Search results for: genetic algorithm method

Authors: Seyed Mahdi Jameii, Arash Nikdel, Seyed Mohsen Jameii

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Energy efficiency is an important issue in the field of Wireless Sensor Networks (WSNs). So, minimizing the energy consumption in this kind of networks should be an essential consideration. Sleep/wake scheduling mechanism is an efficient approach to handling this issue. In this paper, we propose a Genetic Algorithm-based Sleep-Wake up Area Coverage protocol called GA-SWAC. The proposed protocol puts the minimum of nodes in active mode and adjusts the sensing radius of each active node to decrease the energy consumption while maintaining the network’s coverage. The proposed protocol is simulated. The results demonstrate the efficiency of the proposed protocol in terms of coverage ratio, number of active nodes and energy consumption.

Keywords: wireless sensor networks, genetic algorithm, coverage, connectivity

Procedia PDF Downloads 488

Authors: Viraj Tyagi, Ajai Jain, P. K. Jain, Aarushi Jain

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This paper presents a genetic algorithm based loading methodology for a capacity constrained job-shop with the consideration of alternative process plans for each part to be produced. Performance analysis of the proposed methodology is carried out for two case studies by considering two different manufacturing scenarios. Results obtained indicate that the methodology is quite effective in improving the shop load balance, and hence, it can be included in the frameworks of manufacturing planning systems of job-shop oriented industries.

Keywords: manufacturing planning, loading, genetic algorithm, job shop

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Authors: Saleem Z. Ramadan

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The material selection problem is concerned with the determination of the right material for a certain product to optimize certain performance indices in that product such as mass, energy density, and power-to-weight ratio. This paper is concerned about optimizing the selection of the manufacturing process along with the material used in the product under performance indices and availability constraints. In this paper, the material selection problem is formulated using binary programming and solved by genetic algorithm. The objective function of the model is to minimize the total manufacturing cost under performance indices and material and manufacturing process availability constraints.

Keywords: optimization, material selection, process selection, genetic algorithm

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Authors: Bliss Singhal

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Machine learning (ML) is a branch of Artificial Intelligence (AI) where computers analyze data and find patterns in the data. The study focuses on the detection of metastatic cancer using ML. Metastatic cancer is the stage where cancer has spread to other parts of the body and is the cause of approximately 90% of cancer-related deaths. Normally, pathologists spend hours each day to manually classifying whether tumors are benign or malignant. This tedious task contributes to mislabeling metastasis being over 60% of the time and emphasizes the importance of being aware of human error and other inefficiencies. ML is a good candidate to improve the correct identification of metastatic cancer, saving thousands of lives and can also improve the speed and efficiency of the process, thereby taking fewer resources and time. So far, the deep learning methodology of AI has been used in research to detect cancer. This study is a novel approach to determining the potential of using preprocessing algorithms combined with classification algorithms in detecting metastatic cancer. The study used two preprocessing algorithms: principal component analysis (PCA) and the genetic algorithm, to reduce the dimensionality of the dataset and then used three classification algorithms: logistic regression, decision tree classifier, and k-nearest neighbors to detect metastatic cancer in the pathology scans. The highest accuracy of 71.14% was produced by the ML pipeline comprising of PCA, the genetic algorithm, and the k-nearest neighbor algorithm, suggesting that preprocessing and classification algorithms have great potential for detecting metastatic cancer.

Keywords: breast cancer, principal component analysis, genetic algorithm, k-nearest neighbors, decision tree classifier, logistic regression

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Authors: Yue-Tzu Yang, Peng-Jen Chen

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The present study deals with the numerical optimization of wavy channel with the help of genetic algorithm (GA). Three design variables related to the wave amplitude (A), the wavelength (λ) and the channel aspect ratio (α) are chosen and their ranges are decided through preliminary calculations of three-dimensional Navier-stokes and energy equations. A parametric study is also performed to show the effects of different design variables on the overall performance of the wavy channel. Objective functions related to the heat transfer and pressure drop, performance factor (PF) is formulated to analyze the performance of the wavy channel. The numerical results show that the wave amplitude and the channel aspect ratio have significant effects on the thermal performance. It can improve the performance of the wavy channels by increasing wave amplitude or decreasing the channel aspect ratio. Increasing wavelengths have no significant effects on the heat transfer performance.

Keywords: wavy channel, genetic algorithm, optimization, numerical simulation

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Authors: Sonveer Singh, Sanjay Agrawal, D. V. Avasthi, Jayant Shekhar

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The aim of this paper is to improve the efficiency of photovoltaic thermal (PVT) system with the help of Genetic Algorithms with multi-objective function. There are some parameters that affect the efficiency of PVT system like depth and length of the channel, velocity of flowing fluid through the channel, thickness of the tedlar and glass, temperature of inlet fluid i.e. all above parameters are considered for optimization. An attempt has been made to the model and optimizes the parameters of glazed hybrid single channel PVT module when two objective functions have been considered separately. The two objective function for optimization of PVT module is overall electrical and thermal efficiency. All equations for PVT module have been derived. Using genetic algorithms (GAs), above two objective functions of the system has been optimized separately and analysis has been carried out for two cases. Two cases are: Case-I; Improvement in electrical and thermal efficiency when overall electrical efficiency is optimized, Case-II; Improvement in electrical and thermal efficiency when overall thermal efficiency is optimized. All the parameters that are used in genetic algorithms are the parameters that could be changed, and the non-changeable parameters, like solar radiation, ambient temperature cannot be used in the algorithm. It has been observed that electrical efficiency (14.08%) and thermal efficiency (19.48%) are obtained when overall thermal efficiency was an objective function for optimization. It is observed that GA is a very efficient technique to estimate the design parameters of hybrid single channel PVT module.

Keywords: genetic algorithm, energy, exergy, PVT module, optimization

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Authors: L. Petrovičová, Ž. Balážová, Z. Gálová, M. Wójcik-Jagła, M. Rapacz

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In the present study, RAPD-PCR was used to assess genetic diversity of the rye including landrances and new rye cultivars coming from Central Europe and the Union of Soviet Socialist Republics (SUN). Five arbitrary random primers were used to determine RAPD polymorphism in the set of 38 rye genotypes. These primers amplified altogether 43 different DNA fragments with an average number of 8.6 fragments per genotypes. The number of fragments ranged from 7 (RLZ 8, RLZ 9 and RLZ 10) to 12 (RLZ 6). DI and PIC values of all RAPD markers were higher than 0.8 that generally means high level of polymorphism detected between rye genotypes. The dendrogram based on hierarchical cluster analysis using UPGMA algorithm was prepared. The cultivars were grouped into two main clusters. In this experiment, RAPD proved to be a rapid, reliable and practicable method for revealing of polymorphism in the rye cultivars.

Keywords: genetic diversity, polymorphism, RAPD markers, Secale cereale L.

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Authors: Ashwini Gowda K. S, Archana M. R, Anjaneyappa V

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This paper presents pavement maintenance and management system (PMMS) to obtain optimum pavement maintenance and rehabilitation strategies and maintenance scheduling for a network using a multi-objective genetic algorithm (MOGA). Optimal pavement maintenance & rehabilitation strategy is to maximize the pavement condition index of the road section in a network with minimum maintenance and rehabilitation cost during the planning period. In this paper, NSGA-II is applied to perform maintenance optimization; this maintenance approach was expected to preserve and improve the existing condition of the highway network in a cost-effective way. The proposed PMMS is applied to a network that assessed pavement based on the pavement condition index (PCI). The minimum and maximum maintenance cost for a planning period of 20 years obtained from the non-dominated solution was found to be 5.190x10¹⁰ ₹ and 4.81x10¹⁰ ₹, respectively.

Keywords: genetic algorithm, maintenance and rehabilitation, optimization technique, pavement condition index

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Authors: Uswah Khairuddin, Rubiyah Yusof, Nenny Ruthfalydia Rosli

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This paper presents a comparative study between k-Nearest Neighbour (k-NN) and Multi-Layer Perceptron Neural Network (MLP-NN) classifier using Genetic Algorithm (GA) as feature selector for wood recognition system. The features have been extracted from the images using Grey Level Co-Occurrence Matrix (GLCM). The use of GA based feature selection is mainly to ensure that the database used for training the features for the wood species pattern classifier consists of only optimized features. The feature selection process is aimed at selecting only the most discriminating features of the wood species to reduce the confusion for the pattern classifier. This feature selection approach maintains the ‘good’ features that minimizes the inter-class distance and maximizes the intra-class distance. Wrapper GA is used with k-NN classifier as fitness evaluator (GA-kNN). The results shows that k-NN is the best choice of classifier because it uses a very simple distance calculation algorithm and classification tasks can be done in a short time with good classification accuracy.

Keywords: feature selection, genetic algorithm, optimization, wood recognition system

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Authors: Hyeon-Seung Kim, Young-Hwan Kim, Sang-Mi Park, Min-Seo Kim, Jong-Myeung Shin, Leen-Seok Kang

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The project budget at the planned stage might be changed by the insufficient government budget or the design change. There are many cases more especially in the case of a project performed for a long period of time. If the actual construction budget is insufficient comparing with the planned budget, the construction schedule should also be changed to match the changed budget. In that case, most project managers change the planned construction schedule by a heuristic approach without a reasonable consideration on the work priority. This study suggests an optimized methodology to modify the construction schedule according to the changed budget. The genetic algorithm was used to optimize the modified construction schedule within the changed budget. And a simulation system of construction cost histogram in accordance with the construction schedule was developed in the BIM (Building Information Modeling) environment.

Keywords: 5D, BIM, GA, cost optimization

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Authors: Dipti Patra, Guguloth Uma, Smita Pradhan

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Registration is a fundamental task in image processing. It is used to transform different sets of data into one coordinate system, where data are acquired from different times, different viewing angles, and/or different sensors. The registration geometrically aligns two images (the reference and target images). Registration techniques are used in satellite images and it is important in order to be able to compare or integrate the data obtained from these different measurements. In this work, mutual information is considered as a similarity metric for registration of satellite images. The transformation is assumed to be a rigid transformation. An attempt has been made here to optimize the transformation function. The proposed image registration technique hybrid PSO-GA incorporates the notion of Particle Swarm Optimization and Genetic Algorithm and is used for finding the best optimum values of transformation parameters. The performance comparision obtained with the experiments on satellite images found that the proposed hybrid PSO-GA algorithm outperforms the other algorithms in terms of mutual information and registration accuracy.

Keywords: image registration, genetic algorithm, particle swarm optimization, hybrid PSO-GA algorithm and mutual information

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Authors: Choonoh Lee, Seyeon Park, Dongyun Kang, Jaehyeong Choi, Soojee Kim, Younggeun Kim

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Market Kurly is the first South Korean online grocery retailer that guarantees same-day, overnight shipping. More than 1.6 million customers place an average of 4.7 million orders and add 3 to 14 products into a cart per month. The company has sold almost 30,000 kinds of various products in the past 6 months, including food items, cosmetics, kitchenware, toys for kids/pets, and even flowers. The company is operating and expanding multiple dry, cold, and frozen fulfillment centers in order to store and ship these products. Due to the scale and complexity of the fulfillment, pick-pack-ship processes are planned and operated in batches, and thus, the planning that decides the batch of the customers’ orders is a critical factor in overall productivity. This paper introduces a metaheuristic optimization method that reduces the complexity of batch processing in a fulfillment center. The method is an iterative genetic algorithm with heuristic creation and evolution strategies; it aims to group similar orders into pick-pack-ship batches to minimize the total number of distinct products. With a well-designed approach to create initial genes, the method produces streamlined plans, up to 13.5% less complex than the actual plans carried out in the company’s fulfillment centers in the previous months. Furthermore, our digital-twin simulations show that the optimized plans can reduce 3% of operation time for packing, which is the most complex and time-consuming task in the process. The optimization method implements a multithreading design on the Spring framework to support the company’s warehouse management systems in near real-time, finding a solution for 4,000 orders within 5 to 7 seconds on an AWS c5.2xlarge instance.

Keywords: fulfillment planning, genetic algorithm, online grocery retail, optimization

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Authors: Xiyang Li, Qi Yu, Lun Zhang

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In the face of natural disasters and other emergency situations, determining the optimal location of rescue centers is crucial for improving rescue efficiency and minimizing impact on affected populations. This paper proposes a method that integrates genetic algorithms (GA) and backpropagation neural networks (BPNN) to address the site selection optimization problem for emergency rescue centers. We utilize BPNN to accurately estimate the cost of delivering supplies from rescue centers to each temporary camp. Moreover, a genetic algorithm with a special partially matched crossover (PMX) strategy is employed to ensure that the number of temporary camps assigned to each rescue center adheres to predetermined limits. Using the population distribution data during the 2022 epidemic in Jiading District, Shanghai, as an experimental case, this paper verifies the effectiveness of the proposed method. The experimental results demonstrate that the BPNN-GA method proposed in this study outperforms existing algorithms in terms of computational efficiency and optimization performance. Especially considering the requirements for computational resources and response time in emergency situations, the proposed method shows its ability to achieve rapid convergence and optimal performance in the early and mid-stages. Future research could explore incorporating more real-world conditions and variables into the model to further improve its accuracy and applicability.

Keywords: emergency rescue centers, genetic algorithms, back-propagation neural networks, site selection optimization

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Authors: Arslan Ellahi, Syed Amjad Hussain

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Software Testing is a vital process in software development life cycle. We can attain the quality of software after passing it through software testing phase. We have tried to find out automatic test data generation techniques that are a key research area of software testing to achieve test automation that can eventually decrease testing time. In this paper, we review some of the approaches presented in the literature which use evolutionary search based algorithms like Genetic Algorithm, Particle Swarm Optimization (PSO), etc. to validate the test data generation process. We also look into the quality of test data generation which increases or decreases the efficiency of testing. We have proposed test data generation techniques for model-based testing. We have worked on tuning and fitness function of PSO algorithm.

Keywords: search based, evolutionary algorithm, particle swarm optimization, genetic algorithm, test data generation

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Authors: Agi Sunday

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A descriptive analysis of reported cases of sickle cell disease and the level of awareness about genetic counselling in 30 hospitals were carried out. Additionally, 150 individuals between ages 16-45 were randomly selected for evaluation of genetic counselling awareness. The main tools for this study were questionnaires which were taken to hospitals, and individuals completed the others. The numbers of reported cases of sickle cell disease recorded in private, public and teaching hospitals were 14 and 57; 143 and 89; 272 and 57 for the periods of 1995-2000 and 2001-2005, respectively. A general informal genetic counselling took place mostly in the hospitals visited. 122 (86%) individuals had the knowledge of genetic disease and only 43 (30.3%) individuals have been exposed to genetic counselling. 64% of individuals agreed that genetic counselling would help in the prevention of genetic disease.

Keywords: sickle disease, genetic counseling, genetic testing, advocacy

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Authors: Roza Afarin, Saeed Mozaffari

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The aim of this paper is image encryption using Genetic Algorithm (GA). The proposed encryption method consists of two phases. In modification phase, pixels locations are altered to reduce correlation among adjacent pixels. Then, pixels values are changed in the diffusion phase to encrypt the input image. Both phases are performed by GA with binary chromosomes. For modification phase, these binary patterns are generated by Local Binary Pattern (LBP) operator while for diffusion phase binary chromosomes are obtained by Bit Plane Slicing (BPS). Initial population in GA includes rows and columns of the input image. Instead of subjective selection of parents from this initial population, a random generator with predefined key is utilized. It is necessary to decrypt the coded image and reconstruct the initial input image. Fitness function is defined as average of transition from 0 to 1 in LBP image and histogram uniformity in modification and diffusion phases, respectively. Randomness of the encrypted image is measured by entropy, correlation coefficients and histogram analysis. Experimental results show that the proposed method is fast enough and can be used effectively for image encryption.

Keywords: correlation coefficients, genetic algorithm, image encryption, image entropy

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Authors: Essa Abrahim Abdulgader Saleem, Thien-My Dao

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The main objective of this paper is to optimize series-parallel system reliability using Genetic Algorithm (GA) and statistical analysis; considering system reliability constraints which involve the redundant numbers of selected components, total cost, and total weight. To perform this work, firstly the mathematical model which maximizes system reliability subject to maximum system cost and maximum system weight constraints is presented; secondly, a statistical analysis is used to optimize GA parameters, and thirdly GA is used to optimize series-parallel systems reliability. The objective is to determine the strategy choosing the redundancy level for each subsystem to maximize the overall system reliability subject to total cost and total weight constraints. Finally, the series-parallel system case study reliability optimization results are showed, and comparisons with the other previous results are presented to demonstrate the performance of our GA.

Keywords: reliability, optimization, meta-heuristic, genetic algorithm, redundancy

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Authors: Ousmane Youme, Jean Marie Dembele, Eugene Ezin, Christophe Cambier

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In recent years, the convolutional neural networks (CNN) architectures designed by evolution algorithms have proven to be competitive with handcrafted architectures designed by experts. However, these algorithms need a lot of computational power, which is beyond the capabilities of most researchers and engineers. To overcome this problem, we propose an evolution architecture under length constraints. It consists of two algorithms: a search length strategy to find an optimal space and a search architecture strategy based on a genetic algorithm to find the best individual in the optimal space. Our algorithms drastically reduce resource costs and also keep good performance. On the Cifar-10 dataset, our framework presents outstanding performance with an error rate of 5.12% and only 4.6 GPU a day to converge to the optimal individual -22 GPU a day less than the lowest cost automatic evolutionary algorithm in the peer competition.

Keywords: CNN architecture, genetic algorithm, evolution algorithm, length constraints

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Authors: Hyungoo Kang, Jinkoo Kim

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This study investigated the determination of the optimal location and friction force of friction dampers to effectively reduce the seismic response of a reinforced concrete structure designed without considering seismic load. To this end, the genetic algorithm process was applied and the results were compared with those obtained by simplified methods such as distribution of dampers based on the story shear or the inter-story drift ratio. The seismic performance of the model structure with optimally positioned friction dampers was evaluated by nonlinear static and dynamic analyses. The analysis results showed that compared with the system without friction dampers, the maximum roof displacement and the inter-story drift ratio were reduced by about 30% and 40%, respectively. After installation of the dampers about 70% of the earthquake input energy was dissipated by the dampers and the energy dissipated in the structural elements was reduced by about 50%. In comparison with the simplified methods of installation, the genetic algorithm provided more efficient solutions for seismic retrofit of the model structure.

Keywords: friction dampers, genetic algorithm, optimal design, RC buildings

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Authors: Roozbeh Aghamajidi

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Shaft spillways are circling spillways used generally for emptying unexpected floods on earth and concrete dams. There are different types of shaft spillways: Stepped and Smooth spillways. Stepped spillways pass more flow discharges through themselves in comparison to smooth spillways. Therefore, awareness of flow behavior of these spillways helps using them better and more efficiently. Moreover, using vortex breaker has great effect on passing flow through shaft spillway. In order to use more efficiently, the risk of flow pressure decreases to less than fluid vapor pressure, called cavitations, should be prevented as far as possible. At this research, it has been tried to study different behavior of spillway with different vortex shapes on spillway crest on flow. From the viewpoint of the effects of flow regime changes on spillway, changes of step dimensions, and the change of type of discharge will be studied effectively. Therefore, two spillway models with three different vortex breakers and three arrangements have been used to assess the hydraulic characteristics of flow. With regard to the inlet discharge to spillway, the parameters of pressure and flow velocity on spillway surface have been measured at several points and after each run. Using these kinds of information leads us to create better design criteria of spillway profile. To achieve these purposes, optimization has important role and genetic algorithm are utilized to study the emptying discharge. As a result, it turned out that the best type of spillway with maximum discharge coefficient is smooth spillway with ogee shapes as vortex breaker and 3 number as arrangement. Besides it has been concluded that the genetic algorithm can be used to optimize the results.

Keywords: shaft spillway, vortex breaker, flow, genetic algorithm

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Authors: M. Saiful Islam, M. Mohandes, S. Rehman, S. Badran

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Increasing necessity of wind power is directing us to have precise knowledge on wind resources. Methodical investigation of potential locations is required for wind power deployment. High penetration of wind energy to the grid is leading multi megawatt installations with huge investment cost. This fact appeals to determine appropriate places for wind farm operation. For accurate assessment, detailed examination of wind speed profile, relative humidity, temperature and other geological or atmospheric parameters are required. Among all of these uncertainty factors influencing wind power estimation, vertical extrapolation of wind speed is perhaps the most difficult and critical one. Different approaches have been used for the extrapolation of wind speed to hub height which are mainly based on Log law, Power law and various modifications of the two. This paper proposes a Artificial Neural Network (ANN) and Genetic Algorithm (GA) based hybrid model, namely GA-NN for vertical extrapolation of wind speed. This model is very simple in a sense that it does not require any parametric estimations like wind shear coefficient, roughness length or atmospheric stability and also reliable compared to other methods. This model uses available measured wind speeds at 10m, 20m and 30m heights to estimate wind speeds up to 100m. A good comparison is found between measured and estimated wind speeds at 30m and 40m with approximately 3% mean absolute percentage error. Comparisons with ANN and power law, further prove the feasibility of the proposed method.

Keywords: wind profile, vertical extrapolation of wind, genetic algorithm, artificial neural network, hybrid machine learning

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Authors: Prasanna Haddela

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Document analysis is a well matured yet still active research field, partly as a result of the intricate nature of building computational tools but also due to the inherent problems arising from the variety and complexity of human languages. Breaking down language barriers is vital in enabling access to a number of recent technologies. This paper investigates the application of document classification methods to new Sinhalese datasets. This language is geographically isolated and rich with many of its own unique features. We will examine the interpretability of the classification models with a particular focus on the use of evolved Lucene search queries generated using a Genetic Algorithm (GA) as a method of document classification. We will compare the accuracy and interpretability of these search queries with other popular classifiers. The results are promising and are roughly in line with previous work on English language datasets.

Keywords: evolved search queries, Sinhala document classification, Lucene Sinhala analyzer, interpretable text classification, genetic algorithm

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Authors: Linh Nguyen Tung, Anh Truong Viet, Nghien Nguyen Ba, Chuong Trinh Trong

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This paper proposes a reconfiguration methodology based on a continuous genetic algorithm (CGA) and particle swarm optimization (PSO) for minimizing active power loss and minimizing voltage deviation. Both algorithms are adapted using graph theory to generate feasible individuals, and the modified crossover is used for continuous variable of CGA. To demonstrate the performance and effectiveness of the proposed methods, a comparative analysis of CGA with PSO for network reconfiguration, on 33-node and 119-bus radial distribution system is presented. The simulation results have shown that both CGA and PSO can be used in the distribution network reconfiguration and CGA outperformed PSO with significant success rate in finding optimal distribution network configuration.

Keywords: distribution network reconfiguration, particle swarm optimization, continuous genetic algorithm, power loss reduction, voltage deviation

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Authors: Rikson Gultom

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Hate Speech and Abusive language on social media is difficult to detect, usually, it is detected after it becomes viral in cyberspace, of course, it is too late for prevention. An early detection system that has a fairly good accuracy is needed so that it can reduce conflicts that occur in society caused by postings on social media that attack individuals, groups, and governments in Indonesia. The purpose of this study is to find an early detection model on Twitter social media using machine learning that has high accuracy from several machine learning methods studied. In this study, the support vector machine (SVM), Naïve Bayes (NB), and Random Forest Decision Tree (RFDT) methods were compared with the Support Vector machine with genetic algorithm (SVM-GA), Nave Bayes with genetic algorithm (NB-GA), and Random Forest Decision Tree with Genetic Algorithm (RFDT-GA). The study produced a comparison table for the accuracy of the hate speech and abusive language detection model, and presented it in the form of a graph of the accuracy of the six algorithms developed based on the Indonesian-language Twitter dataset, and concluded the best model with the highest accuracy.

Keywords: abusive language, hate speech, machine learning, optimization, social media

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Authors: Arash Rahmani, Ahmad Ghanbari, Abbas Baghernezhad, Babak Safaei

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In the design of parallel manipulators, usually mean value of a dexterity measure over the workspace volume is considered as the objective function to be used in optimization algorithms. The mentioned indexes in a hybrid parallel manipulator (HPM) are quite complicated to solve thanks to infinite solutions for every point within the workspace of the redundant manipulators. In this paper, spatial isotropic design axioms are extended as a well-known method for optimum design of manipulators. An upper limit for the isotropy measure of HPM is calculated and instead of computing and minimizing isotropy measure, minimizing the obtained limit is considered. To this end, two different objective functions are suggested which are obtained from objective functions of comprising modules. Finally, by using genetic algorithm (GA), the best geometric parameters for a specific hybrid parallel robot which is composed of two modified Gough-Stewart platforms (MGSP) are achieved.

Keywords: hybrid manipulator, spatial isotropy, genetic algorithm, optimum design

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Authors: Chaouch Souad, Abdou Latifa, Larbi Chrifi Alaoui

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This paper deals with sensorless backstepping control of induction motor using MRAS technique associated to sliding mode approach. A high order genetic algorithm structure is used to approximate a control law designed by the Backstepping technique, and to find the best parameters globally optimized. However, the Backstepping control approach is unsuitable for high performance applications because the need of a speed sensor for increased accuracy and the absence of any error decay mechanism. In this paper a nonlinear observer, obtained by combining sliding mode structure and model reference adaptive system (MRAS), is designed for the rotor flux and rotor speed estimations. To validate the proposed method, the results are presented for showing the improved drive characteristics and performances.

Keywords: Backstepping Control, Induction Motor, Genetic Algorithm, Sliding Mode observer

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Authors: N. Nalini, Lokesh B. Bhajantri

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In Distributed Sensor Networks, the sensor nodes are prone to failure due to energy depletion and some other reasons. In this regard, fault tolerance of network is essential in distributed sensor environment. Energy efficiency, network or topology control and fault-tolerance are the most important issues in the development of next-generation Distributed Sensor Networks (DSNs). This paper proposes a node fault detection and recovery using Genetic Algorithm (GA) in DSN when some of the sensor nodes are faulty. The main objective of this work is to provide fault tolerance mechanism which is energy efficient and responsive to network using GA, which is used to detect the faulty nodes in the network based on the energy depletion of node and link failure between nodes. The proposed fault detection model is used to detect faults at node level and network level faults (link failure and packet error). Finally, the performance parameters for the proposed scheme are evaluated.

Keywords: distributed sensor networks, genetic algorithm, fault detection and recovery, information technology

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Authors: Maamar Ali Saud Al Tobi, Geraint Bevan, K. P. Ramachandran, Peter Wallace, David Harrison

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Centrifugal pumps are complex machines which can experience different types of fault. Condition monitoring can be used in centrifugal pump fault detection through vibration analysis for mechanical and hydraulic forces. Vibration analysis methods have the potential to be combined with artificial intelligence systems where an automatic diagnostic method can be approached. An automatic fault diagnosis approach could be a good option to minimize human error and to provide a precise machine fault classification. This work aims to introduce an approach to centrifugal pump fault diagnosis based on artificial intelligence and genetic algorithm systems. An overview of the future works, research methodology and proposed experimental setup is presented and discussed. The expected results and outcomes based on the experimental work are illustrated.

Keywords: centrifugal pump setup, vibration analysis, artificial intelligence, genetic algorithm

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Authors: Henrik L. Funke, Lars Ulke-Winter, Sandra Gelbrich, Lothar Kroll

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This work reports about an approach for an automatic adaptation of concrete formulations based on genetic algorithms (GA) to optimize a wide range of different fit-functions. In order to achieve the goal, a method was developed which provides a numerical description of a fibre reinforced concrete (FRC) mixture regarding the production technology and the property spectrum of the concrete. In a first step, the FRC mixture with seven fixed components was characterized by varying amounts of the components. For that purpose, ten concrete mixtures were prepared and tested. The testing procedure comprised flow spread, compressive and bending tensile strength. The analysis and approximation of the determined data was carried out by GAs. The aim was to obtain a closed mathematical expression which best describes the given seven-point cloud of FRC by applying a Gene Expression Programming with Free Coefficients (GEP-FC) strategy. The seven-parametric FRC-mixtures model which is generated according to this method correlated well with the measured data. The developed procedure can be used for concrete mixtures finding closed mathematical expressions, which are based on the measured data.

Keywords: concrete design, fibre reinforced concrete, genetic algorithms, GEP-FC

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Authors: Saul A. Torres Z., Eduardo Liceaga C., Alfredo Arias M.

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Agriculture in the world falls within the main sources of economic and global needs, so care of crop is extremely important for owners and workers; one of the major causes of loss of product is the pest infection of different types of organisms. We seek to develop a UAV for agricultural spraying at a maximum altitude of 5000 meters above sea level, with a payload of 100 liters of fumigant. For the developing the aerodynamic design of the aircraft is using computational tools such as the "Vortex Lattice Athena" software, "MATLAB"," ANSYS FLUENT"," XFoil " package among others. Also methods are being used structured programming, exhaustive analysis of optimization methods and search. The results have a very low margin of error, and the multi- objective problems can be helpful for future developments. The program has 10 functions developed in MATLAB, these functions are related to each other to enable the development of design, and all these functions are controlled by the principal code "Master.m".

Keywords: aerodynamics design, optimization, algorithm genetic, multi-objective problem, stability, vortex

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