Ant Colony Optimization Technique Research Articles

Priority Based Train Scheduling Method Using ACO in Indian Railway Perspective

The aim of this paper is to solve a train scheduling problem (TSP) with different train priorities and travelling at different speeds in the Indian railways (IR) perspective. Initially, a mathematical model for a TSP is developed and then the Ant Colony Optimization (ACO) technique is applied to resolve the problem. A real-world train timetable problem is presented to make a comparison between the manual solutions method used by the Indian Railway (IR) and the solution obtained from ACO and CPLEX solver to check for quality and accuracy in terms of total travel time. The result shows that there is a 6.827% improvement in travel time using ACO over manual solution. A collision free train schedule is determined based on the obtained train sequence.

Parallel distribution compensation PID based on Takagi-Sugeno fuzzy model applied on egyptian load frequency control

This paper presents a new technique for a Takagi-Sugeno (TS) fuzzy parallels distribution compensation-PID'S (TSF-PDC-PID'S) to improve the performance of Egyptian load frequency control (ELFC). In this technique, the inputs to a TS Fuzzy model are the parameters of the change of operating points. The TS Fuzzy model can definite the suitable PID control for a certain operating point. The parameters of PID'S controllers are obtained by ant colony optimization (ACO) technique in each operating point based on an effective cost function. The system controlled by the proposed TSF-PDC-PID’S is investigated under different types of disturbances, uncertainty and parameters variations. The simulation results ensure that the TSF-PDC-PID'S can update the suitable PID controller at several operating points so, it has a good dynamic response under many types of disturbances compared to fixed Optimal PID controller.

Black Hole Attack Prevention in Mobile Ad-hoc Network (MANET) using Ant Colony Optimization Technique

The Mobile ad-hoc networks are auto configured systems where hubs can travel towards any direction. It doesn’t rely on any other frame to build the framework. Because of its versatility, the black hole is a real security issue to be settled. It occurs once a malignant hub referred to as black hole goes into the framework. Black hole center point exhibits its fake lead among the methodology after all disclosure. Currently, varied strategies are made arrangements for Mobile ad-hoc networks. Regardless, at interims seeing Black hole or fake hubs, the entire Mobile ad-hoc frameworks are exposed to various sorts of assaults or network attacks. Among these issues, a black hole center points plugs itself of getting a most nearest targeted hub, whose data packet must be constrained to drop. In this flooding procedures, if the communication from the genuine hub accomplishes later than the fake hub reply as requested by the standard hub. A false communication path is framed through a fake center hub. A perfect method is one in which the package reach to target with less delay and lesser overhead. In this paper we'll talk about the implementation of Ant Colony Optimization Technique and Repetitive Route Configuration with Reactive Routing Protocol for obstruction of black hole Attack in Mobile ad-hoc networks.

An Evaluation of Effective Intrusion DoS Detection and Prevention System Based on SVM Classifier for WSN

The trending technology of Wireless Sensor Networks (WSNs) are broadly used in this recent earth for a greatest rate of purpose for the better worth of person living however owing to their energy constrains as well as utilization and problems as mentioned above, these WSN has their restriction in their intention as well as their practicality. The wireless sensor networks face main threat challenges such as Denial of Service (DoS) attack, flooding attack, replay attack. In this approach, we proposed an Intrusion Detection (ID) technique using Support Vector Machine (SVM). In the ID algorithm, we employ feature vector to constitute nodes. Such components consist of routing message number, varies destinations in sending and receiving routing packets, and similar source node. The similar source node consists of similar characteristics; the node of the similar category has the same features. Therefore, irregular nodes will be notable. The measurement result shows an introduced scheme attain higher rate of accuracy. Here, Ant colony Optimization (ACO) technique to choose best route between entire adjacent feasible routes from source to receiver node for data communication. The proposed approach enhances the network lifespan as well as discovers intrusion nodes in WSN.

Fault-type coverage based ant colony optimization algorithm for attaining smaller test suite

<table width="0" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="593"><p>In this paper, we proposed Fault-Type Coverage Based Ant Colony Optimization (FTCBACO) technique for test suite optimization. An algorithm starts with initialization of FTCBACO factors using test cases in test suite. Then, assign separate ant to each test case called vertex. Each ant chooses best vertices to attain food source called objective of the problem by means of updating of pheromone trails and higher probability trails. This procedure is repeated up to the ant reaches food source. In FTCBACO algorithm, minimal number of test cases with less execution time chosen by an ant to cover all faults type (objective) are taken as optimal solution. We measured the performance of FTCBACO against Greedy approach and Additional Greedy Approach in terms of fault type coverage, test suite size and execution time. However, the heuristic Greedy approach and Additional Greedy approach required more execution time and maximum test suite size to provide the best resolution for test suite optimization problem. Statistical investigations are performed to finalize the performance significance of FTCBACO with other approaches that concludes FTCBACO technique enriches the reduction rate of test suite and minimizes execution time of reducing test cases efficiently.</p></td></tr></tbody></table>

Energy Efficient Cluster based Data Aggregation with Ant Colony Optimization Technique Using Wireless Sensor Network

Energy Efficient Cluster based Data Aggregation with Ant Colony Optimization Technique Using Wireless Sensor Network

SPEED CONTROL OF DC MOTOR USING ACO BASED FOPID CONTROLLER

Direct current (DC) motors are widely used because of their easily controllable and reliable access. DC motors have many salient features that includes a wide range of torque speed control, reliable operation with greater efficiency and a greater starting torque and many more. A nonlinear model for a DC motor can be represented for considering all uncertainties and non-linearities. In this paper, DC motor is studied which is having a second order system. Many controllers are being used for controlling speed of DC motor. An analysis is carried out on mathematical model of DC motor whose field is excited by an external or separate supply. Tuning of FOPID parameters using Ant Colony Optimization Technique (ACO) is discussed here.

Performance Optimization of Brushless DC Motors for the Mitigation of Voltage Dips, Swells and Short Interruptions in Weak Grids

Normally, the BLDC motor drives a sensitive load which is severely influenced by such disturbances. The main aim of this work is to investigate the performance of BLDC motors when they are tied to weak grids. Thus, this paper presents a study on the performance of the BLDC motor under disturbed utility conditions and implements an efficient solution in order to guarantee a soft operation in the case that a BLDC drives a sensitive load. A buck-boost DC converter is merged with the BLDC drive inverter in order to be able to mitigate voltage dips as well as voltage swells. A short interruption of one or two phases of the grid voltage can also be mitigated. The BLDC motor is controlled via cascade PI-controller layout that has an outer loop for the speed control and inner loop for the DC voltage control. The PI controllers’ gains are optimized by using the ant-colony optimization technique and then implemented in a simulation model using MATLAB/SIMULINK. Six cases of disturbances have been simulated including balanced and unbalanced dips, swells and short interruption of one or two phases of the grid. Simulation results with various disturbances show the ability of the proposed controller to mitigate the grid disturbances with good transient and steady-state responses.

Epileptic Detection from the Eeg Signal using the Anti colony Optimization Technique with Deep Neural Network

Extensively used technique to diagnose the epilepsy is EEG. The research objective is to check the variations of frequency found in the epileptic EEG signals.. The EEG dataset were acquired from online database of the Bonn University (BU). Then, butterworth type two filter was implemented to remove the unwanted artifacts from the acquired EEG signals. Further, Multivariate Variational Mode Decomposition (MVMD) methodology was applied to decompose the denoised EEG signals. The signal decomposition helps in finding the necessary information, which required to model the complex time series data. Then, the features were extracted from decomposed signals by using fifteen entropy, linear and statistical features. In addition, ant colony optimization technique was proposed for optimizing the extracted features. The optimized feature vectors were classified by Deep Neural Network (DNN) that includes two circumstances (seizure and healthy), and (Interictal, ictal, and normal). The accuracy attained using the ant colony with deep neural network is 98.12% using the BU EEG dataset, respectively related to the existing models.

Epileptic Detection from the Eeg Signal using the Anti colony Optimization Technique with Deep Neural Network

Extensively used technique to diagnose the epilepsy is EEG. The research objective is to check the variations of frequency found in the epileptic EEG signals.. The EEG dataset were acquired from online database of the Bonn University (BU). Then, butterworth type two filter was implemented to remove the unwanted artifacts from the acquired EEG signals. Further, Multivariate Variational Mode Decomposition (MVMD) methodology was applied to decompose the denoised EEG signals. The signal decomposition helps in finding the necessary information, which required to model the complex time series data. Then, the features were extracted from decomposed signals by using fifteen entropy, linear and statistical features. In addition, ant colony optimization technique was proposed for optimizing the extracted features. The optimized feature vectors were classified by Deep Neural Network (DNN) that includes two circumstances (seizure and healthy), and (Interictal, ictal, and normal). The accuracy attained using the ant colony with deep neural network is 98.12% using the BU EEG dataset, respectively related to the existing models.

Optimal path search and control of mobile robot using hybridized sine-cosine algorithm and ant colony optimization technique

Purpose This paper aims to incorporate a hybridized advanced sine-cosine algorithm (ASCA) and advanced ant colony optimization (AACO) technique for optimal path search with control over multiple mobile robots in static and dynamic unknown environments. Design/methodology/approach The controller for ASCA and AACO is designed and implemented through MATLAB simulation coupled with real-time experiments in various environments. Whenever the sensors detect obstacles, ASCA is applied to find their global best positions within the sensing range, following which AACO is activated to choose the next stand-point. This is how the robot travels to the specified target point. Findings Navigational analysis is carried out by implementing the technique developed here using single and multiple mobile robots. Its efficiency is authenticated through the comparison between simulation and experimental results. Further, the proposed technique is found to be more efficient when compared with existing methodologies. Significant improvements of about 10.21 per cent in path length are achieved along with better control over these. Originality/value Systematic presentation of the proposed technique attracts a wide readership among researchers where AI technique is the application criteria.

A Hierarchical Approach Based on ACO and PSO by Neighborhood Operators for TSPs Solution

It is known that some of the algorithms in optimization field have originated from inspiration from animal behaviors in nature. Natural phenomena such as searching behavior of ants for food in a collective way, movements of birds and fish groups as swarms provided the inspiration for solutions of optimization problems. Traveling Salesman Problem (TSP), a classical problem of combinatorial optimization, has implementations in planning, scheduling and various scientific and engineering fields. Ant colony optimization (ACO) and Particle swarm optimization (PSO) techniques have been commonly used for TSP solutions. The aim of this paper is to propose a new hierarchical ACO- and PSO-based method for TSP solutions. Enhancing neighboring operators were used to achieve better results by hierarchical method. The performance of the proposed system was tested in experiments for selected TSPLIB benchmarks. It was shown that usage of ACO and PSO methods in hierarchical structure with neighboring operators resulted in better results than standard algorithms of ACO and PSO and hierarchical methods in literature.

Nature-inspired algorithm-based secure data dissemination framework for smart city networks

Unceasing population growth and urbanization have intensified the traditional systems to deal with citizen lifestyle, environment, economic issues and good governess. New communication technologies have played a vital role in changes traditional urbanization into a smarter and comfort zone for the citizen. Due to various systems and integration of several new standards and systems, the smart cities have suffered from various open challenges related to technologies, system controlling and management, scalability and security concerns. The new concepts of nature-inspired solutions have implemented to deal with smart cities’ challenges by more optimization and performance-oriented methods. Therefore, this paper aims to handle at least three areas of smart cities including smart mobility, smart living and security provision by developing three nature-inspired solutions. The three proposed solutions are dragon clustering mobility in IoV, moth flame electric management for smart living and ant colony-based intrusion detection system for security provision. These solutions are based on a dragonfly, moth flame and ant colony optimization techniques. The proposed solutions are evaluated in a simulation to check the performance. These solutions will help new researchers to explore the nature-inspired solutions to tackle the new and complex systems of smart cities.

PID Controller Tuning using ACO Algorithm for AVR Systems

This paper features an effective technique to device the parameters of PID controllers for utilization together with an Automatic Voltage Regulator System (AVR). The quintessential goal is to acquire a good load disturbance response by minimizing the performance index/(Integral time square error). Simultaneously, the transient response is assured by limiting maximum overshoot, settling time and rise time of the step response to minimal values. For achieving these goals, optimum and quick tuning of the parameters (Kp, Ki, and Kd) is essential. In an effort to accomplish the aforementioned, the paper put forth an algorithm developed based on the Ant Colony Optimization technique (ACO) to decide optimal gains of PID controller and for getting optimal performance within an AVR system. Simulation results establish superior control response may be accomplished in comparison with methods like conventional tuning method (trial and error) and built-in genetic algorithm (GA) took-kit.e.

A Deterministic Flowshop Scheduling Problem to minimizing the Makespan using PA ACO

Scheduling problems are NP-hard in nature. Flowshop scheduling problems, are consist of sets of machines with number of resources. It matins the continuous flow of task with minimum time. There are various traditional algorithms to maintain the order of resources. Here, in this paper a new stochastic Ant Colony optimization technique based on Pareto optimal (PA-ACO) is implemented for solving the permutation flowshop scheduling problem (PFSP) sets. The proposed technique is employed with a novel local path search technique for initializing and pheromone trails. Pareto optimal mechanism is used to select the best optimal path solution form generated solution sets. A comparative study of the results obtained from simulations shows that the proposed PA-ACO provides minimum makespan and computational time for the Taillard dataset. This work will applied on large scale manufacturing production problem for efficient energy utilization.

Optimum management of manual sectionalizers in electric power distribution networks integrating distributed generations using binary exchange market algorithm

Purpose The use of power sectionalizers in electric power distribution networks as disconnecting devices for optimum network configuration is indispensable. Major reasons to use sectionalizers, here manual sectionalizers, is their lower installation and operating prices compared to other types of disconnecting devices and that most of conventional realistic electric power distribution systems are still using manual sectionalizers due to their ease of procurement. However, in case of failure for these switches, power supply interruptions are unavoidable unless optimum solutions are used for configuration (and possibly reconfiguration) of sectionalizers. Thus, in this research, binary exchange market algorithm (BEMA) as a novel evolutionary metaheuristic is used to meet the maximized customer satisfaction by optimized configuration of sectionalizers within electric power distribution networks in the presence of distributed generations (DGs). To solve the problem, BEMA is used on sectionalizing switch placement problem, which has only two open and close (0/1) states. A novel multi-objective optimization problem has been formulated as a function of two aspects, namely, improved reliability index (for customer benefit) and minimized sectionalizing switch costs (for utility benefits). Simulations are carried out in three different case studies to validate the effectiveness of the BEMA both in theory and practice: Standard IEEE 33-bus test system, practical feeder-8 of MeshkinShahr Town’s electric power distribution network in northwest of Iran; and Roy Billinton test system Bus 4 (RBTS-Bus 4). The obtained results are compared with those of the previously validated ant colony optimization (ACO) technique in RBTS-Bus 4. Design/methodology/approach The optimum configuration of sectionalizers in the presence of DGs has been formulated as a multi-objective function consisting of two conflicting objectives. First objective is to improve the power distribution network reliability indices. Second objective is to fulfill the first objective with a minimized sectionalizing switch cost. The latter is probably obtained by reducing the number of installed sectionalizers. The obtained results by BEMA have been compared with those of ACO technique. Findings In this paper, optimal configuration of sectionalizers has been performed based on a multiobjective function by binary exchange market algorithm. By simulations carried out on two standards and one practical test systems, the proposed algorithm effectiveness was confirmed and the obtained results were compared to ACO algorithm. Changing weighting factors shows that better satisfaction can be obtained when difference between the weighting factors is relatively greater. In other words, the reliability membership function is more than switch cost membership, and thus, if the values of two weighting factors are close enough, the satisfaction level reduces. The number of installed sectionalizing switches by BEMA and ACO techniques in different scenarios were performed. Originality/value Proposal of a novel multi-objective function for finding optimal location of sectionalizers in the presence of DGs with binary exchange market algorithm whose merit over the other heuristics is to consider all the problem specifications only in one multi-objective function. Despite previously reported works that have used various high-priced protective devices for achieving the enhanced reliability this research only utilizes inexpensive manual sectionalizers with the least possible cost in the presence of DGs. Two standard test cases IEEE 33-bus test system and RBTS-Bus 4 and one realistic test case feeder-8 of MeshkinShahr Town power distribution network in northwest of Iran are used to demonstrate the effectiveness of the proposed technique in theory and real-world applications. Thus, utilities may take the advantage of the proposed method for configuration of sectionalizers in their own local power distribution systems throughout the country.

Application of Metaheuristic Optimization Algorithm and 3D Printing Technique in 3D Bandpass Frequency Selective Structure

A 3D band pass frequency selective surface structure with desired center frequency and bandwidth, designed by multi-objective optimization algorithms, is introduced in this paper. The ant colony optimization technique is used to achieve optimal design properties with the required characteristics. The performance of the manufactured structure using the 3D printing method is measured to verify the optimized structure for normal incidence. Come up with the results, the convenience of the optimization algorithm accordingly 3D printing can help in designing and constructing complex 3D structures.

Segmentation of Magnetic Resonance Brain Images Using the Advanced Ant Colony Optimization Technique

MR Brain Image Segmentation is an important step in brain image analysis. It facilitates the automatic interpretation or diagnosis that helps in surgical planning, estimating the changes in the brain’s volume for various types of tissues, and recognizing different neural disorders. Many neurological disorders like epilepsy, Alzheimer’s, tumor, and cancer can be effectively quantified and analyzed by finding the volume of the brain tissues such as White Matter (WM), Gray Matter (GM), and Cerebro Spinal Fluids (CSF). In manual segmentation of brain MRIs physicians manually determines the boundaries of different objects of interest and it is time-consuming and difficult. Thus, several accurate automatic brain MRI segmentation techniques with different levels of complexity have been proposed. This paper proposes an advanced thresholding technique for the segmentation of brain MRIs based on the biologically inspired Ant Colony Optimization (ACO) algorithm. Here the texture features are assumed as heuristic data. The experimental results for the T1-weighted brain MRIs have shown high accuracy than the conventional such as Fuzzy C-Means (FCM), Expectation-Maximization (EM), Improved Bacterial Foraging Algorithm (IBFA), and Improved Particle Swarm Optimization (IPSO).

Non‐redundant frame identification and keyframe selection in DWT‐PCA domain for authentication of video

This study is intended to protect video data and watermark from unauthorised access. The proposed methodology accentuates two new algorithms, namely structural similarity index metric–absolute difference metric (SSIM-AMD) based non-redundant frame identification (NRFI) and entropy–AMD based keyframe selection (KFS) to reduce the challenges posed by traditional discrete wavelet transform–singular value decomposition. Traditional techniques embed the entire watermark to all existing frames in the video, which is cumbersome and time-consuming. In this methodology, NRFI algorithm is applied to segregate the redundant and non-redundant frames to specific database. The KFS algorithm is used to identify suitable keyframes. DWT is applied into keyframes, which decomposes the frames into subbands. The middle band is selected for embedding. The principal component of watermark image block is embedded into identified keyframes in the video. The chaotic map is adapted to reorder the watermark block for improving the authentication level of the watermarking. The ant colony optimization (ACO) technique is adapted to select the suitable scaling factor for watermarking process. The principal component analysis technique is employed for avoiding false-positive attacks. Experimental results show the proposed methodology can withstand image processing, video processing, false-positive attacks and produces good results in terms of perceptual quality and robustness.

COMPREHENSIVE REVIEW ON HEART DISEASE PREDICTION USING OPTIMIZATION TECHNIQUES

Heart disease is one of the main sources of demise around the world. It is imperative to predict the disease at a premature phase. The computer aided systems help the doctor as a tool for predicting and diagnosing heart attack. This research mainly focuses on the study of women heart attack prediction are analysed. Heart disease is the leading cause of death for both men and women. The early symptoms of heart attack can be quite different from men & women. It is a common belief that women are better at looking their health than men. But when it comes to heart health, research shows that many women don’t and often put the needs of others before themselves. In this study we studied various available techniques and diagnosis of heart attack disease with a sophisticated approach to data extraction, the Ant Colony Optimization technique. KEYWORDS: data mining, Heart disease, Ant colony optimization, pheromone