Artificial Fish Swarm Optimization Algorithm 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.

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.

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.

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.

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 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.

Artificial fish swarm optimization algorithm for power system state estimation

In this paper, the power system state estimation (SE) problem is formulated as a general non-linear programming problem with equality constraints and boundary limits on the state variables. The proposed SE problem is solved using an evolutionary based Artificial Fish Swarm Optimization Algorithm (AFSOA). The AFSOA is a global search algorithm based on the characteristics of fish swarm and its autonomous model. The detailed algorithm with its flow chart is presented in this paper. To show the effectiveness of the proposed SE approach, six bus test system is considered. The obtained results are compared with other algorithms reported in the literature.

A microgrid scheduling method based on Evolutionary Game

In order to solve constrained scheduling problem in microgrid. A micrgrid scheduling method based on evolutionary game is proposed in this paper. Further more, a renewable energy and traditional energy generators group with different cost functions was studied as an example. The results show that evolutionary game method proposed in this paper has higher accuracy and stability than the existing artificial fish swarm optimization algorithm (AFS). In the scheduling model of microgrid, the optimized cost of power supply has been reduced by 2%.

Modeling of Optimal Adaptive Weighted Clustering Protocol for Vehicular Ad hoc Networks

Vehicular ad hoc network (VANET) is a mobile adhoc network widely used in intelligent transportation systems (ITS). Owing to the unique features of VANET like self-organized, recurrent link interruptions, and quick topology modifications, the design of an effective clustering protocol is a challenging problem. The clustering process is considered an optimization problem and can be solved using metaheuristic algorithms. Therefore, this paper presents an adaptive weighted clustering protocol with artificial fish swarm optimization (AWCP-AFSO) algorithm for VANET. The proposed AWCP-AFSO technique aims to select the CHs effectively and thereby accomplishes energy efficiency. To construct clusters, the AWCP-AFSO algorithm derives an objective function from electing an optimal set of CHs. A wide range of simulations are performed, and the results are investigated in terms of several performance measures. The experimental values showcased the betterment of the AWCP-AFSO technique over the recent techniques.

Optimal location and sizing of DG and D-STATCOM in distribution networks

<p>The main aim of this paper is to determine optimal locations and sizing of Distributed Generations (DGs) and Distribution STATic COMpensator (D-STATCOM) in the distribution network for reducing the system losses and to improve the voltage profile. In this paper, the loss sensitivity factor approach is used to determine optimal location for DG and voltage stability index is used to determine optimal location for D-STATCOM. The objective of proposed optimization problem is to minimize the total real power losses in the system while satisfying several equality and inequality constraints. Artificial fish swarm optimization algorithm (AFSOA) is used to determine the optimal size of DG and D-STATCOM. The simulations are performed on standard 33 bus and 69 bus radial distribution systems and the obtained  results show the feasibility and effectiveness of proposed approach.</p>

An effective artificial fish swarm optimization algorithm for two-sided assembly line balancing problems

Two-sided assembly lines are often used in assembly of large-sized products, such as automobiles, buses and trucks. Compared to the traditional one-sided assembly line, two-sided assembly line has advantages of shorter line and higher utilization of fixture. However, normal balancing method is not applicable to solve the two-sided assembly line balancing problem since the constraint conditions become more complicated. On the other hand, artificial fish swarm algorithm is a relatively new member of swarm intelligence based on swarm behaviors that were inspired from social behaviors of fish swarm in nature. As a typical application of behaviorism in artificial intelligence, artificial fish swarm algorithm can search for the global optimum. So it is a good candidate for developing new search algorithm for solving optimization problems in operational research. In this research, an effective discrete artificial fish swarm algorithm is developed to solve the cost-oriented assembly line balancing problems which aims to minimize the construction cost and at the same time minimize the number of mate-station. Through extensive computational experiments, the performance of the proposed artificial fish swarm algorithm is examined. The experimental results validate the effectiveness and efficiency of the proposed method.

Enhanced Smart Meter Privacy Protection Using Rechargeable Batteries

Due to the rapid growth of smart grids, use of smart meters (SMs) have increased in the recent days. The main problem with the use of SMs is that by observing the SMs reading, it is possible to infer the daily activities of the consumers. Therefore, protection of privacy is a major concern related to SMs. Using rechargeable batteries (RBs) is a popular method in protecting the privacy in SMs as these methods do not tamper with SM readings. The major problem in RB-based mechanism is that the energy management unit (EMU) cannot protect privacy, if the demand is lower or higher for a longer period. To overcome this problem, in this paper a heuristic method has been proposed by considering time varying target output load based on the three major properties of artificial fish swarm optimization algorithm. For the optimal choice of the time varying target output load, RB constraints as well as reduction of the average cost of energy have been considered in our proposed method. We have proposed two privacy preserving mechanisms for both offline and online scenarios. The proposed method preserves privacy while reducing the cost of energy. Simulation results show that the proposed method is able to provide privacy by overcoming the problem identified in the existing methods.

Support Vector Machine Optimized Using the Improved Fish Swarm Optimization Algorithm and Its Application to Face Recognition

Support vector machine (SVM) is always used for face recognition. However, kernel function selection is a key problem for SVM. This paper tries to make some contributions to this problem with focus on optimizing the parameters in the selected kernel function to improve the accuracy of classification and recognition of SVM. Firstly, an improved artificial fish swarm optimization algorithm (IAFSA) is proposed to optimize the parameters in SVM. In the improved version of artificial fish swarm optimization algorithm, the visual distance and the step size of artificial fish are adjusted adaptively. In the early stage of convergence, artificial fish are widely distributed, and the visual distance and step size take larger values to accelerate the convergence of the algorithm. In the later stage of convergence, artificial fish gathered gradually, and the visual distance and the step size were given small values to prevent oscillation. Then the optimized SVM is used to recognize face images. Simultaneously, in order to improve the accuracy rate of face recognition, an improved local binary pattern (ILBP) is proposed to extract features of face images. Numerical results show the advantage of our new algorithm over a range of existing algorithms.

Image segmentation based on equivalent three-dimensional entropy method and artificial fish swarm optimization algorithm

To improve the timeliness of the three-dimensional (3-D) maximum entropy method, an image segmentation method based on equivalent 3-D entropy and artificial fish swarm optimization algorithm is proposed. An equivalent 3-D entropy method without logarithmic operation is developed, and its equivalence is proved theoretically. The optimal threshold is determined based on the artificial fish swarm optimization algorithm so as to avoid exhaustive search and improve algorithm efficiency. The experimental results demonstrate that the proposed method is more time-efficient than the traditional 3-D entropy method and the equivalent 3-D entropy method without affecting segmentation. Compared with the one-dimensional entropy method and the two-dimensional entropy method, it is obviously superior in noise immunity and detail preservation.

Solving non-convex economic dispatch with prohibited zones using artificial fish swarm optimization

In this paper, a novel approach is proposed to solve the non-convex and discontinuous economic dispatch (ED) problem of power system with thermal power plants. All the practical constraints (loss constraint, generators ramp rate constraints and network constraints) are considered for solving the ED problem. Here, the proposed ED problem is solved by considering the generators with valve point loading (VPL) effects and prohibited operating zones (POZs) effects. In this paper, to solve this practical ED problem, an evolutionary based Artificial Fish Swarm Optimization Algorithm (AFSOA) is utilized. The AFSOA is a global search algorithm based on the characteristics of fish swarm and its autonomous model. The detailed algorithm with its flow chart is presented in this paper. To show the effectiveness of the proposed ED approach, 3 test systems (3, 6 and 20 generating unit systems) are considered. The obtained results are compared with other algorithms reported in the literature.

Hybrid ANN optimized artificial fish swarm algorithm based classifier for classification of suspicious lesions in breast DCE-MRI

Abstract Automatic mass or lesion classification systems are developed to aid in distinguishing between malignant and benign lesions present in the breast DCE-MR images, the systems need to improve both the sensitivity and specificity of DCE-MR image interpretation in order to be successful for clinical use. A new classifier (a set of features together with a classification method) based on artificial neural networks trained using artificial fish swarm optimization (AFSO) algorithm is proposed in this paper. The basic idea behind the proposed classifier is to use AFSO algorithm for searching the best combination of synaptic weights for the neural network. An optimal set of features based on the statistical textural features is presented. The investigational outcomes of the proposed suspicious lesion classifier algorithm therefore confirm that the resulting classifier performs better than other such classifiers reported in the literature. Therefore this classifier demonstrates that the improvement in both the sensitivity and specificity are possible through automated image analysis.

Automatic segmentation of lesion from breast DCE-MR image using artificial fish swarm optimization algorithm

Abstract Interpreting Dynamic Contrast-Enhanced (DCE) MR images for signs of breast cancer is time consuming and complex, since the amount of data that needs to be examined by a radiologist in breast DCE-MRI to locate suspicious lesions is huge. Misclassifications can arise from either overlooking a suspicious region or from incorrectly interpreting a suspicious region. The segmentation of breast DCE-MRI for suspicious lesions in detection is thus attractive, because it drastically decreases the amount of data that needs to be examined. The new segmentation method for detection of suspicious lesions in DCE-MRI of the breast tissues is based on artificial fishes swarm clustering algorithm is presented in this paper. Artificial fish swarm optimization algorithm is a swarm intelligence algorithm, which performs a search based on population and neighborhood search combined with random search. The major criteria for segmentation are based on the image voxel values and the parameters of an empirical parametric model of segmentation algorithms. The experimental results show considerable impact on the performance of the segmentation algorithm, which can assist the physician with the task of locating suspicious regions at minimal time.