Search results for: Genetic programming
953 Using Genetic Algorithms to Outline Crop Rotations and a Cropping-System Model
Authors: Nicolae Bold, Daniel Nijloveanu
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The idea of cropping-system is a method used by farmers. It is an environmentally-friendly method, protecting the natural resources (soil, water, air, nutritive substances) and increase the production at the same time, taking into account some crop particularities. The combination of this powerful method with the concepts of genetic algorithms results into a possibility of generating sequences of crops in order to form a rotation. The usage of this type of algorithms has been efficient in solving problems related to optimization and their polynomial complexity allows them to be used at solving more difficult and various problems. In our case, the optimization consists in finding the most profitable rotation of cultures. One of the expected results is to optimize the usage of the resources, in order to minimize the costs and maximize the profit. In order to achieve these goals, a genetic algorithm was designed. This algorithm ensures the finding of several optimized solutions of cropping-systems possibilities which have the highest profit and, thus, which minimize the costs. The algorithm uses genetic-based methods (mutation, crossover) and structures (genes, chromosomes). A cropping-system possibility will be considered a chromosome and a crop within the rotation is a gene within a chromosome. Results about the efficiency of this method will be presented in a special section. The implementation of this method would bring benefits into the activity of the farmers by giving them hints and helping them to use the resources efficiently.Keywords: Genetic algorithm, chromosomes, genes, cropping, agriculture.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1592952 Two Points Crossover Genetic Algorithm for Loop Layout Design Problem
Authors: Xu LiYun, Briand Florent, Fan GuoLiang
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The loop-layout design problem (LLDP) aims at optimizing the sequence of positioning of the machines around the cyclic production line. Traffic congestion is the usual criteria to minimize in this type of problem, i.e. the number of additional cycles spent by each part in the network until the completion of its required routing sequence of machines. This paper aims at applying several improvements mechanisms such as a positioned-based crossover operator for the Genetic Algorithm (GA) called a Two Points Crossover (TPC) and an offspring selection process. The performance of the improved GA is measured using well-known examples from literature and compared to other evolutionary algorithms. Good results show that GA can still be competitive for this type of problem against more recent evolutionary algorithms.Keywords: Crossover, genetic algorithm, layout design problem, loop-layout, manufacturing optimization.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 830951 A Dual Method for Solving General Convex Quadratic Programs
Authors: Belkacem Brahmi, Mohand Ouamer Bibi
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In this paper, we present a new method for solving quadratic programming problems, not strictly convex. Constraints of the problem are linear equalities and inequalities, with bounded variables. The suggested method combines the active-set strategies and support methods. The algorithm of the method and numerical experiments are presented, while comparing our approach with the active set method on randomly generated problems.
Keywords: Convex quadratic programming, dual support methods, active set methods.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1876950 Synthesis of Logic Circuits Using Fractional-Order Dynamic Fitness Functions
Authors: Cecília Reis, J. A. Tenreiro Machado, J. Boaventura Cunha
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This paper analyses the performance of a genetic algorithm using a new concept, namely a fractional-order dynamic fitness function, for the synthesis of combinational logic circuits. The experiments reveal superior results in terms of speed and convergence to achieve a solution.
Keywords: Circuit design, fractional-order systems, genetic algorithms, logic circuits
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1722949 Genetic Algorithm Parameters Optimization for Bi-Criteria Multiprocessor Task Scheduling Using Design of Experiments
Authors: Sunita Dhingra, Satinder Bal Gupta, Ranjit Biswas
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Multiprocessor task scheduling is a NP-hard problem and Genetic Algorithm (GA) has been revealed as an excellent technique for finding an optimal solution. In the past, several methods have been considered for the solution of this problem based on GAs. But, all these methods consider single criteria and in the present work, minimization of the bi-criteria multiprocessor task scheduling problem has been considered which includes weighted sum of makespan & total completion time. Efficiency and effectiveness of genetic algorithm can be achieved by optimization of its different parameters such as crossover, mutation, crossover probability, selection function etc. The effects of GA parameters on minimization of bi-criteria fitness function and subsequent setting of parameters have been accomplished by central composite design (CCD) approach of response surface methodology (RSM) of Design of Experiments. The experiments have been performed with different levels of GA parameters and analysis of variance has been performed for significant parameters for minimisation of makespan and total completion time simultaneously.
Keywords: Multiprocessor task scheduling, Design of experiments, Genetic Algorithm, Makespan, Total completion time.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2822948 Lego Mindstorms as a Simulation of Robotic Systems
Authors: Miroslav Popelka, Jakub Nožička
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In this paper we deal with using Lego Mindstorms in simulation of robotic systems with respect to cost reduction. Lego Mindstorms kit contains broad variety of hardware components which are required to simulate, program and test the robotics systems in practice. Algorithm programming went in development environment supplied together with Lego kit as in programming language C# as well. Algorithm following the line, which we dealt with in this paper, uses theoretical findings from area of controlling circuits. PID controller has been chosen as controlling circuit whose individual components were experimentally adjusted for optimal motion of robot tracking the line. Data which are determined to process by algorithm are collected by sensors which scan the interface between black and white surfaces followed by robot. Based on discovered facts Lego Mindstorms can be considered for low-cost and capable kit to simulate real robotics systems.
Keywords: LEGO Mindstorms, PID controller, low-cost robotics systems, line follower, sensors, programming language C#, EV3 Home Edition Software.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3820947 Cost Based Warranty Optimisation Using Genetic Algorithm
Authors: Dragan D. Stamenkovic, Vladimir M. Popovic
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Warranty is a powerful marketing tool for the manufacturer and a good protection for both the manufacturer and the customer. However, warranty always involves additional costs to the manufacturer, which depend on product reliability characteristics and warranty parameters. This paper presents an approach to optimisation of warranty parameters for known product failure distribution to reduce the warranty costs to the manufacturer while retaining the promotional function of the warranty. Combination free replacement and pro-rata warranty policy is chosen as a model and the length of free replacement period and pro-rata policy period are varied, as well as the coefficients that define the pro-rata cost function. Multiparametric warranty optimisation is done by using genetic algorithm. Obtained results are guideline for the manufacturer to choose the warranty policy that minimises the costs and maximises the profit.Keywords: costs, genetic algorithm, optimisation, warranty.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1685946 Mathematical Rescheduling Models for Railway Services
Authors: Zuraida Alwadood, Adibah Shuib, Norlida Abd Hamid
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This paper presents the review of past studies concerning mathematical models for rescheduling passenger railway services, as part of delay management in the occurrence of railway disruption. Many past mathematical models highlighted were aimed at minimizing the service delays experienced by passengers during service disruptions. Integer programming (IP) and mixed-integer programming (MIP) models are critically discussed, focusing on the model approach, decision variables, sets and parameters. Some of them have been tested on real-life data of railway companies worldwide, while a few have been validated on fictive data. Based on selected literatures on train rescheduling, this paper is able to assist researchers in the model formulation by providing comprehensive analyses towards the model building. These analyses would be able to help in the development of new approaches in rescheduling strategies or perhaps to enhance the existing rescheduling models and make them more powerful or more applicable with shorter computing time.
Keywords: Mathematical modelling, Mixed-integer programming, Railway rescheduling, Service delays.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3232945 A New Fuzzy Mathematical Model in Recycling Collection Networks: A Possibilistic Approach
Authors: B. Vahdani, R. Tavakkoli-Moghaddam, A. Baboli, S. M. Mousavi
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Focusing on the environmental issues, including the reduction of scrap and consumer residuals, along with the benefiting from the economic value during the life cycle of goods/products leads the companies to have an important competitive approach. The aim of this paper is to present a new mixed nonlinear facility locationallocation model in recycling collection networks by considering multi-echelon, multi-suppliers, multi-collection centers and multifacilities in the recycling network. To make an appropriate decision in reality, demands, returns, capacities, costs and distances, are regarded uncertain in our model. For this purpose, a fuzzy mathematical programming-based possibilistic approach is introduced as a solution methodology from the recent literature to solve the proposed mixed-nonlinear programming model (MNLP). The computational experiments are provided to illustrate the applicability of the designed model in a supply chain environment and to help the decision makers to facilitate their analysis.
Keywords: Location-allocation model, recycling collection networks, fuzzy mathematical programming.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2085944 A New Approach to Polynomial Neural Networks based on Genetic Algorithm
Authors: S. Farzi
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Recently, a lot of attention has been devoted to advanced techniques of system modeling. PNN(polynomial neural network) is a GMDH-type algorithm (Group Method of Data Handling) which is one of the useful method for modeling nonlinear systems but PNN performance depends strongly on the number of input variables and the order of polynomial which are determined by trial and error. In this paper, we introduce GPNN (genetic polynomial neural network) to improve the performance of PNN. GPNN determines the number of input variables and the order of all neurons with GA (genetic algorithm). We use GA to search between all possible values for the number of input variables and the order of polynomial. GPNN performance is obtained by two nonlinear systems. the quadratic equation and the time series Dow Jones stock index are two case studies for obtaining the GPNN performance.Keywords: GMDH, GPNN, GA, PNN.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2083943 The Performance of Genetic Algorithm for Synchronized Chaotic Chen System in CDMA Satellite Channel
Authors: Salah Salmi, Karim Kemih, Malek Benslama
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Synchronization is a difficult problem in CDMA satellite communications. Due to the influence of additive noise and fading in the mobile channel, it is not easy to keep up with the attenuation and offset. This paper considers a recently proposed approach to solve the problem of synchronization chaotic Chen system in CDMA satellite communication in the presence of constant attenuation and offset. An analytic algorithm that provides closed form channel and carrier offset estimates is presented. The principle of this approach is based on adding a compensation block before the receiver to compensate the distortion of the imperfect channel by using genetic algorithm. The resultants presented, show that the receiver is able to recover rapidly the synchronization with the transmitter.Keywords: Chaotic Chen system, genetic algorithm, Synchronization, CDMA
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1519942 A Genetic Algorithm Approach Considering Zero Injection Bus Constraint Modeling for Optimal Phasor Measurement Unit Placement
Authors: G. Chandana Sushma, T. R. Jyothsna
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This paper presents optimal Phasor Measurement Unit (PMU) Placement in network using a genetic algorithm approach as it is infeasible and require high installation cost to place PMUs at every bus in network. This paper proposes optimal PMU allocation considering observability and redundancy utilizing Genetic Algorithm (GA) approach. The nonlinear constraints of buses are modeled to give accurate results. Constraints associated with Zero Injection (ZI) buses and radial buses are modeled to optimize number of locations for PMU placement. GA is modeled with ZI bus constraints to minimize number of locations without losing complete observability. Redundancy of every bus in network is computed to show optimum redundancy of complete system network. The performance of method is measured by Bus Observability Index (BOI) and Complete System Observability Performance Index (CSOPI). MATLAB simulations are carried out on IEEE -14, -30 and -57 bus-systems and compared with other methods in literature survey to show the effectiveness of the proposed approach.
Keywords: Constraints, genetic algorithm, observability, phasor measurement units, redundancy, synchrophasors, zero injection bus.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 771941 Parallezation Protein Sequence Similarity Algorithms using Remote Method Interface
Authors: Mubarak Saif Mohsen, Zurinahni Zainol, Rosalina Abdul Salam, Wahidah Husain
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One of the major problems in genomic field is to perform sequence comparison on DNA and protein sequences. Executing sequence comparison on the DNA and protein data is a computationally intensive task. Sequence comparison is the basic step for all algorithms in protein sequences similarity. Parallel computing is an attractive solution to provide the computational power needed to speedup the lengthy process of the sequence comparison. Our main research is to enhance the protein sequence algorithm using dynamic programming method. In our approach, we parallelize the dynamic programming algorithm using multithreaded program to perform the sequence comparison and also developed a distributed protein database among many PCs using Remote Method Interface (RMI). As a result, we showed how different sizes of protein sequences data and computation of scoring matrix of these protein sequence on different number of processors affected the processing time and speed, as oppose to sequential processing.
Keywords: Protein sequence algorithm, dynamic programming algorithm, multithread
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1888940 Auto Tuning PID Controller based on Improved Genetic Algorithm for Reverse Osmosis Plant
Authors: Jin-Sung Kim, Jin-Hwan Kim, Ji-Mo Park, Sung-Man Park, Won-Yong Choe, Hoon Heo
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An optimal control of Reverse Osmosis (RO) plant is studied in this paper utilizing the auto tuning concept in conjunction with PID controller. A control scheme composing an auto tuning stochastic technique based on an improved Genetic Algorithm (GA) is proposed. For better evaluation of the process in GA, objective function defined newly in sense of root mean square error has been used. Also in order to achieve better performance of GA, more pureness and longer period of random number generation in operation are sought. The main improvement is made by replacing the uniform distribution random number generator in conventional GA technique to newly designed hybrid random generator composed of Cauchy distribution and linear congruential generator, which provides independent and different random numbers at each individual steps in Genetic operation. The performance of newly proposed GA tuned controller is compared with those of conventional ones via simulation.Keywords: Genetic Algorithm, Auto tuning, Hybrid random number generator, Reverse Osmosis, PID controller
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3112939 On the Application of Meta-Design Techniques in Hardware Design Domain
Authors: R. Damaševičius
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System-level design based on high-level abstractions is becoming increasingly important in hardware and embedded system design. This paper analyzes meta-design techniques oriented at developing meta-programs and meta-models for well-understood domains. Meta-design techniques include meta-programming and meta-modeling. At the programming level of design process, metadesign means developing generic components that are usable in a wider context of application than original domain components. At the modeling level, meta-design means developing design patterns that describe general solutions to the common recurring design problems, and meta-models that describe the relationship between different types of design models and abstractions. The paper describes and evaluates the implementation of meta-design in hardware design domain using object-oriented and meta-programming techniques. The presented ideas are illustrated with a case study.Keywords: Design patterns, meta-design, meta-modeling, metaprogramming.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2292938 Welding Process Selection for Storage Tank by Integrated Data Envelopment Analysis and Fuzzy Credibility Constrained Programming Approach
Authors: Rahmad Wisnu Wardana, Eakachai Warinsiriruk, Sutep Joy-A-Ka
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Selecting the most suitable welding process usually depends on experiences or common application in similar companies. However, this approach generally ignores many criteria that can be affecting the suitable welding process selection. Therefore, knowledge automation through knowledge-based systems will significantly improve the decision-making process. The aims of this research propose integrated data envelopment analysis (DEA) and fuzzy credibility constrained programming approach for identifying the best welding process for stainless steel storage tank in the food and beverage industry. The proposed approach uses fuzzy concept and credibility measure to deal with uncertain data from experts' judgment. Furthermore, 12 parameters are used to determine the most appropriate welding processes among six competitive welding processes.
Keywords: Welding process selection, data envelopment analysis, fuzzy credibility constrained programming, storage tank.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 788937 Hybridizing Genetic Algorithm with Biased Chance Local Search
Authors: Mehdi Basikhasteh, Mohamad A. Movafaghpour
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This paper explores university course timetabling problem. There are several characteristics that make scheduling and timetabling problems particularly difficult to solve: they have huge search spaces, they are often highly constrained, they require sophisticated solution representation schemes, and they usually require very time-consuming fitness evaluation routines. Thus standard evolutionary algorithms lack of efficiency to deal with them. In this paper we have proposed a memetic algorithm that incorporates the problem specific knowledge such that most of chromosomes generated are decoded into feasible solutions. Generating vast amount of feasible chromosomes makes the progress of search process possible in a time efficient manner. Experimental results exhibit the advantages of the developed Hybrid Genetic Algorithm than the standard Genetic Algorithm.Keywords: University Course Timetabling, Memetic Algorithm, Biased Chance Assignment, Optimization.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1684936 A Review of Genetic Algorithm Optimization: Operations and Applications to Water Pipeline Systems
Authors: I. Abuiziah, N. Shakarneh
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Genetic Algorithm (GA) is a powerful technique for solving optimization problems. It follows the idea of survival of the fittest - Better and better solutions evolve from previous generations until a near optimal solution is obtained. GA uses the main three operations, the selection, crossover and mutation to produce new generations from the old ones. GA has been widely used to solve optimization problems in many applications such as traveling salesman problem, airport traffic control, information retrieval (IR), reactive power optimization, job shop scheduling, and hydraulics systems such as water pipeline systems. In water pipeline systems we need to achieve some goals optimally such as minimum cost of construction, minimum length of pipes and diameters, and the place of protection devices. GA shows high performance over the other optimization techniques, moreover, it is easy to implement and use. Also, it searches a limited number of solutions.
Keywords: Genetic Algorithm, optimization, pipeline systems, selection, cross over.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 5082935 Determination of Moisture Diffusivity of AACin Drying Phase using Genetic Algorithm
Authors: Jan Kočí, Jiří Maděra, Miloš Jerman, Robert Černý
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The current practice of determination of moisture diffusivity of building materials under laboratory conditions is predominantly aimed at the absorption phase. The main reason is the simplicity of the inverse analysis of measured moisture profiles. However, the liquid moisture transport may exhibit significant hysteresis. Thus, the moisture diffusivity should be different in the absorption (wetting) and desorption (drying) phase. In order to bring computer simulations of hygrothermal performance of building materials closer to the reality, it is then necessary to find new methods for inverse analysis which could be used in the desorption phase as well. In this paper we present genetic algorithm as a possible method of solution of the inverse problem of moisture transport in desorption phase. Its application is demonstrated for AAC as a typical building material.
Keywords: autoclaved aerated concrete, desorption, genetic algorithm, inverse analysis
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1581934 Optimization of Flexible Job Shop Scheduling Problem with Sequence Dependent Setup Times Using Genetic Algorithm Approach
Authors: Sanjay Kumar Parjapati, Ajai Jain
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This paper presents optimization of makespan for ‘n’ jobs and ‘m’ machines flexible job shop scheduling problem with sequence dependent setup time using genetic algorithm (GA) approach. A restart scheme has also been applied to prevent the premature convergence. Two case studies are taken into consideration. Results are obtained by considering crossover probability (pc = 0.85) and mutation probability (pm = 0.15). Five simulation runs for each case study are taken and minimum value among them is taken as optimal makespan. Results indicate that optimal makespan can be achieved with more than one sequence of jobs in a production order.
Keywords: Flexible Job Shop, Genetic Algorithm, Makespan, Sequence Dependent Setup Times.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3278933 Minimization of Non-Productive Time during 2.5D Milling
Authors: Satish Kumar, Arun Kumar Gupta, Pankaj Chandna
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In the modern manufacturing systems, the use of thermal cutting techniques using oxyfuel, plasma and laser have become indispensable for the shape forming of high quality complex components; however, the conventional chip removal production techniques still have its widespread space in the manufacturing industry. Both these types of machining operations require the positioning of end effector tool at the edge where the cutting process commences. This repositioning of the cutting tool in every machining operation is repeated several times and is termed as non-productive time or airtime motion. Minimization of this non-productive machining time plays an important role in mass production with high speed machining. As, the tool moves from one region to the other by rapid movement and visits a meticulous region once in the whole operation, hence the non-productive time can be minimized by synchronizing the tool movements. In this work, this problem is being formulated as a general travelling salesman problem (TSP) and a genetic algorithm approach has been applied to solve the same. For improving the efficiency of the algorithm, the GA has been hybridized with a noble special heuristic and simulating annealing (SA). In the present work a novel heuristic in the combination of GA has been developed for synchronization of toolpath movements during repositioning of the tool. A comparative analysis of new Meta heuristic techniques with simple genetic algorithm has been performed. The proposed metaheuristic approach shows better performance than simple genetic algorithm for minimization of nonproductive toolpath length. Also, the results obtained with the help of hybrid simulated annealing genetic algorithm (HSAGA) are also found better than the results using simple genetic algorithm only.
Keywords: Non-productive time, Airtime, 2.5 D milling, Laser cutting, Metaheuristic, Genetic Algorithm, Simulated Annealing.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2722932 Playing Games with Genetic Algorithms: Application on Price-QoS Competition in Telecommunications Market
Authors: M’hamed Outanoute, Mohamed Baslam, Belaid Bouikhalene
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The customers use the best compromise criterion between price and quality of service (QoS) to select or change their Service Provider (SP). The SPs share the same market and are competing to attract more customers to gain more profit. Due to the divergence of SPs interests, we believe that this situation is a non-cooperative game of price and QoS. The game converges to an equilibrium position known Nash Equilibrium (NE). In this work, we formulate a game theoretic framework for the dynamical behaviors of SPs. We use Genetic Algorithms (GAs) to find the price and QoS strategies that maximize the profit for each SP and illustrate the corresponding strategy in NE. In order to quantify how this NE point is performant, we perform a detailed analysis of the price of anarchy induced by the NE solution. Finally, we provide an extensive numerical study to point out the importance of considering price and QoS as a joint decision parameter.
Keywords: Pricing, QoS, Market share game, Genetic algorithms, Nash equilibrium, Learning, Price of anarchy.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1791931 Model Discovery and Validation for the Qsar Problem using Association Rule Mining
Authors: Luminita Dumitriu, Cristina Segal, Marian Craciun, Adina Cocu, Lucian P. Georgescu
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There are several approaches in trying to solve the Quantitative 1Structure-Activity Relationship (QSAR) problem. These approaches are based either on statistical methods or on predictive data mining. Among the statistical methods, one should consider regression analysis, pattern recognition (such as cluster analysis, factor analysis and principal components analysis) or partial least squares. Predictive data mining techniques use either neural networks, or genetic programming, or neuro-fuzzy knowledge. These approaches have a low explanatory capability or non at all. This paper attempts to establish a new approach in solving QSAR problems using descriptive data mining. This way, the relationship between the chemical properties and the activity of a substance would be comprehensibly modeled.Keywords: association rules, classification, data mining, Quantitative Structure - Activity Relationship.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1774930 A Nondominated Sorting Genetic Algorithm for Shortest Path Routing Problem
Authors: C. Chitra, P. Subbaraj
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The shortest path routing problem is a multiobjective nonlinear optimization problem with constraints. This problem has been addressed by considering Quality of service parameters, delay and cost objectives separately or as a weighted sum of both objectives. Multiobjective evolutionary algorithms can find multiple pareto-optimal solutions in one single run and this ability makes them attractive for solving problems with multiple and conflicting objectives. This paper uses an elitist multiobjective evolutionary algorithm based on the Non-dominated Sorting Genetic Algorithm (NSGA), for solving the dynamic shortest path routing problem in computer networks. A priority-based encoding scheme is proposed for population initialization. Elitism ensures that the best solution does not deteriorate in the next generations. Results for a sample test network have been presented to demonstrate the capabilities of the proposed approach to generate well-distributed pareto-optimal solutions of dynamic routing problem in one single run. The results obtained by NSGA are compared with single objective weighting factor method for which Genetic Algorithm (GA) was applied.
Keywords: Multiobjective optimization, Non-dominated Sorting Genetic Algorithm, Routing, Weighted sum.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1909929 Application of Neuro-Fuzzy Dynamic Programming to Improve the Reactive Power and Voltage Profile of a Distribution Substation
Authors: M. Tarafdar Haque, S. Najafi
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Improving the reactive power and voltage profile of a distribution substation is investigated in this paper. The purpose is to properly determination of the shunt capacitors on/off status and suitable tap changer (TC) position of a substation transformer. In addition, the limitation of secondary bus voltage, the maximum allowable number of switching operation in a day for on load tap changer and on/off status of capacitors are taken into account. To achieve these goals, an artificial neural network (ANN) is designed to provide preliminary scheduling. Input of ANN is active and reactive powers of transformer and its primary and secondary bus voltages. The output of ANN is capacitors on/off status and TC position. The preliminary schedule is further refined by fuzzy dynamic programming in order to reach the final schedule. The operation of proposed method in Q/V improving is compared with the results obtained by operator operation in a distribution substation.Keywords: Neuro-fuzzy, Dynamic programming, Reactive power, Voltage profile.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1616928 Transfer Knowledge from Multiple Source Problems to a Target Problem in Genetic Algorithm
Authors: Tami Alghamdi, Terence Soule
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To study how to transfer knowledge from multiple source problems to the target problem, we modeled the Transfer Learning (TL) process using Genetic Algorithms as the model solver. TL is the process that aims to transfer learned data from one problem to another problem. The TL process aims to help Machine Learning (ML) algorithms find a solution to the problems. The Genetic Algorithms (GA) give researchers access to information that we have about how the old problem is solved. In this paper, we have five different source problems, and we transfer the knowledge to the target problem. We studied different scenarios of the target problem. The results showed that combined knowledge from multiple source problems improves the GA performance. Also, the process of combining knowledge from several problems results in promoting diversity of the transferred population.
Keywords: Transfer Learning, Multiple Sources, Knowledge Transfer, Domain Adaptation, Source, Target.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 316927 Learning Process Enhancement for Robot Behaviors
Authors: Saeed Mohammed Baneamoon, Rosalina Abdul Salam, Abdullah Zawawi Hj. Talib
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Designing a simulated system and training it to optimize its tasks in simulated environment helps the designers to avoid problems that may appear when designing the system directly in real world. These problems are: time consuming, high cost, high errors percentage and low efficiency and accuracy of the system. The proposed system will investigate and improve the efficiency and accuracy of a simulated robot to choose correct behavior to perform its task. In this paper, machine learning, which uses genetic algorithm, is adopted. This type of machine learning is called genetic-based machine learning in which a distributed classifier system is used to improve the efficiency and accuracy of the robot. Consequently, it helps the robot to achieve optimal action.Keywords: Machine Learning, Genetic-Based MachineLearning, Learning Classifier System, Behaviors.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1517926 Design Optimization of Ferrocement-Laminated Plate Using Genetic Algorithm
Authors: M. Rokonuzzaman, Z. Gürdal
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This paper describes the design optimization of ferrocement-laminated plate made up of reinforcing steel wire mesh(es) and cement mortar. For the improvement of the designing process, the plate is modeled as a multi-layer medium, dividing the ferrocement plate into layers of mortar and ferrocement. The mortar layers are assumed to be isotropic in nature and the ferrocement layers are assumed to be orthotropic. The ferrocement layers are little stiffer, but much more costlier, than the mortar layers due the presence of steel wire mesh. The optimization is performed for minimum weight design of the laminate using a genetic algorithm. The optimum designs are discussed for different plate configurations and loadings, and it is compared with the worst designs obtained at the final generation. The paper provides a procedure for the designers in decision-making process.
Keywords: Buckling, Ferrocement-Laminated Plate, Genetic Algorithm, Plate Theory.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2168925 Genetic Algorithm Optimization of the Economical, Ecological and Self-Consumption Impact of the Energy Production of a Single Building
Authors: Ludovic Favre, Thibaut M. Schafer, Jean-Luc Robyr, Elena-Lavinia Niederhäuser
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This paper presents an optimization method based on genetic algorithm for the energy management inside buildings developed in the frame of the project Smart Living Lab (SLL) in Fribourg (Switzerland). This algorithm optimizes the interaction between renewable energy production, storage systems and energy consumers. In comparison with standard algorithms, the innovative aspect of this project is the extension of the smart regulation over three simultaneous criteria: the energy self-consumption, the decrease of greenhouse gas emissions and operating costs. The genetic algorithm approach was chosen due to the large quantity of optimization variables and the non-linearity of the optimization function. The optimization process includes also real time data of the building as well as weather forecast and users habits. This information is used by a physical model of the building energy resources to predict the future energy production and needs, to select the best energetic strategy, to combine production or storage of energy in order to guarantee the demand of electrical and thermal energy. The principle of operation of the algorithm as well as typical output example of the algorithm is presented.Keywords: Building’s energy, control system, energy management, modelling, genetic optimization algorithm, renewable energy, greenhouse gases, energy storage.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 767924 Using the PGAS Programming Paradigm for Biological Sequence Alignment on a Chip Multi-Threading Architecture
Authors: M. Bakhouya, S. A. Bahra, T. El-Ghazawi
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The Partitioned Global Address Space (PGAS) programming paradigm offers ease-of-use in expressing parallelism through a global shared address space while emphasizing performance by providing locality awareness through the partitioning of this address space. Therefore, the interest in PGAS programming languages is growing and many new languages have emerged and are becoming ubiquitously available on nearly all modern parallel architectures. Recently, new parallel machines with multiple cores are designed for targeting high performance applications. Most of the efforts have gone into benchmarking but there are a few examples of real high performance applications running on multicore machines. In this paper, we present and evaluate a parallelization technique for implementing a local DNA sequence alignment algorithm using a PGAS based language, UPC (Unified Parallel C) on a chip multithreading architecture, the UltraSPARC T1.Keywords: Partitioned Global Address Space, Unified Parallel C, Multicore machines, Multi-threading Architecture, Sequence alignment.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1380