Machine Intelligent Research Labs MIR Labs

Anderson, United States

Machine Intelligent Research Labs MIR Labs

Anderson, United States
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Chen Y.,University of Jinan | Yang B.,University of Jinan | Meng Q.,University of Jinan | Zhao Y.,University of Jinan | Abraham A.,Machine Intelligent Research Labs MIR Labs
Information Sciences | Year: 2011

This paper presents a hybrid evolutionary method for identifying a system of ordinary differential equations (ODEs) to predict the small-time scale traffic measurements data. We used the tree-structure based evolutionary algorithm to evolve the architecture and a particle swarm optimization (PSO) algorithm to fine tune the parameters of the additive tree models for the system of ordinary differential equations. We also illustrate some experimental comparisons with genetic programming, gene expression programming and a feedforward neural network optimized using PSO algorithm. Experimental results reveal that the proposed method is feasible and efficient for forecasting the small-scale traffic measurements data. © 2010 Elsevier Inc. All rights reserved.


Zhang L.,University of Jinan | Chen Y.,University of Jinan | Abraham A.,Machine Intelligent Research Labs Mir Labs | Chen Z.,University of Jinan
Proceedings of the 2011 World Congress on Information and Communication Technologies, WICT 2011 | Year: 2011

In this paper, a novel method for improving flexible neural tree is proposed to classify the leukemia cancer data. The hybrid flexible neural tree with pre-defined instruction sets can be created and evolved. The structure and parameter of hybrid flexible neural tree are optimized using probabilistic incremental program evolution (PIPE) and particle swarm optimization (PSO) algorithm. The experimental results indicate that the proposed method illustrates feasible and efficient for the classifications of microarray data. © 2011 IEEE.


Thangaraj R.,University of Luxembourg | Pant M.,Indian Institute of Technology Roorkee | Abraham A.,VSB - Technical University of Ostrava | Abraham A.,Machine Intelligent Research Labs MIR Labs | Snasel V.,VSB - Technical University of Ostrava
International Journal of Innovative Computing, Information and Control | Year: 2012

Particle Swarm Optimization (PSO) is a population-based computational intelligence paradigm; it originated as a simulation of simplified social model of birds in a flock. The PSO algorithm is easy to implement and has been proven to be very competitive for solving diverse global optimization problems including both test and application problems in comparison to conventional methods and other meta-heuristics. In the present study, a new velocity vector is introduced in the BPSO algorithms and is analyzed on thirty six benchmark problems and three real life problems taken from the literature. The numerical results show that the incorporation of the proposed velocity vector helps in improving the performance of BPSO in terms of final objective function value, number of function evaluations and convergence rate. © 2012 ICIC International.


Thangaraj R.,Indian Institute of Technology Roorkee | Chelliah T.R.,Indian Institute of Technology Roorkee | Pant M.,Indian Institute of Technology Roorkee | Abraham A.,Machine Intelligent Research Labs MIR Labs | Bouvry P.,University of Luxembourg
Intelligent Systems Reference Library | Year: 2013

This chapter presents application of two popular Nature Inspired Algorithms (NIA); Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms for solving the optimization problems that arise in the field of electrical engineering. The main focus is on efficient utilization of electrical energy and the protection of transmission lines, the most important fields of optimization in electrical engineering having nonconvex characteristics with several equality and inequality constraints. The PSO and DE variants are applied to various electrical engineering problems including over-current relay settings in transmission lines, in-situ parameter estimation of electric motors, design and control of induction motors (IM) serving to process industries and proportional-integral (PI) controller tuning in variable speed drives. © Springer-Verlag Berlin Heidelberg 2013.


Raj C.T.,Indian Institute of Technology Roorkee | Thangaraj R.,Indian Institute of Technology Roorkee | Pant M.,Indian Institute of Technology Roorkee | Bouvry P.,University of Luxembourg | And 2 more authors.
Applied Artificial Intelligence | Year: 2012

This article deals with the design optimization of a squirrel-cage three-phase induction motor, selected as the driving power of spinning machines in the textile industry, using three newly developed versions of differential evolution (DE) algorithms called modified DE versions (CMDE, GMDE, and LMDE). Efficiency, which decides the operating or running cost of the motor (industry), is considered as the objective function. First, the algorithms are applied to design a general purpose motor with seven variables and nine performance-related parameters with their nominal values as constraints. To make the machine feasible, practically acceptable to serve in textile industries, and less costly to operate, certain constraints are modified in accordance with the demands of the spinning application. Comparison of the optimum designs with the industrial (existing) motor reveals that the motor designed by the proposed algorithms consumes less power input. © 2012 Copyright Taylor and Francis Group, LLC.


Thangaraj R.,University of Luxembourg | Pant M.,Indian Institute of Technology Roorkee | Bouvry P.,University of Luxembourg | Abraham A.,Machine Intelligent Research Labs Mir Labs
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2010

Stochastic (or probabilistic) programming is an optimization technique in which the constraints and/or the objective function of an optimization problem contains random variables. The mathematical models of these problems may follow any particular probability distribution for model coefficients. The objective here is to determine the proper values for model parameters influenced by random events. In this study, Differential Evolution (DE) and its two recent variants LDE1 and LDE2 are presented for solving multi objective linear stochastic programming (MOSLP) problems, having several conflicting objectives. The numerical results obtained by DE and its variants are compared with the available results from where it is observed that the DE and its variants significantly improve the quality of solution of the given considered problem in comparison with the quoted results in the literature. © 2010 Springer-Verlag.


Abraham A.,Machine Intelligent Research Labs MIR Labs | Abraham A.,VSB - Technical University of Ostrava | Jatoth R.K.,National Institute of Technology Warangal | Rajasekhar A.,National Institute of Technology Warangal
Journal of Computational and Theoretical Nanoscience | Year: 2012

Artificial Bee Colony Algorithm (ABCA) is a new population-based meta-heuristic approach inspired by the foraging behaviour of bees. This article describes an application of a novel Hybrid Differential Artificial Bee Colony Algorithm (HDABCA), which combines Differential Evolution strategy with Artificial Bee Colony algorithm. We illustrate the proposed method using several test functions and also compared with classical differential evolution algorithm and artificial bee colony algorithm. Simulation results illustrate that the proposed method is very efficient. Copyright © 2012 American Scientific Publishers.


Thangaraj R.,University of Luxembourg | Chelliah T.R.,University of Luxembourg | Bouvry P.,University of Luxembourg | Pant M.,Indian Institute of Technology Roorkee | Abraham A.,Machine Intelligent Research Labs Mir Labs
2010 International Conference on Computer Information Systems and Industrial Management Applications, CISIM 2010 | Year: 2010

This paper deals with the design optimization of a squirrel-cage three-phase induction motor, selected as the driving power of spinning machine in textile industry, using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Efficiency, which decides the operating or running cost of the motor (industry), is considered as objective function. First, the algorithms are applied to design a general purpose motor with seven variables and nine performance related parameters with their nominal values as constraints. To make the machine feasible, practically acceptable to serve in textile industries and less operating cost, certain constraints are modified in accordance with the demands in spinning application. Comparison of the optimum designs with the industrial (existing) motor reveals that the motor designed for textile load diagram consumes less power input. Economical analysis is also given. ©2010 IEEE.

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