Conference Papers

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Subject: search.filters.subject.Genetic algorithm

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    A combined genetic algorithm and Sugeno fuzzy logic based approach for on-line tuning in pH process
    (2008) Valarmathi, K.; Devaraj, D.; Radhakrishnan, T.K.
    Computing the optimal values of Proportional Integral derivative (PID) control gains is an important task in the design of PID controller. This paper presents the application of Sugeno fuzzy model for on-line tuning of PID controllers in pH process. The optimal PID controller parameters required to develop the Sugeno fuzzy model are estimated by genetic algorithm. The developed fuzzy controller can give the PID parameters on line for different operating conditions. The suitability of the proposed approach has been demonstrated through computer simulation using MATLAB Simulink. © 2008 IEEE.
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    Coordinated voltage control in 3 phase unbalanced distribution system with multiple regulators using genetic algorithm
    (Elsevier Ltd, 2012) Shivarudraswamy, R.; Gaonkar, D.N.; Nayak, S.K.
    The continued interest in the distributed generation in recent years is leading to a number of generators connected to distribution network. The introduction of DG in the distribution system changes the operating features and has significant technical impact. One of the main obstacle for high DG penetration in the distribution feeder is the voltage rise effect. Present network design practice is to limit the generator capacity to a level at which the upper voltage limit is not exceeding; this reduces the efficiency of DG system. This paper presents an efficient algorithm for voltage control in 3 phase unbalanced system with multiple voltage regulators. The genetic algorithm is successfully applied on 13 bus unbalanced radial system for different load conditions to control the voltage level. The voltage profiles are improved & are within the specified limits with optimal setting of voltage regulators like Load ratio transformer (LRT), Static Var Compensator (SVC), Shunt Capacitor (SC) and DGs reactive power for providing smooth voltage profiles at all the load conditions. © 2011 Published by Elsevier Ltd.
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    Multi-objective Genetic Algorithm for efficient point matching in multi-sensor satellite image
    (2012) Senthilnath, J.; Omkar, S.N.; Mani, V.; Kalro, N.P.; Diwakar, P.G.
    This paper investigates a new approach for point matching in multi-sensor satellite images. The feature points are matched using multi-objective optimization (angle criterion and distance condition) based on Genetic Algorithm (GA). This optimization process is more efficient as it considers both the angle criterion and distance condition to incorporate multi-objective switching in the fitness function. This optimization process helps in matching three corresponding corner points detected in the reference and sensed image and thereby using the affine transformation, the sensed image is aligned with the reference image. From the results obtained, the performance of the image registration is evaluated and it is concluded that the proposed approach is efficient. © 2012 IEEE.
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    Spectral-spatial MODIS image analysis using swarm intelligence algorithms and region based segmentation for flood assessment
    (Springer Verlag service@springer.de, 2013) Senthilnath, J.; Vikram Shenoy, H.; Omkar, S.N.; Mani, V.
    This paper discusses an approach for river mapping and flood evaluation based on multi-temporal time-series analysis of satellite images utilizing pixel spectral information for image clustering and region based segmentation for extracting water covered regions. MODIS satellite images are analyzed at two stages: before flood and during flood. Multi-temporal MODIS images are processed in two steps. In the first step, clustering algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to distinguish the water regions from the non-water based on spectral information. These algorithms are chosen since they are quite efficient in solving multi-modal optimization problems. These classified images are then segmented using spatial features of the water region to extract the river. From the results obtained, we evaluate the performance of the methods and conclude that incorporating region based image segmentation along with clustering algorithms provides accurate and reliable approach for the extraction of water covered region. © 2013 Springer.
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    Clustering using levy flight cuckoo search
    (Springer Verlag service@springer.de, 2013) Senthilnath, J.; Das, V.; Omkar, S.N.; Mani, V.
    In this paper, a comparative study is carried using three nature-inspired algorithms namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Cuckoo Search (CS) on clustering problem. Cuckoo search is used with levy flight. The heavy-tail property of levy flight is exploited here. These algorithms are used on three standard benchmark datasets and one real-time multi-spectral satellite dataset. The results are tabulated and analysed using various techniques. Finally we conclude that under the given set of parameters, cuckoo search works efficiently for majority of the dataset and levy flight plays an important role. © 2013 Springer.
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    Location management in mobile computing using swarm intelligence techniques
    (Springer Verlag service@springer.de, 2014) Goel, N.; Senthilnath, J.; Omkar, S.N.; Mani, V.
    Location management is an important and complex issue in mobile computing. Location management problem can be solved by partitioning the network into location areas such that the total cost, i.e., sum of handoff (update) cost and paging cost is minimum. Finding the optimal number of location areas and the corresponding configuration of the partitioned network is NP-complete problem. In this paper, we present two swarm intelligence algorithms namely genetic algorithm (GA) and artificial bee colony (ABC) to obtain minimum cost in the location management problem. We compare the performance of the swarm intelligence algorithms and the results show that ABC give better optimal solution to locate the optimal solution. © Springer India 2014.
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    Generating test data for path coverage based testing using genetic algorithms
    (Springer Verlag service@springer.de, 2014) Panda, M.; Mohapatra, D.P.
    In this paper, we have developed an approach to generate test data for path coverage based testing using genetic algorithm. We have used control flow graph and cyclomatic complexity of the example program to find out the number of feasible paths present in the program and compared it with the actual number of paths covered by genetic algorithm. We have used genetic algorithm for generating test data automatically. We have shown that our algorithm is giving cent percent coverage, successfully covering all feasible paths. In our approach, we have observed that genetic algorithm is much more effective in generating test data within less time period, giving better coverage. © Springer India 2014.
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    A new SIFT matching criteria in a genetic algorithm framework for registering multisensory satellite imagery
    (Association for Computing Machinery acmhelp@acm.org, 2014) Senthilnath, J.; Prasad, R.
    Synthetic Aperture Radar (SAR) images are efficient and reliable source of information in extraction of damaged regions in case of floods. In assessment of damage accurately due to floods, image registration of optical (before-flood) and SAR images (after-flood) has to be carried out efficiently. In this paper, we discuss a robust multi-sensor image registration algorithm using scale invariant feature points for keypoint extraction. For matching the keypoints, a multi-objective genetic algorithm is developed with angle, distance and vicinity criterion as the fitness functions. This optimization process helps in matching the scale invariant feature points. From the obtained results, the performance of the image registration is evaluated and it is concluded that the proposed approach is efficient. © 2014 ACM.
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    A novel family genetic approach for virtual machine allocation
    (Elsevier B.V., 2015) Joseph, C.T.; Chandrasekaran, K.; Cyriac, R.
    The concept of virtualization forms the heart of systems like the Cloud and Grid. Efficiency of systems that employ virtualization greatly depends on the efficiency of the technique used to allocate the virtual machines to suitable hosts. The literature contains many evolutionary approaches to solve the virtual machine allocation problem, a broad category of which employ Genetic Algorithm. This paper proposes a novel technique to allocate virtual machines using the Family Gene approach. Experimental analysis proves that the proposed approach reduces energy consumption and the rate of migrations, and hence offers much scope for future research. © 2015 Published by Elsevier B.V.
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    Optimal GA based SMC with adaptive PID sliding surface for robot manipulator
    (Institute of Electrical and Electronics Engineers Inc., 2015) Vijay, M.; Jena, D.
    Different types of robotic manipulator controllers are developed to acquire dynamic properties and improve the global stability. In this paper a control strategy for robotic manipulator based on the coupling of the Artificial Neuro Fuzzy Inference System (ANFIS) with sliding mode control (SMC) approach has been presented. Initially, the Proportional Integral Derivative (PID) controller has developed for three different control strategies (IATE, ISE and ISTE) using GA. SMC has developed for best optimal criterion by using GA. The main objectives of these controller are to provide stability, good disturbance rejection and small tracking error. Finally, we have trained an ANFIS network, which can generate the adaptive PID control signal to the SMC of robot manipulator. The stability of the system is guaranteed by the checking of the Lyapunov stability theorem. Numerical simulations using the dynamic model of 2 DOF planner rigid robot manipulator with input torque disturbance shows the effectiveness in trajectory tracking problem and disturbance rejection. The simulation results of these controllers are compared with various torque disturbances in terms of path tracking and disturbance rejection. The proposed ANFIS adaptive SMC controller can achieve favorable tracking performance and it is robust with regard to disturbances in input torque. © 2014 IEEE.