex internal chemical mechanism of the battery, the differences between the same batch of batteries lead to its application limitations; The second is the data-driven method. Through the extraction of parameters in the process of battery operation, such as voltage and current, support vector machine, artificial neural network and other methods are used for prediction. From the prediction results, the data-driven method has high precision and wide application. It is the mainstream research method at present.It is of great significance to accurately determine the health status of lithium-ion batteries. To address the problem that the prediction of a single limit learning machine algorithm is prone to jumping, the method of using artificial fish swarm optimization to optimize the limit learning machine is proposed to try its best to predict the model of the remaining life of lithium-ion batteries. Firstly, the isovoltage discharge time is extracted as an indirect health factor, then the limit learning machine is optimised using the artificial fish swarm algorithm to build an indirect prediction model for the remaining life of Li-ion batteries, and finally a validation evaluation is carried out based on the NASA dataset B0005-B0006. The experimental results show that the proposed model predicts stable prediction results with high accuracy and small error in prediction results.