International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
OPTIMAL GLOBAL THRESHOLD ESTIMATION USING STATISTICAL CHANGE-POINT DETECTIONsipij
Aim of this paper is reformulation of global image thresholding problem as a well-founded statistical
method known as change-point detection (CPD) problem. Our proposed CPD thresholding algorithm does
not assume any prior statistical distribution of background and object grey levels. Further, this method is
less influenced by an outlier due to our judicious derivation of a robust criterion function depending on
Kullback-Leibler (KL) divergence measure. Experimental result shows efficacy of proposed method
compared to other popular methods available for global image thresholding. In this paper we also propose
a performance criterion for comparison of thresholding algorithms. This performance criteria does not
depend on any ground truth image. We have used this performance criterion to compare the results of
proposed thresholding algorithm with most cited global thresholding algorithms in the literature.
Inventory Model with Price-Dependent Demand Rate and No Shortages: An Interva...orajjournal
In this paper, an interval-valued inventory optimization model is proposed. The model involves the price dependent
demand and no shortages. The input data for this model are not fixed, but vary in some real bounded intervals. The aim is to determine the optimal order quantity, maximizing the total profit and minimizing the holding cost subjecting to three constraints: budget constraint, space constraint, and
budgetary constraint on ordering cost of each item. We apply the linear fractional programming approach based on interval numbers. To apply this approach, a linear fractional programming problem is modeled with interval type uncertainty. This problem is further converted to an optimization problem with interval valued
objective function having its bounds as linear fractional functions. Two numerical examples in crisp
case and interval-valued case are solved to illustrate the proposed approach.
Optimization of Mechanical Design Problems Using Improved Differential Evolut...IDES Editor
Differential Evolution (DE) is a novel evolutionary
approach capable of handling non-differentiable, non-linear
and multi-modal objective functions. DE has been consistently
ranked as one of the best search algorithm for solving global
optimization problems in several case studies. This paper
presents an Improved Constraint Differential Evolution
(ICDE) algorithm for solving constrained optimization
problems. The proposed ICDE algorithm differs from
unconstrained DE algorithm only in the place of initialization,
selection of particles to the next generation and sorting the
final results. Also we implemented the new idea to five versions
of DE algorithm. The performance of ICDE algorithm is
validated on four mechanical engineering problems. The
experimental results show that the performance of ICDE
algorithm in terms of final objective function value, number
of function evaluations and convergence time.
LOGNORMAL ORDINARY KRIGING METAMODEL IN SIMULATION OPTIMIZATIONorajjournal
This paper presents a lognormal ordinary kriging (LOK) metamodel algorithm and its application to
optimize a stochastic simulation problem. Kriging models have been developed as an interpolation method
in geology. They have been successfully used for the deterministic simulation optimization (SO) problem. In
recent years, kriging metamodeling has attracted a growing interest with stochastic problems. SO
researchers have begun using ordinary kriging through global optimization in stochastic systems. The
goals of this study are to present LOK metamodel algorithm and to analyze the result of the application
step-by-step. The results show that LOK is a powerful alternative metamodel in simulation optimization
when the data are too skewed.
The Evaluation of Topsis and Fuzzy-Topsis Method for Decision Making System i...IRJET Journal
This document discusses using fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) as an analytical tool for decision making in data mining. Fuzzy TOPSIS extends the traditional TOPSIS method to handle uncertainties by using fuzzy set theory. It involves defining ratings and weights as linguistic variables represented by fuzzy numbers. The key steps are normalizing the fuzzy decision matrix, determining fuzzy positive and negative ideal solutions, calculating distances from the ideal solutions, and determining a closeness coefficient to rank the alternatives. The literature review discusses previous research applying fuzzy set concepts to TOPSIS to address limitations of crisp data in modeling real-world decision problems.
OPTIMIZATION TECHNIQUES
Optimization techniques are methods for achieving the best possible result under given constraints. There are various classical and advanced optimization methods. Classical methods include techniques for single-variable, multi-variable without constraints, and multi-variable with equality or inequality constraints using methods like Lagrange multipliers or Kuhn-Tucker conditions. Advanced methods include hill climbing, simulated annealing, genetic algorithms, and ant colony optimization. Optimization has applications in fields like engineering, business/economics, and pharmaceutical formulation to improve processes and outcomes under constraints.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
OPTIMAL GLOBAL THRESHOLD ESTIMATION USING STATISTICAL CHANGE-POINT DETECTIONsipij
Aim of this paper is reformulation of global image thresholding problem as a well-founded statistical
method known as change-point detection (CPD) problem. Our proposed CPD thresholding algorithm does
not assume any prior statistical distribution of background and object grey levels. Further, this method is
less influenced by an outlier due to our judicious derivation of a robust criterion function depending on
Kullback-Leibler (KL) divergence measure. Experimental result shows efficacy of proposed method
compared to other popular methods available for global image thresholding. In this paper we also propose
a performance criterion for comparison of thresholding algorithms. This performance criteria does not
depend on any ground truth image. We have used this performance criterion to compare the results of
proposed thresholding algorithm with most cited global thresholding algorithms in the literature.
Inventory Model with Price-Dependent Demand Rate and No Shortages: An Interva...orajjournal
In this paper, an interval-valued inventory optimization model is proposed. The model involves the price dependent
demand and no shortages. The input data for this model are not fixed, but vary in some real bounded intervals. The aim is to determine the optimal order quantity, maximizing the total profit and minimizing the holding cost subjecting to three constraints: budget constraint, space constraint, and
budgetary constraint on ordering cost of each item. We apply the linear fractional programming approach based on interval numbers. To apply this approach, a linear fractional programming problem is modeled with interval type uncertainty. This problem is further converted to an optimization problem with interval valued
objective function having its bounds as linear fractional functions. Two numerical examples in crisp
case and interval-valued case are solved to illustrate the proposed approach.
Optimization of Mechanical Design Problems Using Improved Differential Evolut...IDES Editor
Differential Evolution (DE) is a novel evolutionary
approach capable of handling non-differentiable, non-linear
and multi-modal objective functions. DE has been consistently
ranked as one of the best search algorithm for solving global
optimization problems in several case studies. This paper
presents an Improved Constraint Differential Evolution
(ICDE) algorithm for solving constrained optimization
problems. The proposed ICDE algorithm differs from
unconstrained DE algorithm only in the place of initialization,
selection of particles to the next generation and sorting the
final results. Also we implemented the new idea to five versions
of DE algorithm. The performance of ICDE algorithm is
validated on four mechanical engineering problems. The
experimental results show that the performance of ICDE
algorithm in terms of final objective function value, number
of function evaluations and convergence time.
LOGNORMAL ORDINARY KRIGING METAMODEL IN SIMULATION OPTIMIZATIONorajjournal
This paper presents a lognormal ordinary kriging (LOK) metamodel algorithm and its application to
optimize a stochastic simulation problem. Kriging models have been developed as an interpolation method
in geology. They have been successfully used for the deterministic simulation optimization (SO) problem. In
recent years, kriging metamodeling has attracted a growing interest with stochastic problems. SO
researchers have begun using ordinary kriging through global optimization in stochastic systems. The
goals of this study are to present LOK metamodel algorithm and to analyze the result of the application
step-by-step. The results show that LOK is a powerful alternative metamodel in simulation optimization
when the data are too skewed.
The Evaluation of Topsis and Fuzzy-Topsis Method for Decision Making System i...IRJET Journal
This document discusses using fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) as an analytical tool for decision making in data mining. Fuzzy TOPSIS extends the traditional TOPSIS method to handle uncertainties by using fuzzy set theory. It involves defining ratings and weights as linguistic variables represented by fuzzy numbers. The key steps are normalizing the fuzzy decision matrix, determining fuzzy positive and negative ideal solutions, calculating distances from the ideal solutions, and determining a closeness coefficient to rank the alternatives. The literature review discusses previous research applying fuzzy set concepts to TOPSIS to address limitations of crisp data in modeling real-world decision problems.
OPTIMIZATION TECHNIQUES
Optimization techniques are methods for achieving the best possible result under given constraints. There are various classical and advanced optimization methods. Classical methods include techniques for single-variable, multi-variable without constraints, and multi-variable with equality or inequality constraints using methods like Lagrange multipliers or Kuhn-Tucker conditions. Advanced methods include hill climbing, simulated annealing, genetic algorithms, and ant colony optimization. Optimization has applications in fields like engineering, business/economics, and pharmaceutical formulation to improve processes and outcomes under constraints.
A review of automatic differentiationand its efficient implementationssuserfa7e73
Automatic differentiation is a powerful tool for automatically calculating derivatives of mathematical functions and algorithms. It works by expressing the target function as a sequence of elementary operations and then applying the chain rule to differentiate each operation. This can be done using either forward or reverse mode. Forward mode calculates how changes in inputs propagate through the function to influence the outputs, while reverse mode calculates how changes in outputs backpropagate to influence the inputs. Both modes require performing the computation twice - once for the forward pass and once for the derivative pass. Careful implementation is required to make automatic differentiation efficient in terms of speed and memory usage.
This document presents new certified optimal solutions found by the Charibde algorithm for six difficult benchmark optimization problems. Charibde combines an evolutionary algorithm and interval-based methods in a cooperative framework. It has achieved optimality proofs for five bound-constrained problems and one nonlinearly constrained problem. These problems are highly multimodal and some had not been solved before even with approximate methods. The document also compares Charibde's performance to other state-of-the-art solvers, showing it is highly competitive while providing reliable optimality proofs.
AN EFFICIENT PARALLEL ALGORITHM FOR COMPUTING DETERMINANT OF NON-SQUARE MATRI...ijdpsjournal
One of the most significant challenges in Computing Determinant of Rectangular Matrices is high time
complexity of its algorithm. Among all definitions of determinant of rectangular matrices, Radic’s
definition has special features which make it more notable. But in this definition, C(N
M
) sub matrices of the
order m×m needed to be generated that put this problem in np-hard class. On the other hand, any row or
column reduction operation may hardly lead to diminish the volume of calculation. Therefore, in this paper
we try to present the parallel algorithm which can decrease the time complexity of computing the
determinant of non-square matrices to O(N).
This document contains a quiz on the topic of finite element analysis (FEA).
1. FEA involves discretizing a structure into smaller elements. Basic ideas of FEA were developed by aircraft engineers, and modern development occurred first in structural analysis.
2. Common methods of FEA include the force method and displacement method. Elements include 1D, 2D, and 3D elements like bars, beams, triangles, and quadrilaterals. Polynomial interpolation is often used.
3. FEA includes preprocessing like element discretization, solving using methods like the weighted residual or variational approach, and postprocessing of results. Software includes NISA and COSMOS. Analysis can be static or dynamic.
This document discusses different ways to classify optimization problems. It describes classifications based on:
1) The existence of constraints - problems are either constrained or unconstrained.
2) The nature of design variables - problems involve either parameter/static optimization or trajectory/dynamic optimization.
3) The physical structure - problems are either optimal control problems or non-optimal control problems.
4) The nature of equations - problems involve linear, nonlinear, geometric, or quadratic equations.
5) The permissible variable values - problems have either integer/discrete or real-valued variables.
6) The variable determinism - problems have either deterministic or stochastic/probabilistic variables.
7) The function
A Mathematical Model to Solve Nonlinear Initial and Boundary Value Problems b...IJERA Editor
In this paper, a novel method called Laplace-differential transform method (LDTM) is used to obtain an
approximate analytical solution for strong nonlinear initial and boundary value problems associated in
engineering phenomena. It is determined that the method works very well for the wide range of parameters and
an excellent agreement is demonstrated and discussed between the approximate solution and the exact one in
three examples. The most significant features of this method are its capability of handling non-linear boundary
value problems.
A NEW ALGORITHM FOR SOLVING FULLY FUZZY BI-LEVEL QUADRATIC PROGRAMMING PROBLEMSorajjournal
This paper is concerned with new method to find the fuzzy optimal solution of fully fuzzy bi-level non-linear (quadratic) programming (FFBLQP) problems where all the coefficients and decision variables of both objective functions and the constraints are triangular fuzzy numbers (TFNs). A new method is based on decomposed the given problem into bi-level problem with three crisp quadratic objective functions and bounded variables constraints. In order to often a fuzzy optimal solution of the FFBLQP problems, the concept of tolerance membership function is used to develop a fuzzy max-min decision model for generating satisfactory fuzzy solution for FFBLQP problems in which the upper-level decision maker (ULDM) specifies his/her objective functions and decisions with possible tolerances which are described by membership functions of fuzzy set theory. Then, the lower-level decision maker (LLDM) uses this preference information for ULDM and solves his/her problem subject to the ULDMs restrictions. Finally, the decomposed method is illustrated by numerical example.
The document summarizes four numerical methods commonly used in geomechanics:
1. The Distinct Element Method (DEM) explicitly models discontinuities.
2. The Discontinuous Deformation Analysis Method (DDA) can consider discontinuities explicitly or implicitly.
3. The Bonded Particle Method (BPM) models geomaterials as an assembly of discrete particles.
4. The Artificial Neural Network Method (ANN) is a data-driven modeling approach not classified as continuum or discontinuum.
The document provides a brief overview of the fundamental algorithms of each method and examples of their applications.
THE RESEARCH OF QUANTUM PHASE ESTIMATION ALGORITHMIJCSEA Journal
This document discusses phase estimation in quantum computing. It begins by introducing quantum Fourier transforms and how they are important for algorithms like Shor's algorithm. It then describes the phase estimation algorithm in detail, including how it uses two registers to estimate the phase of a quantum state and how the inverse quantum Fourier transform improves this estimate. Simulation results are presented that show the probability distribution of the estimated phase converging to the true value and how the probability of success increases with more qubits while computational costs rise polynomially. The paper concludes that the optimal number of qubits balances high success probability and low costs for phase estimation.
A MODIFIED VORTEX SEARCH ALGORITHM FOR NUMERICAL FUNCTION OPTIMIZATIONijaia
This document presents a modified version of the Vortex Search (VS) algorithm called the Modified Vortex Search (MVS) algorithm for numerical function optimization. The VS algorithm has the drawback that it can get trapped in local minima for functions with multiple local minima. The MVS algorithm addresses this by generating candidate solutions around multiple points at each iteration rather than a single point, allowing it to escape local minima more easily. Computational results on benchmark functions showed the MVS algorithm outperformed the original VS algorithm, as well as PSO2011 and ABC algorithms.
This document summarizes and analyzes the performance of Newton's method, BFGS method, and SR1 method for minimizing a quadratic and convex function. It finds that:
1) Newton's method performed the best, requiring fewer iterations and achieving greater accuracy than the other methods.
2) For constrained problems, the SR1 method achieved some success due to its flexibility in not always requiring a descent direction.
3) While Newton's method has the best theoretical convergence rate, quasi-Newton methods are more applicable to complex problems as hessian inversion becomes more computationally expensive.
4) When minimizing quadratic and convex functions, Newton's method generally performs better than the other tested methods. However, the best
Comparisons of linear goal programming algorithmsAlexander Decker
This document compares different algorithms for solving linear goal programming problems:
1) Lee's modified simplex algorithm from 1972 and Ignizio's sequential algorithm from 1976 are two commonly used algorithms but require many columns and objective function rows, adding to computational time.
2) Orumie and Ebong developed a new algorithm in 2011 utilizing modified simplex procedures that has better computational times than existing algorithms for all problems tested.
3) The document reviews several other goal programming algorithms and finds that Orumie and Ebong's new method provides the best reduction in computational time for solving the problems.
GENETIC ALGORITHM FOR FUNCTION APPROXIMATION: AN EXPERIMENTAL INVESTIGATIONijaia
Function Approximation is a popular engineering problems used in system identification or Equation
optimization. Due to the complex search space it requires, AI techniques has been used extensively to spot
the best curves that match the real behavior of the system. Genetic algorithm is known for their fast
convergence and their ability to find an optimal structure of the solution. We propose using a genetic
algorithm as a function approximator. Our attempt will focus on using the polynomial form of the
approximation. After implementing the algorithm, we are going to report our results and compare it with
the real function output.
Financial Time Series Analysis Based On Normalized Mutual Information FunctionsIJCI JOURNAL
A method of predictability analysis of future values of financial time series is described. The method is based on normalized mutual information functions. In the analysis, the use of these functions allowed to refuse any restrictions on the distributions of the parameters and on the correlations between parameters. A comparative analysis of the predictability of financial time series of Tel Aviv 25 stock exchange has been carried out.
This document discusses probabilistic models with latent variables for density estimation and dimensionality reduction. It introduces latent variables to model multimodal distributions as mixtures and uses expectation maximization to estimate parameters. Key algorithms discussed are Gaussian mixture models estimated with EM, K-means clustering as a hard version of EM, and principal component analysis for dimensionality reduction which can be framed as a latent variable model.
IRJET- Performance Analysis of Optimization Techniques by using ClusteringIRJET Journal
This document discusses optimization techniques for clustering algorithms. It introduces fuzzy bee colony optimization (FBCO) and compares its performance to other swarm algorithms like fuzzy c-means (FCM) and fuzzy particle swarm optimization (FPSO). FBCO is motivated by the natural behaviors of bee colonies and aims to avoid local minima problems. The document provides background on clustering, describes the FCM and FPSO algorithms, and proposes a FBCO algorithm to improve clustering performance.
Basics of Algorithms and Analysis of algorithm is in there, which includes Time complexity , space complexity, three cases ( best, average, worst) and analysis of Insertion sort.
*For knowledge purpose only*
*Hope you'll come up with better one*
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document presents a rough set theory-based approach for detecting malignant breast cancer cells. It uses a breast cancer dataset from a public repository containing information on 699 patients. The algorithm performs feature selection to identify a minimal set of important attributes. It is evaluated using various classifiers on subsets of 100, 350, and 699 instances. The reduced attribute sets were 3, 4, and 4 attributes respectively. Classification accuracy was found to increase with more training instances, reaching up to 96.85% accuracy. The root mean squared error metric also decreased with more instances, indicating the model fit the data better with larger training sizes. The rough set approach was shown to be effective for feature selection and breast cancer classification, especially on larger datasets.
A review of automatic differentiationand its efficient implementationssuserfa7e73
Automatic differentiation is a powerful tool for automatically calculating derivatives of mathematical functions and algorithms. It works by expressing the target function as a sequence of elementary operations and then applying the chain rule to differentiate each operation. This can be done using either forward or reverse mode. Forward mode calculates how changes in inputs propagate through the function to influence the outputs, while reverse mode calculates how changes in outputs backpropagate to influence the inputs. Both modes require performing the computation twice - once for the forward pass and once for the derivative pass. Careful implementation is required to make automatic differentiation efficient in terms of speed and memory usage.
This document presents new certified optimal solutions found by the Charibde algorithm for six difficult benchmark optimization problems. Charibde combines an evolutionary algorithm and interval-based methods in a cooperative framework. It has achieved optimality proofs for five bound-constrained problems and one nonlinearly constrained problem. These problems are highly multimodal and some had not been solved before even with approximate methods. The document also compares Charibde's performance to other state-of-the-art solvers, showing it is highly competitive while providing reliable optimality proofs.
AN EFFICIENT PARALLEL ALGORITHM FOR COMPUTING DETERMINANT OF NON-SQUARE MATRI...ijdpsjournal
One of the most significant challenges in Computing Determinant of Rectangular Matrices is high time
complexity of its algorithm. Among all definitions of determinant of rectangular matrices, Radic’s
definition has special features which make it more notable. But in this definition, C(N
M
) sub matrices of the
order m×m needed to be generated that put this problem in np-hard class. On the other hand, any row or
column reduction operation may hardly lead to diminish the volume of calculation. Therefore, in this paper
we try to present the parallel algorithm which can decrease the time complexity of computing the
determinant of non-square matrices to O(N).
This document contains a quiz on the topic of finite element analysis (FEA).
1. FEA involves discretizing a structure into smaller elements. Basic ideas of FEA were developed by aircraft engineers, and modern development occurred first in structural analysis.
2. Common methods of FEA include the force method and displacement method. Elements include 1D, 2D, and 3D elements like bars, beams, triangles, and quadrilaterals. Polynomial interpolation is often used.
3. FEA includes preprocessing like element discretization, solving using methods like the weighted residual or variational approach, and postprocessing of results. Software includes NISA and COSMOS. Analysis can be static or dynamic.
This document discusses different ways to classify optimization problems. It describes classifications based on:
1) The existence of constraints - problems are either constrained or unconstrained.
2) The nature of design variables - problems involve either parameter/static optimization or trajectory/dynamic optimization.
3) The physical structure - problems are either optimal control problems or non-optimal control problems.
4) The nature of equations - problems involve linear, nonlinear, geometric, or quadratic equations.
5) The permissible variable values - problems have either integer/discrete or real-valued variables.
6) The variable determinism - problems have either deterministic or stochastic/probabilistic variables.
7) The function
A Mathematical Model to Solve Nonlinear Initial and Boundary Value Problems b...IJERA Editor
In this paper, a novel method called Laplace-differential transform method (LDTM) is used to obtain an
approximate analytical solution for strong nonlinear initial and boundary value problems associated in
engineering phenomena. It is determined that the method works very well for the wide range of parameters and
an excellent agreement is demonstrated and discussed between the approximate solution and the exact one in
three examples. The most significant features of this method are its capability of handling non-linear boundary
value problems.
A NEW ALGORITHM FOR SOLVING FULLY FUZZY BI-LEVEL QUADRATIC PROGRAMMING PROBLEMSorajjournal
This paper is concerned with new method to find the fuzzy optimal solution of fully fuzzy bi-level non-linear (quadratic) programming (FFBLQP) problems where all the coefficients and decision variables of both objective functions and the constraints are triangular fuzzy numbers (TFNs). A new method is based on decomposed the given problem into bi-level problem with three crisp quadratic objective functions and bounded variables constraints. In order to often a fuzzy optimal solution of the FFBLQP problems, the concept of tolerance membership function is used to develop a fuzzy max-min decision model for generating satisfactory fuzzy solution for FFBLQP problems in which the upper-level decision maker (ULDM) specifies his/her objective functions and decisions with possible tolerances which are described by membership functions of fuzzy set theory. Then, the lower-level decision maker (LLDM) uses this preference information for ULDM and solves his/her problem subject to the ULDMs restrictions. Finally, the decomposed method is illustrated by numerical example.
The document summarizes four numerical methods commonly used in geomechanics:
1. The Distinct Element Method (DEM) explicitly models discontinuities.
2. The Discontinuous Deformation Analysis Method (DDA) can consider discontinuities explicitly or implicitly.
3. The Bonded Particle Method (BPM) models geomaterials as an assembly of discrete particles.
4. The Artificial Neural Network Method (ANN) is a data-driven modeling approach not classified as continuum or discontinuum.
The document provides a brief overview of the fundamental algorithms of each method and examples of their applications.
THE RESEARCH OF QUANTUM PHASE ESTIMATION ALGORITHMIJCSEA Journal
This document discusses phase estimation in quantum computing. It begins by introducing quantum Fourier transforms and how they are important for algorithms like Shor's algorithm. It then describes the phase estimation algorithm in detail, including how it uses two registers to estimate the phase of a quantum state and how the inverse quantum Fourier transform improves this estimate. Simulation results are presented that show the probability distribution of the estimated phase converging to the true value and how the probability of success increases with more qubits while computational costs rise polynomially. The paper concludes that the optimal number of qubits balances high success probability and low costs for phase estimation.
A MODIFIED VORTEX SEARCH ALGORITHM FOR NUMERICAL FUNCTION OPTIMIZATIONijaia
This document presents a modified version of the Vortex Search (VS) algorithm called the Modified Vortex Search (MVS) algorithm for numerical function optimization. The VS algorithm has the drawback that it can get trapped in local minima for functions with multiple local minima. The MVS algorithm addresses this by generating candidate solutions around multiple points at each iteration rather than a single point, allowing it to escape local minima more easily. Computational results on benchmark functions showed the MVS algorithm outperformed the original VS algorithm, as well as PSO2011 and ABC algorithms.
This document summarizes and analyzes the performance of Newton's method, BFGS method, and SR1 method for minimizing a quadratic and convex function. It finds that:
1) Newton's method performed the best, requiring fewer iterations and achieving greater accuracy than the other methods.
2) For constrained problems, the SR1 method achieved some success due to its flexibility in not always requiring a descent direction.
3) While Newton's method has the best theoretical convergence rate, quasi-Newton methods are more applicable to complex problems as hessian inversion becomes more computationally expensive.
4) When minimizing quadratic and convex functions, Newton's method generally performs better than the other tested methods. However, the best
Comparisons of linear goal programming algorithmsAlexander Decker
This document compares different algorithms for solving linear goal programming problems:
1) Lee's modified simplex algorithm from 1972 and Ignizio's sequential algorithm from 1976 are two commonly used algorithms but require many columns and objective function rows, adding to computational time.
2) Orumie and Ebong developed a new algorithm in 2011 utilizing modified simplex procedures that has better computational times than existing algorithms for all problems tested.
3) The document reviews several other goal programming algorithms and finds that Orumie and Ebong's new method provides the best reduction in computational time for solving the problems.
GENETIC ALGORITHM FOR FUNCTION APPROXIMATION: AN EXPERIMENTAL INVESTIGATIONijaia
Function Approximation is a popular engineering problems used in system identification or Equation
optimization. Due to the complex search space it requires, AI techniques has been used extensively to spot
the best curves that match the real behavior of the system. Genetic algorithm is known for their fast
convergence and their ability to find an optimal structure of the solution. We propose using a genetic
algorithm as a function approximator. Our attempt will focus on using the polynomial form of the
approximation. After implementing the algorithm, we are going to report our results and compare it with
the real function output.
Financial Time Series Analysis Based On Normalized Mutual Information FunctionsIJCI JOURNAL
A method of predictability analysis of future values of financial time series is described. The method is based on normalized mutual information functions. In the analysis, the use of these functions allowed to refuse any restrictions on the distributions of the parameters and on the correlations between parameters. A comparative analysis of the predictability of financial time series of Tel Aviv 25 stock exchange has been carried out.
This document discusses probabilistic models with latent variables for density estimation and dimensionality reduction. It introduces latent variables to model multimodal distributions as mixtures and uses expectation maximization to estimate parameters. Key algorithms discussed are Gaussian mixture models estimated with EM, K-means clustering as a hard version of EM, and principal component analysis for dimensionality reduction which can be framed as a latent variable model.
IRJET- Performance Analysis of Optimization Techniques by using ClusteringIRJET Journal
This document discusses optimization techniques for clustering algorithms. It introduces fuzzy bee colony optimization (FBCO) and compares its performance to other swarm algorithms like fuzzy c-means (FCM) and fuzzy particle swarm optimization (FPSO). FBCO is motivated by the natural behaviors of bee colonies and aims to avoid local minima problems. The document provides background on clustering, describes the FCM and FPSO algorithms, and proposes a FBCO algorithm to improve clustering performance.
Basics of Algorithms and Analysis of algorithm is in there, which includes Time complexity , space complexity, three cases ( best, average, worst) and analysis of Insertion sort.
*For knowledge purpose only*
*Hope you'll come up with better one*
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document presents a rough set theory-based approach for detecting malignant breast cancer cells. It uses a breast cancer dataset from a public repository containing information on 699 patients. The algorithm performs feature selection to identify a minimal set of important attributes. It is evaluated using various classifiers on subsets of 100, 350, and 699 instances. The reduced attribute sets were 3, 4, and 4 attributes respectively. Classification accuracy was found to increase with more training instances, reaching up to 96.85% accuracy. The root mean squared error metric also decreased with more instances, indicating the model fit the data better with larger training sizes. The rough set approach was shown to be effective for feature selection and breast cancer classification, especially on larger datasets.
This study investigated the effects of process conditions on properties of an instant grain base made by extruding a blend of wheat, mungbean, and groundnut. Extrusion was performed at different moisture contents, screw speeds, and barrel temperatures. Response surface methodology was used to develop models relating these processing variables to responses like specific mechanical energy, expansion ratio, density, water absorption index, and water solubility index. The models showed these responses were significantly affected by the processing conditions. Optimization found the optimum conditions were 14.08% moisture, 521 rpm screw speed, and 140°C temperature. This produced desirable properties with high expansion and water absorption.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document compares various dissolved gas analysis (DGA) methods for diagnosing transformer faults, including Rogers ratio, IEC ratio, Doernenburg, Duval triangle, key gas, and artificial neural network methods. It evaluates these methods based on their ability to successfully predict different fault types (F1-F5) using DGA data from previous studies. The results show that the Duval gas and key gas methods achieved 100% accuracy for some fault types, while the IEC method had the highest accuracy of 82% for fault type F3. Overall, the Duval gas method and key gas method were the most consistent according to the analysis.
This document analyzes the effects of shadowing and fading on the performance of ad hoc routing protocols in mobile ad hoc networks (MANETs). It simulates the performance of three routing protocols - Location-Aided Routing (LAR), Routing Information Protocol (RIP), and LANMAR - under different shadowing and fading conditions using the QualNet simulator. The simulation evaluates the protocols based on application layer metrics like end-to-end delay, jitter, throughput, and packet delivery ratio, as well as physical layer metrics like power consumption and battery usage. The results show the impact of realistic channel models like shadowing and fading on routing protocol performance in MANETs.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document summarizes a research paper that proposes using a technique called "tiny video representation" to classify and retrieve video frames and videos. The proposed method involves preprocessing videos by splitting them into frames, removing black bars, resizing frames to 32x32 pixels, and using affinity propagation to cluster unique frames. This creates a "tiny video database" that can be used for content-based copy detection, video categorization through classification of frames, and retrieval of related videos through nearest neighbor searches. Experimental results showed the tiny video database approach improved classification precision and recall compared to using individual frames or videos.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
1. The document describes a proposed multi-touch table system with RFID technology that could be used in hotels.
2. The system would allow customers to place food orders directly from their table by selecting items on a multi-touch display and authenticating with an RFID card.
3. The table would be connected to a backend system to send orders to the kitchen and deduct amounts from the customer's account.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This document proposes and evaluates a new metaheuristic optimization algorithm called Current Search (CS) and applies it to optimize PID controller parameters for DC motor speed control. The CS is inspired by electric current flow and aims to balance exploration and exploitation. It outperforms genetic algorithm, particle swarm optimization, and adaptive tabu search on benchmark optimization problems, finding better solutions faster. When applied to optimize a PID controller for DC motor speed control, the CS successfully controlled motor speed.
Particle Swarm Optimization to Solve Multiple Traveling Salesman ProblemIRJET Journal
This document proposes a new genetic ant colony optimization algorithm for solving the multiple traveling salesman problem (mTSP). The algorithm combines properties of genetic algorithms and ant colony optimization. Each salesman's route is determined using ant colony optimization, while the routes of different salesmen are combined into a complete solution controlled by the genetic algorithm. The algorithm is tested on benchmark problem instances and shown to perform efficiently compared to other existing algorithms for mTSP. Key aspects of the algorithm include the representation of solutions, crossover operators that always generate feasible solutions, and the integration of ant colony optimization and genetic algorithms.
The document discusses various optimization techniques including evolutionary computing techniques such as particle swarm optimization and genetic algorithms. It provides an overview of the goal of optimization problems and discusses black-box optimization approaches. Evolutionary algorithms and swarm intelligence techniques that are inspired by nature are also introduced. The document then focuses on particle swarm optimization, providing details on the concepts, mathematical equations, components and steps involved in PSO. It also discusses genetic algorithms at a high level.
MULTIPROCESSOR SCHEDULING AND PERFORMANCE EVALUATION USING ELITIST NON DOMINA...ijcsa
Task scheduling plays an important part in the improvement of parallel and distributed systems. The problem of task scheduling has been shown to be NP hard. The time consuming is more to solve the problem in deterministic techniques. There are algorithms developed to schedule tasks for distributed environment, which focus on single objective. The problem becomes more complex, while considering biobjective.This paper presents bi-objective independent task scheduling algorithm using elitist Nondominated
sorting genetic algorithm (NSGA-II) to minimize the makespan and flowtime. This algorithm generates pareto global optimal solutions for this bi-objective task scheduling problem. NSGA-II is implemented by using the set of benchmark instances. The experimental result shows NSGA-II generates efficient optimal schedules.
On the Performance of the Pareto Set Pursuing (PSP) Method for Mixed-Variable...Amir Ziai
This document describes a study on modifying the Pareto Set Pursuing (PSP) method to solve multi-objective optimization problems with mixed continuous and discrete variables. The PSP method was originally developed for problems with only continuous variables. The modifications allow it to handle mixed variable problems. The performance of the modified PSP method is compared to other multi-objective algorithms based on metrics like efficiency, robustness, and closeness to the true Pareto front with a limited number of function evaluations. Preliminary results on benchmark problems and two engineering design examples show that the modified PSP is competitive when the number of function evaluations is limited, but its performance decreases as the number of design variables increases.
MULTI-OBJECTIVE ENERGY EFFICIENT OPTIMIZATION ALGORITHM FOR COVERAGE CONTROL ...ijcseit
Many studies have been done in the area of Wireless Sensor Networks (WSNs) in recent years. In this kind of networks, some of the key objectives that need to be satisfied are area coverage, number of active sensors and energy consumed by nodes. In this paper, we propose a NSGA-II based multi-objective algorithm for optimizing all of these objectives simultaneously. The efficiency of our algorithm is demonstrated in the simulation results. This efficiency can be shown as finding the optimal balance point among the maximum coverage rate, the least energy consumption, and the minimum number of active nodes while maintaining the connectivity of the network
4-Unconstrained Single Variable Optimization-Methods and Application.pdfkhadijabutt34
The document discusses unconstrained single variable optimization methods. It covers several topics:
- Direct search methods like bracketing methods, which use only function values, and region elimination methods.
- Methods requiring derivatives like Newton-Raphson, bisection, and secant methods, which use function and derivative values.
- Specific methods covered in more detail include exhaustive search, bounding phase, dichotomous search, and interval halving for region elimination. Algorithms for some of these methods are provided.
Optimization of Mechanical Design Problems Using Improved Differential Evolut...IDES Editor
Differential Evolution (DE) is a novel evolutionary
approach capable of handling non-differentiable, non-linear
and multi-modal objective functions. DE has been consistently
ranked as one of the best search algorithm for solving global
optimization problems in several case studies. This paper
presents an Improved Constraint Differential Evolution
(ICDE) algorithm for solving constrained optimization
problems. The proposed ICDE algorithm differs from
unconstrained DE algorithm only in the place of initialization,
selection of particles to the next generation and sorting the
final results. Also we implemented the new idea to five versions
of DE algorithm. The performance of ICDE algorithm is
validated on four mechanical engineering problems. The
experimental results show that the performance of ICDE
algorithm in terms of final objective function value, number
of function evaluations and convergence time.
A NEW APPROACH IN DYNAMIC TRAVELING SALESMAN PROBLEM: A HYBRID OF ANT COLONY ...ijmpict
Nowadays swarm intelligence-based algorithms are being used widely to optimize the dynamic traveling salesman problem (DTSP). In this paper, we have used mixed method of Ant Colony Optimization (AOC) and gradient descent to optimize DTSP which differs with ACO algorithm in evaporation rate and innovative data. This approach prevents premature convergence and scape from local optimum spots and also makes it possible to find better solutions for algorithm. In this paper, we’re going to offer gradient descent and ACO algorithm which in comparison to some former methods it shows that algorithm has significantly improved routes optimization.
Using particle swarm optimization to solve test functions problemsriyaniaes
In this paper the benchmarking functions are used to evaluate and check the particle swarm optimization (PSO) algorithm. However, the functions utilized have two dimension but they selected with different difficulty and with different models. In order to prove capability of PSO, it is compared with genetic algorithm (GA). Hence, the two algorithms are compared in terms of objective functions and the standard deviation. Different runs have been taken to get convincing results and the parameters are chosen properly where the Matlab software is used. Where the suggested algorithm can solve different engineering problems with different dimension and outperform the others in term of accuracy and speed of convergence.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
This document provides a comparative study of several artificial intelligence algorithms: Heuristics, A* algorithm, K-Nearest Neighbors (KNN), and Linear Regression. It describes each algorithm, provides an example problem that each algorithm could solve, and discusses their applications. The document aims to illustrate the variety of problems that can be solved by these AI algorithms and help readers understand which algorithm may be best suited to different AI problems.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
Fault diagnosis using genetic algorithms and principal curveseSAT Journals
Abstract Several applications of nonlinear principal component analysis (NPCA) have appeared recently in process monitoring and fault diagnosis. In this paper a new approach is proposed for fault detection based on principal curves and genetic algorithms. The principal curve is a generation of linear principal component (PCA) introduced by Hastie as a parametric curve passes satisfactorily through the middle of data. The existing principal curves algorithms employ the first component of the data as an initial estimation of principal curve. However the dependence on initial line leads to a lack of flexibility and the final curve is only satisfactory for specific problems. In this paper we extend this work in two ways. First, we propose a new method based on genetic algorithms to find the principal curve. Here, lines are fitted and connected to form polygonal lines (PL). Second, potential application of principal curves is discussed. An example is used to illustrate fault diagnosis of nonlinear process using the proposed approach. Index Terms: Principal curve, Genetic Algorithm, Nonlinear principal component analysis, Fault detection.
This document proposes a new variant of the differential evolution algorithm called DE New. It presents the basic differential evolution algorithm and the proposed DE New algorithm. The DE New algorithm modifies the mutation strategy to better explore the search space and solve stagnation problems. The performance of DE New is evaluated on 24 benchmark functions from 2D to 40D and is found to outperform GA, DE-PSO, and DE-AUTO on most of the benchmark functions, particularly for unimodal and multi-modal problems.
Multi objective predictive control a solution using metaheuristicsijcsit
The application of multi objective model predictive control approaches is significantly limited with
computation time associated with optimization algorithms. Metaheuristics are general purpose heuristics
that have been successfully used in solving difficult optimization problems in a reasonable computation
time. In this work , we use and compare two multi objective metaheuristics, Multi-Objective Particle
swarm Optimization, MOPSO, and Multi-Objective Gravitational Search Algorithm, MOGSA, to generate
a set of approximately Pareto-optimal solutions in a single run. Two examples are studied, a nonlinear
system consisting of two mobile robots tracking trajectories and avoiding obstacles and a linear multi
variable system. The computation times and the quality of the solution in terms of the smoothness of the
control signals and precision of tracking show that MOPSO can be an alternative for real time
applications.
This document presents a particle swarm optimization (PSO) algorithm to solve economic load dispatch (ELD) problems more efficiently than genetic algorithms. The PSO technique requires less computational time per iteration and finds solutions faster. Simulation results show that using PSO, the computational time and generation costs are lower than when using genetic algorithms. PSO is effective at finding the minimum cost solution to the economic load dispatch problem with fewer iterations and less computation time compared to other optimization methods.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
This document presents a particle swarm optimization (PSO) algorithm to solve economic load dispatch (ELD) problems more efficiently than genetic algorithms. The PSO technique requires less computational time per iteration and finds solutions faster. Simulation results show that using PSO, the computational time and generation costs are lower than when using genetic algorithms. PSO is effective at finding the minimum cost generation dispatch that meets load demand while satisfying constraints such as generator limits.
1) The document presents a particle swarm optimization (PSO) technique to solve economic load dispatch problems in a more computationally efficient manner than genetic algorithms.
2) PSO is a population-based optimization technique that is faster and requires less computation time per iteration than genetic algorithms. It is well-suited for solving complex non-linear optimization problems.
3) The study applies PSO to minimize generation costs for an economic load dispatch problem subject to constraints. Simulation results demonstrate that PSO solves the problem with less computational time and iterations compared to genetic algorithms.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Ds33717725
1. Anitha Santhoshi.M, Durga Devi.G / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 3, May-Jun 2013, pp.717-725
717 | P a g e
Optimal Gear Design By Using Box And Random Search
Methods
Anitha Santhoshi.M*, Durga Devi.G**
*(Asst.prof, Department of Mechanical Engg, SVCET, Etcherla)
** (Asst.prof, Department of Mechanical Engg, SVCET, Etcherla)
ABSTRACT
The development of evolutionary algorithms
plays a major role, in recent days, for optimal
design of gears, so as to reduce the weight. In this
study an optimal weight design (OWD) problem
of gear is formulated for constrained bending
strength of gear, tortional strength of shafts and
each gear dimension as a NIP problem and solved
it directly by keeping nonlinear constraint using
Box and Random search methods, such that the
number of decision {design} variables does not
increase and easily get the best compromised
solution. An extensive computer program in Java
has been written exclusively for their purpose
and is successfully used to obtain the optimal gear
design.
Keywords – optimal weight design (OWD), NIP
problem, Box Method, Random search method,
Decision {design} variables.
1. INTRODUCTION
The most important problem that confronts
practical engineers is the mechanical design, a field
of creativity. In case of gear design, an infinite
number of possible design solutions are found within
the overall objective. Any one of these solutions is
adequate because it represents a synthesis, which
merely satisfies the functional requirements [1].
Here lies a conductive environment for applying cut
and try technique to obtain an optimal design
solution among the available solutions. The
approach to solve certain design problem has so
relied on the trail-and-cut methods which, because of
their methodology, take considerable time to obtain
the optimal solution.
In this study an optimal weight design
(OWD) problem of gear is formulated for
constrained bending strength of gear, tortional
strength of shafts and each gear dimension as a NIP
problem and solves it directly by keeping nonlinear
constraint by using Box and Random Search
Methods.
As a result, the number of decision (design)
variables does not increase and easily get the best
compromised solution. An extensive computer
program in Java has been written exclusively for
their purpose and is successfully used to obtain the
optimal gear design.
2. ENGINEERING OPTIMIZATION
Optimization is the act of obtaining the best
result under given circumstances. In design,
construction and maintenance of any engineering
system, engineers have to take many technological
and managerial decisions at several stages. The
ultimate goal of all such decisions is either to
minimize the effort required or to maximize the
desired benefit.
2.1 Optimization Algorithms
2.1.1 Single variable optimization algorithms
These algorithms provide a good
understanding of the properties of the minimum and
maximum points in a function and how optimization
algorithms work iteratively to find the optimum
point in a problem. The algorithms are classified into
two categories, they are direct methods and gradient
based methods. Direct methods do not use any
derivative information of the objective function:
only objective function values are used to guide the
search process. However, gradient based methods
use derivative information (first and/ or second
order) to guide search process.
2.1.2 Multi – variable optimization algorithms
A number of algorithms for unconstrained,
multi-variable optimization problems are present.
These algorithms demonstrate how this search for
optimum points progress in multiple dimensions.
2.1.3 Constrained optimization algorithms
Constrained optimization algorithms used
in single variable and multi variable optimization
algorithms repeatedly and simultaneously
maintained the search effort inside the feasible
search region. These algorithms are mostly used in
engineering optimization problems. These
algorithms are divided into two broad categories;
they are direct search methods and gradient-based
methods. In constraint optimization problem,
equality constraints make the search process slow
and difficult to converge.
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2.1.4 Specialized optimization algorithms
There exist a number of structured
algorithms, which are ideal for only a certain class of
optimization problems. Two of these algorithms are
integer programming and geometric programming.
These are often used in engineering design
problems. Integer programming methods can solve
optimization problems with integer design variables.
Geometric programming methods solve optimization
problems with objective functions and constraints
written in a special form.
3. BOX METHOD
The Box method is similar to the simplex
method of unconstrained search except that the
constraints are handled in the former method. This
method was developed by M.J.Box in 1965; [2], the
algorithm begins with a number of feasible points
created at random. If a point is found to be
infeasible, a new point is created using the
previously – generated feasible points. Usually, the
infeasible point is pushed towards the centroid of the
previously found feasible points. Once a set of
feasible points is found, the worst point is reflected
about the centroid of rest of the points to find a new
point, Depending on the feasibility and function
value of the new point, the point is further modified
or accepted. If the new point falls outside the
variable boundaries, the point is modified to fall on
the violated boundary. If the new point is infeasible,
the point is retracted to towards the feasible points.
The worst point in the simplex is replaced by this
new feasible point and the algorithm continues for
the next iteration. The Box Method is also called as
“Complex Search Method”.
3.1 BOX [Complex Search] Algorithm [2]
Step 1: Assume a bound in x (x (L), x (U)), a
reflection parameter α.
Step 2: Generate an initial set of P (usually 2n)
feasible points. For each point
(a) Sample n times to determine the point
)( p
ix in the given bound.
(b) If x (p)
is infeasible, calculate x
(centroid) of current set of points and reset
)(
2
1 )()()( ppp
xxxx
Until
)( p
x is feasible;
Else if
)( p
x is feasible, continue with (a)
until P points are created
(c) Evaluate )( )( p
xf for p = 0, 1, 2…, (P-1)
Step 3: Carry out the reflection step:
(a) Select xR
such that
f (xR
) = max f(x (p)
) = Fmax
(b) Calculate the centroi x d (of points
except xR
) and the next point
)( Rm
xxxx
(c) If xm
is feasible and f (xm
) > Fmax
retract half the distance to the
centroid x . Continue until f(xm
) < Fmax
Else if xm
is feasible and f (xm
) < Fmax,
go to Step 5.
Else if xm
is infeasible, go to Step 4.
Step 4: Check for feasibility of the solution
(a) For all i, reset violated variable
bounds:
If
)()( L
i
m
i
L
i
m
i xxsetxx
If
)()( U
i
m
i
U
i
m
i xxsetxx
(b) If the resulting xm
is infeasible, retract
half the distance to the centroid.
Continue until xm
is feasible. Go to
Step 3(c).
Step 5: Replace xR
by xm
. Check for termination.
Calculate
p
p
xf
P
f )(
1 )(
and x =
p
p
x
P
)(1
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719 | P a g e
Fig.3.1 Flow chart of Box Method
q=q+1
constraint
satisfaction
No
infeasible feasible
infeasible feasible
Yes
Set x
l
= (20,10,30,18), x
r
= (12,20,10,7)
n=8,p=1q=1,P=8,Q=500,α =1.3
Start
Print lowest of f (xp) = w and related xp
Calculate X
i
(P)
= X
i
l
+ r
i
X
i
r
Select xR such that f (xR) and f (xp) =
Xm = + α ( -xr) x is centriod (expect xR)
xp= xp+1/2( - xp ) Save xp, f (xp) =w. set
p= p+1 until P is created
Terminate
If q>Q
Calculate f (xm)
If xm < X
i
l
then xm =
Xi
l
and xm < Xi
u then
xm = Xi
u
Retract half distance to the
centriod
Replace xR by xm calculate f bar,
IF xm
f (xm) ≥ Fmax retract
half of distance to
centriod.until f (xm) <
Fmax
f (xm) < Fmax
Stop
Generate the random numbers
ri=(i=1, 2,…8) b/w limit 0 to 1
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4. RANDOM SEARCH METHOD
Like the Box method, the random search
method also works with a population of points. But
instead of replacing the next point in the population
by a point created in a structured manner points are
created either at random or by performing a
unidirectional search along the random search
directions. Here, we describe one such method.
Since there is no specific search direction used in the
method, random search methods work equally
efficiently to many problems. In the Luus and
jaakola method 1973; an initial point and an initial
interval are chosen at random. Depending on the
function values at a number of random points in the
interval, the search interval is reduced at every
iteration by a constant factor. Then it is increased. In
the following algorithm, P points are considered at
each iteration and Q such iterations are performed.
Thus, if the initial interval in one variable is do and
at every iteration the interval is reduced by a factor
€, the final accuracy in the solution in that variable
becomes (1-€) ^Q do and the required number of
function evaluations is P X Q.
4.1. Random Search Algorithm [2]
Step 1 Given an initial feasible point x0
,
an initial range zo
such that the minimum, x*
, lies in
)
2
1
,
2
1
( 0000
zxzx Choose the Parameter
0< 1 For each of Q blocks, initially set q = 1 &
p = 1.
Step 2 For i = 1, 2 …N, create points
using a uniform distribution of r in the range (-0.5,
0.5). Set
11)(
q
i
q
i
p
i rzxx
Step 3 If
)( p
x is infeasible and p < P, repeat
Step-2. If x(p)
is feasible, save x(p)
and f(x(p)
),
increment p and repeat Step-2;
Else if p = P, set xq
to be the point that has the
lowest f(x(p)
), overall feasible x(p)
including xq-1
and
reset p = 1.
Step 4 Reduce the range
via
1
)1(
q
i
q
i zz .
Step 5 If q > Q, Terminate;
Else increment q and continue with Step-2.
The suggested values of parameters are €
=0.05, P= 5(depending upon the design variables),
and Q is related to the desired accuracy in the
solution. It is to be noted that the obtained solution is
not guaranteed to be the true optimum.
5. PROBLEM DESCRIPTION
In the present work an OWD of a gear with
a minimum weight is considered in fig above. Input
power of 7.5 KW, the speed of crank shaft gear
(pinion) is considered to be 1500 rpm and the gear
ratio is 4. Necessary conditions required for
developing a mathematical model for gear design are
discussed in this section as given in [4].
Preliminary Gear considerations: The following
are input parameters required for preliminary gear
design [6].
1. Power to be transmitted (H), KW.
2. Speed of the pinion (N1), rpm.
3. Gear ratio (a)
5. Anitha Santhoshi.M, Durga Devi.G / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
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Fig4.1 Flow chart of Random Search Method
Set x
0
= (20,10,30,18), z
0
= (12,20,10,7)
n=5,p=1q=1,P=5,Q=500,ε =0.05
Generate the random numbers
r
i
=(i=1,2,3,4) b/w limit -0.5 to +0.5
uniformly distributed
Terminate
q=q+1
If q>Q
Xi
(P)
= Xi
q-1
+ ri Zi
q-1
If p=P take lowest value of f (xp) and xp reset p=1
Zi
q
= (1-ε) Zi
q-1
p<P Save xp, f (xp) =w.
set p=p+1.until P is
created
Constraint
satisfaction
No Yes
infeasible feasible
Print lowest of f (xp) = w and relate xp
Stop
Start
7. Anitha Santhoshi.M, Durga Devi.G / International Journal of Engineering Research and
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5.2. Application of Random Search Method
[RSM]
Here first create random numbers
depending upon the decision variables .Here 4
design variables so random numbers (r1, r2, r3,
r4).The random numbers are created between(-
0.5to+0.5)limit. It is uniformly distributed. After
that we have to find
11)(
q
i
q
i
p
i rzxx
.
where
(i=1, 2…4).If
)( p
x is infeasible and p < P, repeat
finding
11)(
q
i
q
i
p
i rzxx
.
If x (p)
is feasible,
save x (p)
and f(x (p)
), increment p and repeat same
procedure until p=P. Take minimum of value of
„W‟ (i.e. f(x (p)
) and reset p=1. Reduce the range
via
1
)1(
q
i
q
i zz .Repeat the procedure until
q>Q. Terminate; Else increment q and continue the
procedure and calculate „W‟ value by doing 500
iterations we will take least value of „W‟ and
corresponding x = [b, , , ].
6. DISCUSSION ON THE RESULTS
Here the main objective is to minimize the
weight. For that Box and Random Search Methods
are used. The gear module (m) values considered
are 2.75mm; 3mm and 3.5mm.These have been
compared with those of literature and incorporated
in tables 7.1 to 7.3. By observing the tabulated
values it is found that Random search method gives
better results than the Box method.
Box method and Random search method
[RSM] are applied to the OWD problem of the
gear. The results obtained by both the methods are
compared with that available in literature [4].
Among the three methods the Random search
method is found to be giving good results for the
problem considered can be effectively applied for
single stage gear design problem. From the tables
7.1 to 7.3 even though the results of [4] give
minimum values the variables are violated the
constraints. Therefore the solutions presented are
not feasible solutions. Hence RSM is found to be
best method.
7. COMPARISON OF RESULTS
For Module m=2.75
** indicates constrained violation
Table: 7.1
For Module m=3
Thickness
of web:
9.625 9.63 9.63
Outside
diameter of
boss:
65 64.99 55
Drill holls:
36.09 28.10 30.6
BY BOX
METHOD
BY
RANDOM
SEARCH
METHOD
BY
LITERA
TURE
WEIGHT
7560.98 7077.23 3512.6
Face width: b 26.69 23.94 24
Diameter of
pinion shaft: 30.0 29.88 30
Diameter of
gear shaft: 40.0 39.99 30**
Number of
teeth(pinion):
20.91=(21) 18 18
Number of
teeth(gear):
83.64=(84) 72 72
Module: m 2.75 2.75 2.75
Pitch
circle(pinion): 57.50 49.5 49.5
Pitch
circle(gear):
230.01 198 198
Between the
axes: C 143.75 123.75 123.75
Surface
durability: k 0.287 0.3747 0.374
Dedendum
circle(gear): 223.135 191.1 191.1
Inside
diameter of
rim:
209.385 177.4 177.4
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Vol. 3, Issue 3, May-Jun 2013, pp.717-725
724 | P a g e
Pitch
circle(gear):
230.01 198 198
Between the
axes: C
143.75 123.75 123.75
Surface
durability: k
0.287 0.3747 0.374
Dedendum
circle(gear):
223.135 191.1 191.1
Inside diameter
of rim:
209.385 177.4 177.4
Thickness of
web:
9.625 9.63 9.63
Outside diameter
of boss:
65 64.99 55
Drill holls: 36.09 28.10 30.6
** indicates constrained violation
Table: 7.2
For Module m=3.5
BY BOX
METHOD
BY
RANDOM
SEARCH
METHOD
WEIGHT 10033.21 7111.95
Face width: b 25.82 23.94
Diameter of
pinion shaft: 30.0 26.55
Diameter of
gear shaft: 40.0 39.52
Number of
teeth(pinion):
21.98=(22) 18
Number of
teeth(gear):
87.92=(88) 72
Module: m 3.5 3.5
Pitch
circle(pinion): 76.93 63
Pitch
circle(gear):
307.72 252
Between the
axes: C
192.325 157.5
Surface
durability: k
0.273 0.333
Dedendum
circle(gear):
298.97 243.25
Inside diameter
of rim:
281.47 225.75
Thickness of
web:
12.25 63
Outside diameter
of boss:
65 64.52
Drill holls: 54.11 40.30
Table: 7.3
8. CONCLUSIONS AND SUGGESTIONS
FOR FURTHER WORK
The gear is one of the machine elements.
It transmits power with accuracy to parallel shafts,
skew shafts and intermittent action gear etc.
Therefore it has various uses in industrial
production. When designing a gear usually the trail
and cut methods are used to determine factors such
as input power, rotation frequency, transmission
ratio, bending strength of the gear, tortional
strength of shafts and each gear dimension.
However, this method does not include the method
of optimal weight design [4]. The mathematical
model of an optimal weight design problem of gear
for minimizing objective functions includes the
above mentioned design factors.
Box method and Random search method
[RSM] are applied to the OWD problem of the
gear. Example taken in this study is a spur gear.
The results obtained by both the methods are
compared with that available in literature [4].
Among the three methods the Random search
method is found to be giving good results for the
problem considered can be effectively applied for
single stage gear design problem. From the tables
7.1 to 7.3 even though the results of [4] give
minimum values the variables are violated the
constraints. Therefore the solutions presented are
BY BOX
METHOD
BY
RANDOM
SEARCH
METHOD
BY
LITERATURE
WEIGHT 7560.98 7077.23 3512.6
Face width: b 26.69 23.94 24
Diameter of
pinion shaft:
30.0 29.88 30
Diameter of gear
shaft:
40.0 39.99 30**
Number of
teeth(pinion):
20.91=(21) 18 18
Number of
teeth(gear):
83.64=(84) 72 72
Module: m 2.75 2.75 2.75
Pitch
circle(pinion): 57.50 49.5 49.5
9. Anitha Santhoshi.M, Durga Devi.G / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 3, May-Jun 2013, pp.717-725
725 | P a g e
not feasible solutions. Hence RSM is found to be
best method. As a result the minimum weight of
the gear considered using RSM is 7077.23.
This study can be extended using other
methods like cutting plane method and feasible
direction method to get faster and better values.
And also the BOX and RSM Algorithms can be
applied for designing optimization of the
mechanical elements.
BIBLOGRAPHY
1. Jhonson, C.R., 1961, “Optimal Design of
Mechanical Elements”, John Wiley and
Sons Inc, New York
2. Deb, K., 1996, “Optimization for
Engineering Design”, Prentice Hall of
India, New Delhi.
3. Rao, S.S., 1984, “Optimization theory and
Applications”, Wiley Eastern, New Delhi.
4. TakaoYokota, Takeaki Taguchi, and
Mitsuo Gen, 1998, “A Solution Method
for optimal Weight Design Problem of
Gear Using Genetic Algorithm”,
Computer and Industrial Engineering
Vol.35 (3-4), pp.523-526.
5. Department of Mechanical Engineering,
PSG College of Technology, Coimbatore
641004, 1983, “Design Data”.
6. Dudley, D.W., 1962, “Gear Hand Book”,
The Design, Manufacture, and Application
of Gears”, Mc Graw Hill Book Co, New
York.