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In this paper, an adaptive controller is presented to control a quadrotor, whose parameters are extracted from the genetic algorithm optimization method. The advantage of this method is that based on the system states, the control coefficients are calculated online. For this purpose, a function between system states-space and control coefficients is obtained. From the database collected from the genetic algorithm optimization method, the parameters of the control coefficient function are obtained using the least squares method. The stability of the proposed controller is proved by the Lyapunov method. Finally, the performance of the proposed controller is compared with the PID controller, which is widely used in the literature. The results show that the proposed approach is promising. [ABSTRACT FROM AUTHOR]

In this paper, a trajectory planning problem based on high-order polynomials is formulated for a point-to-point motion. The problem aims to find suitable polynomial trajectories that connect an initial to a final configuration while satisfying other specified constraints. The constraints are considered as zero velocity at the endpoint as well as a limitation on acceleration for the whole motion time. However, this problem is very difficult to trace more particularly when the number of coefficients (decision-making variables) is large. As a new approach, a high-order polynomial equation containing only two-term is proposed to generate suitable trajectories between two configurations. The advantage of the proposed polynomial is that it can be traced analytically in order to get solutions for the two independent coefficients in a closed-form. The motion simulations show that the resulting high-degree trajectories with two-term polynomial satisfy the mentioned constraints as well as they are continuous and smooth. Additionally, comparing outputs of Genetic Algorithm with the closed-form solutions for the problem show that closed-form expressions generate coefficients that are near optimal. [ABSTRACT FROM AUTHOR]

Optimization algorithms inspired by nature as intelligent optimization methods with classical methods have demonstrated significant success. Some of these techniques are genetic algorithms, inspired by biological evolution of humans and other creatures) ant colony optimization and simulated annealing method (inspired by the refrigeration process metals). The methods for solving optimization problems in many different areas such as determining the optimal course of their work, designing optimal control for industrial processes, solving industrial engineering major issues such as the optimal layout design for industrial units, problem solving, and queuing in the design of intelligent agents have been used. This paper introduces a new algorithm for optimization, which is not a natural phenomenon, but a phenomenon inspired teaching-human. It is entitled Education System algorithm (ESA). Results demonstrate this method is better than other method in this area. [ABSTRACT FROM AUTHOR]

The Flexible AC Transmission System (FACTS) is a system for improving the operation of electrical power systems. It is very important to know that their implementation is very difficult and imposes a particular location that must be mandatory optimal. In this paper one of the heuristics methods based on genetics has been implemented for determining the optimal locations of FACTS devices in the electrical network. The standard 14-node IEEE network has been used for testing and validating the proposed method it in the MATLAB environment. [ABSTRACT FROM AUTHOR]

Curse of dimensionality is one of the biggest challenges in classification problems. High dimensionality of problem increases classification rate and brings about classification error. Selecting an effective subset of features is an important point in analyzing correlation rate in classification issues. The main purpose of this paper is enhancing characters recognition and classification, creating quick and low-cost classes, and eventually recognizing Persian handwritten characters more accurately and faster. In this paper, to reduce feature dimensionality of datasets, a hybrid approach using artificial neural network, genetic algorithm and quantum genetic algorithm is proposed that can be used to distinguish Persian handwritten letters. Implementation results show that proposed algorithms are able to reduce number of features by 19% to 49%. They also show that recognition and classification accuracy of resulted subset of features has risen, by 7/31%, comparing to primitive dataset. [ABSTRACT FROM AUTHOR]

The multi machine power system, with the interconnection of number of generators and loads, has the dynamic stability as the important factor for maintaining the step with respect to the generators connected to it. The stability of individual machine, as well as, the stability of a generator with the other generators are more important terms. The supply of the damping torque required for getting the desired condition of stability enhancement is done by the power system stabilizer. In this paper a new method is proposed for stability enhancement of a three machine nine bus system by using the coordinated application of the unified power flow controller and the power system stabilizer designed by using the Firefly algorithm. The improved stability performance of the tested multi machine system was compared with Genetic search algorithm approach without and with the application of the unified power flow controller. From the pseudo spectrum analysis, it is observed that the better improvement of the stability of the multi machine system is achieved by using the proposed method. From the step responses it can be deduced that the relative variations of inter machine states with unified power flow controller and firefly-based power system stabilizer are settled at a faster rate. The contingency analysis is performed to consider the non-linearity problem. The responses of the system with unified power flow controller and firefly based power system stabilizer are settled at a faster rate in the normal case, as well as, in the contingency cases, respectively. [ABSTRACT FROM AUTHOR]

In this paper optimum size and location of the capacitors and distributed generators (DGs) are determined for reliability improvement and power loss reduction using genetic algorithm (GA). The main innovation of this paper is using both DG and Capacitor for the reliability improvement and power loss reduction. For this purpose an objective function consisting of reliability cost, power loss cost and also DG's and capacitor's investment cost are considered. The effectiveness of the proposed method is examined in the 10 and 33 bus test systems and comparative studies are conducted before and after DG and Capacitor installation in the test systems. The results obtained show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents determination of optimum size and location of distributed generators (DGs) for reliability improvement of distribution systems in the presence of time varying loads using Genetic Algorithm (GA). The main innovation of this paper is considering of the annual load duration curve for determination of size and location of DGs for reliability improvement. For this purpose, a load duration curve, including four load levels with different weighting factor is considered. For reliability assessment, the customer-oriented reliability indices such as SAIFI, SAIDI, CAIDI, ASUI and also load- and energy-oriented indices such as ENS are evaluated. In this paper, the effects of system reconfiguration and load shedding are also considered for reliability improvement. The best size and location of DGs in distribution systems are determined based on different reliability indices, separately. The effectiveness of the proposed algorithm is examined on a standard distribution system consisting of 33 nodes, and comparative studies are conducted in the different cases to investigate the impacts of optimal DGs placement and its size determination on reliability improvement. The obtained results show the effectiveness of the proposed method for reliability improvement. [ABSTRACT FROM AUTHOR]

In order to analyze the mathematical modeling and PID controller performance of a quadrotor, this paper firstly, describes the quadrotor flight dynamics according to “Newton-Euler laws”, then equations of motion linearized and transfer functions for 6 degrees of freedom obtained in state space domain. Classic PID controller based on “ZieglerNichols method” is designed and implemented on the system. In order to have better performance, Genetic Algorithm based on step response optimization is used to optimize PID controller performance and compared with classic method. Finally, step responses comparison for each transfer function show that Genetic Algorithm with PID control synthesis better efficiency than the classic PID controller. [ABSTRACT FROM AUTHOR]

Rolling mill Industry is one of the most profitable industries in the world. Chatter phenomenon is one of the key issues in this industry. Chatter or rolling unwanted vibrations not only has an adverse effect on product quality, but also reduces considerably the efficiency with reduced rolling velocities of rolling lines. This paper is an attempt to simulate the phenomenon of Chatter more accurate than the previous performed simulations. In order to increase the production speed, it needs to avoid parameters which effect on the Chatter and varieties with the rolling lines condition. Actual values of these parameters were determined in the archives of the Mobarakeh two stand cold rolling mills and collected on the 210 case study of real chattering. To simulate the experiment, a neural network is trained and weights and bias values of the neural network with genetic optimization algorithm were used to get an optimal neural network which reduces bugs on the test data. So this model is capable to predict speed of Chatter threshold on rolling process of two stand cold rolling mill with the accuracy less than one percent. So it can be used in rolling process with the building intelligent recognition systems to prevent the creator conditions of the chatter frequency range. [ABSTRACT FROM AUTHOR]

This paper presents an advanced optimization technique to solve unit commitment problems and reliability issues simultaneously for thermal generating units. To solve unit commitment, generalized benders decomposition along with genetic algorithm to include minimum up/down time constraints are proposed, and for reliability issues consideration, a fuzzy stochastic-based technique is presented. To implement the problem into an optimization program, the MATLAB software, and CPLEX and KNITRO solvers are used. To verify the proposed technique and algorithm, two case studies that are IEEE 14 and 118 bus systems are implemented for optimal generation scheduling, and reliability issues. Finally, a comparison with other solution techniques has been given. [ABSTRACT FROM AUTHOR]

The main objectives of distribution network reconfiguration are real power loss reduction and relieving the overload. As well, integration of distributed generation (DG) units and fixed/switched capacitor banks are effective options for operation cost reduction, reducing system losses, improving voltage profile and increasing voltage stability index in the distribution systems. Therefore, simultaneous network reconfiguration, optimal placement of DG units and fixed/switched capacitor banks are proposed in this paper. The dedicated genetic algorithm is proposed for simultaneous network reconfiguration and allocation of DG units and fixed/switched capacitor banks in distribution systems considering different load levels. The proposed approach is able to find good network configurations and determine the optimal site and size of DG units and fixed/switched capacitor banks in distribution systems, simultaneously. The considered objective function encompasses the total cost of power loss, the investment and operation costs of DG units, the installation cost of fixed/switched capacitor banks and cost of purchased active power demand from upstream network. The proposed method is applied to 69-bus distribution system. The simulation results and comparison of different scenarios reveals that the simultaneous network reconfiguration and allocation of DG units and fixed/switched capacitor banks, results in significant advantages in the total cost. [ABSTRACT FROM AUTHOR]

Modern electric power utilities are facing many challenges due to increasing complexity in their operation and structure. In the recent times, one of the problems that got wide attention is the power system instabilities due to lack of new transmission facilities. Existing transmission facilities can be better utilized by installing Flexible AC Transmission System (FACTS) devices. The Thyristor Controlled Series Capacitor (TCSC) is the most effective FACTS device used to increase the power transferable capabilities of the transmission line. This paper presents a sensitivity analysis based on Complex Power Flow Sensitivity Index (CPSI) calculation for placing the TCSC at an appropriate location. Once the location for installing the TCSC is determined, the optimal tuning of the TCSC and the impact of TCSC on generation reallocation is determined through Firefly Algorithm. This Algorithm was implemented on multi objective function to obtain the Optimal Power Flow. The multi objective function consists of total real power loss, total voltage magnitude deviations, the fuel cost of total real power generation and the branch loading. Simulations have been carried out in MATLAB environment for the IEEE 57-bus system. The results have been taken for Firefly Algorithm based Optimal Power Flow without and with TCSC. The results obtained with Firefly Algorithm were compared with Genetic Algorithm (GA) [ABSTRACT FROM AUTHOR]

Rolling mill Industry is one of the most profitable industries in the world. Chatter phenomenon is one of the key issues in this industry. Chatter or rolling unwanted vibrations not only has an adverse effect on product quality, but also reduces considerably the efficiency with reduced rolling velocities of rolling lines. This paper is an attempt to simulate the phenomenon of Chatter more accurate than the previous performed simulations. In order to increase the production speed, it needs to avoid parameters which effect on the Chatter and varieties with the rolling lines condition. Actual values of these parameters were determined in the archives of the Mobarakeh two stand cold rolling mills and collected on the 210 case study of real chattering. To simulate the experiment, a neural network is trained and weights and bias values of the neural network with genetic optimization algorithm were used to get an optimal neural network which reduces bugs on the test data. So this model is capable to predict speed of Chatter threshold on rolling process of two stand cold rolling mill with the accuracy less than one percent. So it can be used in rolling process with the building intelligent recognition systems to prevent the creator conditions of the chatter frequency range. [ABSTRACT FROM AUTHOR]

This paper presents an advanced optimization technique to solve unit commitment problems and reliability issues simultaneously for thermal generating units. To solve unit commitment, generalized benders decomposition along with genetic algorithm to include minimum up/down time constraints are proposed, and for reliability issues consideration, a fuzzy stochastic-based technique is presented. To implement the problem into an optimization program, the MATLAB software, and CPLEX and KNITRO solvers are used. To verify the proposed technique and algorithm, two case studies that are IEEE 14 and 118 bus systems are implemented for optimal generation scheduling, and reliability issues. Finally, a comparison with other solution techniques has been given. [ABSTRACT FROM AUTHOR]

This paper presents a new method for optimal multi-objective reconfiguration of distribution system based on the novel Intelligent Water Drops (IWD) algorithm in order to mitigation of losses, improving the voltage profile and equalizing the feeder load balancing. The proposed method is validated using the IEEE33-bus test system and a Tai-Power 11.4 KV distribution system as a real distribution network. Also to assess the performance of the proposed method at different load levels, the two test networks are simulated at three load levels: light, nominal, and heavy. The results show that multi objective reconfiguration using Intelligent Water Drops leads to improvement in all objectives at various load levels. The obtained results prove that the proposed technique can be more accurate than other well known methods such as Genetic Algorithm, Particle Swarm Optimization, and Harmony Search algorithm. [ABSTRACT FROM AUTHOR]

Early detection of heart diseases/abnormalities can prolong life and enhance the quality of living through appropriate treatment. Whereas clustering of electrocardiogram (ECG) signals will help to identify the heart diseases as soon as possible. In this regard, neural network and fuzzy logic have been used in many application areas while each of them has the advantages and disadvantages. Thus, the present paper utilizes the proposed fuzzy neural network (FNN) with initial weights generated by genetic algorithm (GFNN) for the sake of improvement testing speed, accuracy and for reducing the chance of the FNN getting stuck on a local minimum. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat and atrial fibrillation beat) obtained from the Physio Bank databases were clustered by the proposed GFNN model. Model of evaluation results indicate that the proposed model can perform more accurately and it has less testing speed than the conventional statistical methods, a single ANN and FNN. The total clustering accuracy of the GFNN model is 98.23%. [ABSTRACT FROM AUTHOR]

Minimal stopping distance, guaranteed steering ability and stability are the three most important purposes in Anti-lock Braking System (ABS) realm. The ABS system is a nonlinear, time variant and multivariable system with some uncertainties. Some research work has been carried out on ABS control systems using intricate methods, which are expensive to implement. In this paper at the first step, the system interference is decreased via decoupling matrix and the ABS is controlled with a robust diagonal controller. In fact, a decentralized control technique is used for our ABS control mechanism. At the second step, we exploit a multivariable technique in linear control to attack the problem. This is the Designed Linear Control with Multivariable Technique. The Optimal Eigenstructure assignment with the Genetic Algorithm (GA) method is also applied. Simulation and comparison studies are used to show the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Biogeography based optimization (BBO) is a new stochastic force based on the science of biogeography. Biogeography is the schoolwork of geographical allotment of biological organisms. BBO utilizes migration operator to share information between the problem solutions. The problem solutions are known as habitats and sharing of features is called migration. In this paper, BBO algorithm is developed to optimize the current excitations of concentric circular antenna arrays (CCAA). Concentric Circular Antenna Array (CCAA) has numerous attractive features that make it essential in mobile and communication applications. The goal of the optimization is to reduce the side lobe levels and the primary lobe beam width as much as possible. To confirm the capabilities of BBO, three different CCAA antennas of different sizes are taken. The results obtained by BBO are compared with the Real coded Genetic Algorithm (RGA), Craziness based Particle Swarm Optimization (CRPSO) and Hybrid Evolutionary Programming (HEP). [ABSTRACT FROM AUTHOR]

Electricity market plays an important role in improving the economics of electrical power system. Transmission network is vital entity in an open access restructured electricity market. Whenever transmission network congestion occurs in an electricity market, it divides the market in different zones and the trading price of electricity will no longer remains the same for the whole system. Bidding strategies in an electricity market, where by changing the bid, market player changes the revenue of every participant of the market. In this paper, the bidding strategy problem with congestion management is modeled as an optimization problem and solved using Particle Swarm Optimization (PSO). Search procedure of PSO is based on the concept of combined effect of cognitive and social learning of the members in a group. The effectiveness of the proposed method is tested with a numerical example and the results are compared with Genetic Algorithm (GA) approach. The results shows that PSO takes less computational time and maximizing the social welfare compared to GA approach. [ABSTRACT FROM AUTHOR]

An important technique in image and video processing is global motion estimation (GME). The common GME methods can be classified in direct and indirect categories. Whereas the direct global motion estimation techniques boast reasonable precision they tend to suffer from high complexity. As with indirect methods, though presenting lower complexity, they mostly exhibit lower accuracy than their direct counterparts. In this paper, the authors introduce a robust algorithm for GME with near identical accuracy and almost 50-times faster than MPEG-4 verification model (VM). This approach entails two stages in which, first, motion vector of sampled block is employed to obtain initial GME then Levenberg-Marquardt algorithm is applied to the subsampled pixels to optimize the initial GME values. As will be shown, the proposed solution exhibits remarkable accuracy and speed features with experimental results distinctively bearing them out. [ABSTRACT FROM AUTHOR]

The number of attacks in computer networks has grown extensively, and many new intrusive methods have been appeared. Intrusion detection is known as an effective method to secure the information and communication systems. In this paper, the performance of Elman and partial-connected dynamic neural network (PCDNN) architectures are investigated for misuse detection in computer networks. To select the most significant features, logistic regression is also used to rank the input features of mentioned neural networks (NNs) based on the Chi-square values for different selected subsets in this work. In addition, genetic algorithm (GA) is used as an optimization search scheme to determine the sub-optimal architecture of investigated NNs with selected input features. International knowledge discovery and data mining group (KDD) dataset is used for training and test of the mentioned models in this study. The features of KDD data are categorized as basic, content, time-based traffic, and host-based traffic features. Empirical results show that PCDNN with selected input features and categorized input connections offers better detection rate (DR) among the investigated models. The mentioned NN also performs better in terms of cost per example (CPE) when compared to other proposed models in this study. False alarm rate (FAR) of the PCDNN with selected input features and categorized input connections is better than other proposed models, as well. [ABSTRACT FROM AUTHOR]

In this paper, we propose a genetic fuzzy image filtering based on rank-ordered absolute differences (ROAD) and median of the absolute deviations from the median (MAD). The proposed method which is consisted of three components, including fuzzy noise detection system, fuzzy switching scheme filtering, and fuzzy parameters optimization use genetic algorithms (GA) to perform efficient and effective noise removal. Our idea is to utilize MAD and ROAD as measures of noise probability of a pixel. Fuzzy inference system is used to justify the degree of which a pixel can be categorized as noisy. Based on the fuzzy inference result, the fuzzy switching scheme that adopts median filter as the main estimator is applied to the filtering. The GA training aims to find the best parameters for the fuzzy sets in the fuzzy noise detection. Based on the experimental results, the proposed method has successfully removed mixed impulse noise in low to medium probabilities, while keeping the uncorrupted pixels less affected by the median filtering. It also surpasses the other methods, either classical or soft computing-based approaches to impulse noise removal, in MAE and PSNR evaluations. [ABSTRACT FROM AUTHOR]

The reports show a rapid growth in the numbers of attacks to the information and communication systems. Also, we witness smarter behaviors from the attackers. Thus, to prevent our systems from these attackers, we need to create smarter intrusion detection systems. In this paper, a new intelligent intrusion detection system has been proposed using genetic algorithms. In this system, at first, the network connection features were ranked according to their importance in detecting attack using information theory measures. Then, the network traffic linear classifiers based on genetic algorithms have been designed. These classifiers were trained and tested using KDD99 data sets. A detection engine based on these classifiers was build and experimented. The experimental results showed a detection rate up to 92.94%. This engine can be used in real-time mode. [ABSTRACT FROM AUTHOR]

Linear permanent magnet synchronous motors are increasingly used in industrial application. Proper performance of these motors requires appropriate design and construction of them. This paper presents the suitable topology selection and optimal design of a linear permanent magnet synchronous motor for precise applications. For this, a comprehensive analysis is done on different topologies of this kind of machines and their benefits and drawbacks are discussed in details. The proper topology is then selected and modeled via layer method. A typical motor is then optimized using this model and genetic algorithm method to reduce the consumed permanent magnet material and therefore reducing the cost. The performance of the optimal motor is finally assessed by dynamic finite element method. The results demonstrate the validity of the proposed method. [ABSTRACT FROM AUTHOR]

With increase energy consumption in the world, power network development is necessary but creating a new transmission line is very costly. Therefore increasing line transmission capability is economic. We propose a method to FACTS device to the power network, in this paper and resulted in introducing decrease power network loss's and increase transmission capability with optimal choice and allocation of FACTS device by using genetic algorithm. We examine, proving this method by simulation IEEE6 bus system and using the genetic algorithm for optimal choice and allocation of FACTS device in this system. [ABSTRACT FROM AUTHOR]