Abstract: Engineering design problems are complex by nature because of their critical objective functions involving many variables and Constraints. Engineers have to ensure the compatibility with the imposed specifications keeping the manufacturing costs low. Moreover, the methodology may vary according to the design problem.
The main issue is to choose the proper tool for optimization. In the earlier days, a design problem was optimized by some of the conventional optimization techniques like gradient Search, evolutionary optimization, random search etc. These are known as classical methods.
The method is to be properly Chosen depending on the nature of the problem- an incorrect choice may sometimes fail to give the optimal solution. So the methods are less robust.
Now-a-days soft-computing techniques are being widely used for optimizing a function. These are more robust. Genetic algorithm is one such method. It is an effective tool in the realm of stochastic optimization (non-classical). The algorithm produces many strings and generation to reach the optimal point.
The main objective of the paper is to optimize engineering design problems using Genetic Algorithm and to analyze how the algorithm reaches the optima effectively and closely. We choose a mathematical expression for the objective function in terms of the design variables and optimize the same under given constraints using GA.
A Genetic Algorithm on Optimization Test FunctionsIJMERJOURNAL
ABSTRACT: Genetic Algorithms (GAs) have become increasingly useful over the years for solving combinatorial problems. Though they are generally accepted to be good performers among metaheuristic algorithms, most works have concentrated on the application of the GAs rather than the theoretical justifications. In this paper, we examine and justify the suitability of Genetic Algorithms in solving complex, multi-variable and multi-modal optimization problems. To achieve this, a simple Genetic Algorithm was used to solve four standard complicated optimization test functions, namely Rosenbrock, Schwefel, Rastrigin and Shubert functions. These functions are benchmarks to test the quality of an optimization procedure towards a global optimum. We show that the method has a quicker convergence to the global optima and that the optimal values for the Rosenbrock, Rastrigin, Schwefel and Shubert functions are zero (0), zero (0), -418.9829 and -14.5080 respectively
An Application of Genetic Algorithm for Non-restricted Space and Pre-determin...drboon
The use of a genetic algorithm is presented to solve a facility layout problem in the situation where there is non-restricted space but the ratio of plant length and width is pre-determined. A two-leveled chromosome is constructed. Six rules are established to translate the chromosome to facility design. An approach of solving a facility layout problem is proposed. A numerical example is employed to illustrate the approach.
The potential role of ai in the minimisation and mitigation of project delayPieter Rautenbach
Artificial intelligence (AI) can have wide reaching application within the construction
industry, however, the actual application of this set of technologies is currently under exploited. This
paper considers the role that the application of AI can take in optimising the efficiencies of project
execution and how this can potentially reduce project duration and minimise and mitigate delay on
projects.
ESTIMATING PROJECT DEVELOPMENT EFFORT USING CLUSTERED REGRESSION APPROACHcscpconf
Due to the intangible nature of “software”, accurate and reliable software effort estimation is a challenge in the software Industry. It is unlikely to expect very accurate estimates of software
development effort because of the inherent uncertainty in software development projects and the complex and dynamic interaction of factors that impact software development. Heterogeneity exists in the software engineering datasets because data is made available from diverse sources.
This can be reduced by defining certain relationship between the data values by classifying them into different clusters. This study focuses on how the combination of clustering and
regression techniques can reduce the potential problems in effectiveness of predictive efficiency due to heterogeneity of the data. Using a clustered approach creates the subsets of data having a degree of homogeneity that enhances prediction accuracy. It was also observed in this study that ridge regression performs better than other regression techniques used in the analysis.
A Genetic Algorithm on Optimization Test FunctionsIJMERJOURNAL
ABSTRACT: Genetic Algorithms (GAs) have become increasingly useful over the years for solving combinatorial problems. Though they are generally accepted to be good performers among metaheuristic algorithms, most works have concentrated on the application of the GAs rather than the theoretical justifications. In this paper, we examine and justify the suitability of Genetic Algorithms in solving complex, multi-variable and multi-modal optimization problems. To achieve this, a simple Genetic Algorithm was used to solve four standard complicated optimization test functions, namely Rosenbrock, Schwefel, Rastrigin and Shubert functions. These functions are benchmarks to test the quality of an optimization procedure towards a global optimum. We show that the method has a quicker convergence to the global optima and that the optimal values for the Rosenbrock, Rastrigin, Schwefel and Shubert functions are zero (0), zero (0), -418.9829 and -14.5080 respectively
An Application of Genetic Algorithm for Non-restricted Space and Pre-determin...drboon
The use of a genetic algorithm is presented to solve a facility layout problem in the situation where there is non-restricted space but the ratio of plant length and width is pre-determined. A two-leveled chromosome is constructed. Six rules are established to translate the chromosome to facility design. An approach of solving a facility layout problem is proposed. A numerical example is employed to illustrate the approach.
The potential role of ai in the minimisation and mitigation of project delayPieter Rautenbach
Artificial intelligence (AI) can have wide reaching application within the construction
industry, however, the actual application of this set of technologies is currently under exploited. This
paper considers the role that the application of AI can take in optimising the efficiencies of project
execution and how this can potentially reduce project duration and minimise and mitigate delay on
projects.
ESTIMATING PROJECT DEVELOPMENT EFFORT USING CLUSTERED REGRESSION APPROACHcscpconf
Due to the intangible nature of “software”, accurate and reliable software effort estimation is a challenge in the software Industry. It is unlikely to expect very accurate estimates of software
development effort because of the inherent uncertainty in software development projects and the complex and dynamic interaction of factors that impact software development. Heterogeneity exists in the software engineering datasets because data is made available from diverse sources.
This can be reduced by defining certain relationship between the data values by classifying them into different clusters. This study focuses on how the combination of clustering and
regression techniques can reduce the potential problems in effectiveness of predictive efficiency due to heterogeneity of the data. Using a clustered approach creates the subsets of data having a degree of homogeneity that enhances prediction accuracy. It was also observed in this study that ridge regression performs better than other regression techniques used in the analysis.
Estimating project development effort using clustered regression approachcsandit
Due to the intangible nature of “software”, accurate and reliable software effort estimation is a
challenge in the software Industry. It is unlikely to expect very accurate estimates of software
development effort because of the inherent uncertainty in software development projects and the
complex and dynamic interaction of factors that impact software development. Heterogeneity
exists in the software engineering datasets because data is made available from diverse sources.
This can be reduced by defining certain relationship between the data values by classifying
them into different clusters. This study focuses on how the combination of clustering and
regression techniques can reduce the potential problems in effectiveness of predictive efficiency
due to heterogeneity of the data. Using a clustered approach creates the subsets of data having
a degree of homogeneity that enhances prediction accuracy. It was also observed in this study
that ridge regression performs better than other regression techniques used in the analysis.
Research Inventy : International Journal of Engineering and Scienceinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Feature selection using modified particle swarm optimisation for face recogni...eSAT Journals
Abstract
One of the major influential factors which affects the accuracy of classification rate is the selection of right features. Not all features have vital role in classification. Many of the features in the dataset may be redundant and irrelevant, which increase the computational cost and may reduce classification rate. In this paper, we used DCT(Discrete cosine transform) coefficients as features for face recognition application. The coefficients are optimally selected based on a modified PSO algorithm. In this, the choice of coefficients is done by incorporating the average of the mean normalized standard deviations of various classes and giving more weightage to the lower indexed DCT coefficients. The algorithm is tested on ORL database. A recognition rate of 97% is obtained. Average number of features selected is about 40 percent for a 10 × 10 input. The modified PSO took about 50 iterations for convergence. These performance figures are found to be better than some of the work reported in literature.
Keywords: Particle swarm optimization, Discrete cosine transform, feature extraction, feature selection, face recognition, classification rate.
ANP-GP Approach for Selection of Software Architecture StylesWaqas Tariq
Abstract Selection of Software Architecture for any system is a difficult task as many different stake holders are involved in the selection process. Stakeholders view on quality requirements is different and at times they may also be conflicting in nature. Also selecting appropriate styles for the software architecture is important as styles impact characteristics of software (e.g. reliability, performance). Moreover, styles influence how software is built as they determine architectural elements (e.g. components, connectors) and rules on how to integrate these elements in the architecture. Selecting the best style is difficult because there are multiple factors such as project risk, corporate goals, limited availability of resources, etc. Therefore this study presents a method, called SSAS, for the selection of software architecture styles. Moreover, this selection is a multi-criteria decision-making problem in which different goals and objectives must be taken into consideration. In this paper, we suggest an improved selection methodology, which reflects interdependencies among evaluation criteria and alternatives using analytic network process (ANP) within a zero-one goal programming (ZOGP) model. Keywords: Software Architecture; Selection of Software Architecture Styles; Multi-Criteria Decision Making; Interdependence; Analytic Network Process (ANP); Zero-One Goal Programming (ZOGP)
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
GRID COMPUTING: STRATEGIC DECISION MAKING IN RESOURCE SELECTIONIJCSEA Journal
The rapid development of computer networks around the world generated new areas especially in computer instruction processing. In grid computing, instruction processing is performed by external processors available to the system. An important topic in this area is task scheduling to available external resources. However, we do not deal with this topic here. In this paper we intend to work on strategic decision making on selecting the best alternative resources for processing instructions with respect to criteria in special conditions. Where the criteria might be security, political, technical, cost, etc. Grid computing should be determined with respect to the processing objectives of instructions of a program. This paper seeks a way through combining Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to help us in ranking and selecting available resources according to considerable criteria in allocating instructions to resources. Therefore, our findings will help technical managers of organizations in choosing as well as ranking candidate alternatives for processing program instructions.
Comparison of Cost Estimation Methods using Hybrid Artificial Intelligence on...IJERA Editor
Cost estimating at schematic design stage as the basis of project evaluation, engineering design, and cost
management, plays an important role in project decision under a limited definition of scope and constraints in
available information and time, and the presence of uncertainties. The purpose of this study is to compare the
performance of cost estimation models of two different hybrid artificial intelligence approaches: regression
analysis-adaptive neuro fuzzy inference system (RANFIS) and case based reasoning-genetic algorithm (CBRGA)
techniques. The models were developed based on the same 50 low-cost apartment project datasets in
Indonesia. Tested on another five testing data, the models were proven to perform very well in term of accuracy.
A CBR-GA model was found to be the best performer but suffered from disadvantage of needing 15 cost drivers
if compared to only 4 cost drivers required by RANFIS for on-par performance.
Nonlinear Programming: Theories and Algorithms of Some Unconstrained Optimiza...Dr. Amarjeet Singh
Nonlinear programming problem (NPP) had become an important branch of operations research, and it was the mathematical programming with the objective function or constraints being nonlinear functions. There were a variety of traditional methods to solve nonlinear programming problems such as bisection method, gradient projection method, the penalty function method, feasible direction method, the multiplier method. But these methods had their specific scope and limitations, the objective function and constraint conditions generally had continuous and differentiable request. The traditional optimization methods were difficult to adopt as the optimized object being more complicated. However, in this paper, mathematical programming techniques that are commonly used to extremize nonlinear functions of single and multiple (n) design variables subject to no constraints are been used to overcome the above challenge. Although most structural optimization problems involve constraints that bound the design space, study of the methods of unconstrained optimization is important for several reasons. Steepest Descent and Newton’s methods are employed in this paper to solve an optimization problem.
A Non-Revisiting Genetic Algorithm for Optimizing Numeric Multi-Dimensional F...ijcsa
Genetic Algorithm (GA) is a robust and popular stochastic optimization algorithm for large and complex search spaces. The major shortcomings of Genetic Algorithms are premature convergence and revisits to individual solutions in the search space. In other words, Genetic algorithm is a revisiting algorithm that escorts to duplicate function evaluations which is a clear wastage of time and computational resources. In this paper, a non-revisiting genetic algorithm with adaptive mutation is proposed for the domain of MultiDimensional numeric function optimization. In this algorithm whenever a revisit occurs, the underlined search point is replaced with a mutated version of the best/random (chosen probabilistically) individual from the GA population. Furthermore, the recommended approach is not using any extra memory resources to avoid revisits. To analyze the influence of the method, the proposed non-revisiting algorithm is evaluated using nine benchmarks functions with two and four dimensions. The performance of the proposed genetic algorithm is superior as contrasted to simple genetic algorithm as confirmed by the experimental results.
A HYBRID COA/ε-CONSTRAINT METHOD FOR SOLVING MULTI-OBJECTIVE PROBLEMSijfcstjournal
In this paper, a hybrid method for solving multi-objective problem has been provided. The proposed method is combining the ε-Constraint and the Cuckoo algorithm. First the multi objective problem transfers into a single-objective problem using ε-Constraint, then the Cuckoo optimization algorithm will optimize the problem in each task. At last the optimized Pareto frontier will be drawn. The advantage of
this method is the high accuracy and the dispersion of its Pareto frontier. In order to testing the efficiency of the suggested method, a lot of test problems have been solved using this method. Comparing the results of this method with the results of other similar methods shows that the Cuckoo algorithm is more suitable for solving the multi-objective problems.
An Heterogeneous Population-Based Genetic Algorithm for Data Clusteringijeei-iaes
As a primary data mining method for knowledge discovery, clustering is a technique of classifying a dataset into groups of similar objects. The most popular method for data clustering K-means suffers from the drawbacks of requiring the number of clusters and their initial centers, which should be provided by the user. In the literature, several methods have proposed in a form of k-means variants, genetic algorithms, or combinations between them for calculating the number of clusters and finding proper clusters centers. However, none of these solutions has provided satisfactory results and determining the number of clusters and the initial centers are still the main challenge in clustering processes. In this paper we present an approach to automatically generate such parameters to achieve optimal clusters using a modified genetic algorithm operating on varied individual structures and using a new crossover operator. Experimental results show that our modified genetic algorithm is a better efficient alternative to the existing approaches.
Analysis of selection schemes for solving job shop scheduling problem using g...eSAT Journals
Abstract Scheduling problems have the standard consideration in the field of manufacturing. Among the various types of scheduling problems, the job shop scheduling problem is one of the most interesting NP-hard problems. As the job shop scheduling is an optimization problem, Genetic algorithm was selected to solve it In this study. Selection scheme is one of the important operators of Genetic algorithm. The choice of selection method to be applied for solving problems has a wide role in the Genetic algorithm process. The speed of convergence towards the optimum solution for the chosen problem is largely determined by the selection mechanism used in the Genetic algorithm. Depending upon the selection scheme applied, the population fitness over the successive generations could be improved. There are various type of selection schemes in genetic algorithm are available, where each selection scheme has its own feasibility for solving a particular problem. In this study, the selection schemes namely Stochastic Universal Sampling (SUS), Roulette Wheel Selection (RWS), Rank Based Roulette Wheel Selection (RRWS) and Binary Tournament Selection (BTS) were chosen for implementation. The characteristics of chosen selection mechanisms of Genetic algorithm for solving the job shop scheduling problem were analyzed. The Genetic algorithm with four different selection schemes is tested on instances of 7 benchmark problems of different size. The result shows that the each of the four selection schemes of Genetic algorithm have been successfully applied to the job shop scheduling problems efficiently and the performance of Stochastic Universal Sampling selection method is better than all other four selection schemes. Keywords: Genetic Algorithm, Makespan, Selection schemes
Project Risk management is an integral part for business survival. This research paper focuses on determining project risk factors using genetic algorithm and fuzzy logic base on the demerits of conventional approaches. Genetic algorithm help optimise the parameters data items while fuzzy logic handle imprecisions. Unified Modelling Language was utilized for modelling the software system, depicting clearly the interaction between various components and the dynamic aspect of the system. This paper demonstrates the practical application of metric based soft computing techniques in the health sector in determining patient’s satisfaction
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.
Abstract: Wireless location finding is one of the key technologies for wireless sensor networks. GPS is the technology used but it can be used for the outdoor location. When we deal with the indoor locations GPS does not work. Indoor locations include buildings like supermarkets, big malls, parking, universities, and locations under the same roof. In these areas the accuracy of the GPS location is greatly reduced. Location showed on the map in not correct when the GPS is used under the indoor environments. But for the indoor localization it requires the higher accuracy sp GPS is not feasible for the current view. And also when the GPS is used in the mobile device it consumes a lot of the mobile battery to run the application which causes the drainage of the mobile battery within some hours. So to find out the accurate location for indoor environment we use the RSSI based trilateral localization algorithm. The algorithm has the low cost and the algorithm does not require any additional hardware support and moreover the algorithm is easy to understand. The algorithm consumes very less battery as compared to the battery consumption of the GPS. Because of these this algorithm has become the mainstream localization algorithm in the wireless sensor networks. With the development of the wireless sensor networks and the smart devices the WIFI access points are also increasing. The mobile smart devices detect three or more known WIFI hotspots positions. And using the values from the WIFI routers it calculates the current location of the mobile device. In this paper we have proposed a system so that we can find out the exact location of the mobile device under the indoor environment and can navigate to the destination using the navigation function and also can enable the low consumption of the smart mobile battery for the tracking purpose.
Goals:
1. Useful at the places where GPS cannot work
2. Reduces the battery consumption
3. Routers are used.
4. Provides the path as well as the information of the location as per the requirement of user.
Design and Implementation of Maximum Power Point Tracking Using Fuzzy Logic C...paperpublications3
Abstract: The maximum power point tracking (MPPT) is a process which tracks maximum power point from array input, varying the ratio between the voltage and current delivered to get the most power it can. This paper proposes Maximum Power Point Tracking (MPPT) of a photovoltaic system using Fuzzy Logic Algorithm. For efficient utilization of solar energy, the PV panel should track the maximum power point (MPP) under various weather conditions. Boost converter increases output voltage of the solar panel and converter output voltage depends upon the duty cycle of the MOSFET present in the boost converter. The change in the duty cycle is done by Fuzzy logic controller by sensing the power output of the solar panel. Fuzzy logic controller (FLC) provides an adaptive nature, fast response, good performance and ability to handle non-linear characteristics. The proposed controller is aimed at adjusting the duty cycle of the DC-DC converter switch to track the maximum power of a solar cell array. MATLAB/SIMULINK is used to develop and design the PV array system equipped with the proposed MPPT controller using fuzzy logic.
Estimating project development effort using clustered regression approachcsandit
Due to the intangible nature of “software”, accurate and reliable software effort estimation is a
challenge in the software Industry. It is unlikely to expect very accurate estimates of software
development effort because of the inherent uncertainty in software development projects and the
complex and dynamic interaction of factors that impact software development. Heterogeneity
exists in the software engineering datasets because data is made available from diverse sources.
This can be reduced by defining certain relationship between the data values by classifying
them into different clusters. This study focuses on how the combination of clustering and
regression techniques can reduce the potential problems in effectiveness of predictive efficiency
due to heterogeneity of the data. Using a clustered approach creates the subsets of data having
a degree of homogeneity that enhances prediction accuracy. It was also observed in this study
that ridge regression performs better than other regression techniques used in the analysis.
Research Inventy : International Journal of Engineering and Scienceinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Feature selection using modified particle swarm optimisation for face recogni...eSAT Journals
Abstract
One of the major influential factors which affects the accuracy of classification rate is the selection of right features. Not all features have vital role in classification. Many of the features in the dataset may be redundant and irrelevant, which increase the computational cost and may reduce classification rate. In this paper, we used DCT(Discrete cosine transform) coefficients as features for face recognition application. The coefficients are optimally selected based on a modified PSO algorithm. In this, the choice of coefficients is done by incorporating the average of the mean normalized standard deviations of various classes and giving more weightage to the lower indexed DCT coefficients. The algorithm is tested on ORL database. A recognition rate of 97% is obtained. Average number of features selected is about 40 percent for a 10 × 10 input. The modified PSO took about 50 iterations for convergence. These performance figures are found to be better than some of the work reported in literature.
Keywords: Particle swarm optimization, Discrete cosine transform, feature extraction, feature selection, face recognition, classification rate.
ANP-GP Approach for Selection of Software Architecture StylesWaqas Tariq
Abstract Selection of Software Architecture for any system is a difficult task as many different stake holders are involved in the selection process. Stakeholders view on quality requirements is different and at times they may also be conflicting in nature. Also selecting appropriate styles for the software architecture is important as styles impact characteristics of software (e.g. reliability, performance). Moreover, styles influence how software is built as they determine architectural elements (e.g. components, connectors) and rules on how to integrate these elements in the architecture. Selecting the best style is difficult because there are multiple factors such as project risk, corporate goals, limited availability of resources, etc. Therefore this study presents a method, called SSAS, for the selection of software architecture styles. Moreover, this selection is a multi-criteria decision-making problem in which different goals and objectives must be taken into consideration. In this paper, we suggest an improved selection methodology, which reflects interdependencies among evaluation criteria and alternatives using analytic network process (ANP) within a zero-one goal programming (ZOGP) model. Keywords: Software Architecture; Selection of Software Architecture Styles; Multi-Criteria Decision Making; Interdependence; Analytic Network Process (ANP); Zero-One Goal Programming (ZOGP)
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
GRID COMPUTING: STRATEGIC DECISION MAKING IN RESOURCE SELECTIONIJCSEA Journal
The rapid development of computer networks around the world generated new areas especially in computer instruction processing. In grid computing, instruction processing is performed by external processors available to the system. An important topic in this area is task scheduling to available external resources. However, we do not deal with this topic here. In this paper we intend to work on strategic decision making on selecting the best alternative resources for processing instructions with respect to criteria in special conditions. Where the criteria might be security, political, technical, cost, etc. Grid computing should be determined with respect to the processing objectives of instructions of a program. This paper seeks a way through combining Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to help us in ranking and selecting available resources according to considerable criteria in allocating instructions to resources. Therefore, our findings will help technical managers of organizations in choosing as well as ranking candidate alternatives for processing program instructions.
Comparison of Cost Estimation Methods using Hybrid Artificial Intelligence on...IJERA Editor
Cost estimating at schematic design stage as the basis of project evaluation, engineering design, and cost
management, plays an important role in project decision under a limited definition of scope and constraints in
available information and time, and the presence of uncertainties. The purpose of this study is to compare the
performance of cost estimation models of two different hybrid artificial intelligence approaches: regression
analysis-adaptive neuro fuzzy inference system (RANFIS) and case based reasoning-genetic algorithm (CBRGA)
techniques. The models were developed based on the same 50 low-cost apartment project datasets in
Indonesia. Tested on another five testing data, the models were proven to perform very well in term of accuracy.
A CBR-GA model was found to be the best performer but suffered from disadvantage of needing 15 cost drivers
if compared to only 4 cost drivers required by RANFIS for on-par performance.
Nonlinear Programming: Theories and Algorithms of Some Unconstrained Optimiza...Dr. Amarjeet Singh
Nonlinear programming problem (NPP) had become an important branch of operations research, and it was the mathematical programming with the objective function or constraints being nonlinear functions. There were a variety of traditional methods to solve nonlinear programming problems such as bisection method, gradient projection method, the penalty function method, feasible direction method, the multiplier method. But these methods had their specific scope and limitations, the objective function and constraint conditions generally had continuous and differentiable request. The traditional optimization methods were difficult to adopt as the optimized object being more complicated. However, in this paper, mathematical programming techniques that are commonly used to extremize nonlinear functions of single and multiple (n) design variables subject to no constraints are been used to overcome the above challenge. Although most structural optimization problems involve constraints that bound the design space, study of the methods of unconstrained optimization is important for several reasons. Steepest Descent and Newton’s methods are employed in this paper to solve an optimization problem.
A Non-Revisiting Genetic Algorithm for Optimizing Numeric Multi-Dimensional F...ijcsa
Genetic Algorithm (GA) is a robust and popular stochastic optimization algorithm for large and complex search spaces. The major shortcomings of Genetic Algorithms are premature convergence and revisits to individual solutions in the search space. In other words, Genetic algorithm is a revisiting algorithm that escorts to duplicate function evaluations which is a clear wastage of time and computational resources. In this paper, a non-revisiting genetic algorithm with adaptive mutation is proposed for the domain of MultiDimensional numeric function optimization. In this algorithm whenever a revisit occurs, the underlined search point is replaced with a mutated version of the best/random (chosen probabilistically) individual from the GA population. Furthermore, the recommended approach is not using any extra memory resources to avoid revisits. To analyze the influence of the method, the proposed non-revisiting algorithm is evaluated using nine benchmarks functions with two and four dimensions. The performance of the proposed genetic algorithm is superior as contrasted to simple genetic algorithm as confirmed by the experimental results.
A HYBRID COA/ε-CONSTRAINT METHOD FOR SOLVING MULTI-OBJECTIVE PROBLEMSijfcstjournal
In this paper, a hybrid method for solving multi-objective problem has been provided. The proposed method is combining the ε-Constraint and the Cuckoo algorithm. First the multi objective problem transfers into a single-objective problem using ε-Constraint, then the Cuckoo optimization algorithm will optimize the problem in each task. At last the optimized Pareto frontier will be drawn. The advantage of
this method is the high accuracy and the dispersion of its Pareto frontier. In order to testing the efficiency of the suggested method, a lot of test problems have been solved using this method. Comparing the results of this method with the results of other similar methods shows that the Cuckoo algorithm is more suitable for solving the multi-objective problems.
An Heterogeneous Population-Based Genetic Algorithm for Data Clusteringijeei-iaes
As a primary data mining method for knowledge discovery, clustering is a technique of classifying a dataset into groups of similar objects. The most popular method for data clustering K-means suffers from the drawbacks of requiring the number of clusters and their initial centers, which should be provided by the user. In the literature, several methods have proposed in a form of k-means variants, genetic algorithms, or combinations between them for calculating the number of clusters and finding proper clusters centers. However, none of these solutions has provided satisfactory results and determining the number of clusters and the initial centers are still the main challenge in clustering processes. In this paper we present an approach to automatically generate such parameters to achieve optimal clusters using a modified genetic algorithm operating on varied individual structures and using a new crossover operator. Experimental results show that our modified genetic algorithm is a better efficient alternative to the existing approaches.
Analysis of selection schemes for solving job shop scheduling problem using g...eSAT Journals
Abstract Scheduling problems have the standard consideration in the field of manufacturing. Among the various types of scheduling problems, the job shop scheduling problem is one of the most interesting NP-hard problems. As the job shop scheduling is an optimization problem, Genetic algorithm was selected to solve it In this study. Selection scheme is one of the important operators of Genetic algorithm. The choice of selection method to be applied for solving problems has a wide role in the Genetic algorithm process. The speed of convergence towards the optimum solution for the chosen problem is largely determined by the selection mechanism used in the Genetic algorithm. Depending upon the selection scheme applied, the population fitness over the successive generations could be improved. There are various type of selection schemes in genetic algorithm are available, where each selection scheme has its own feasibility for solving a particular problem. In this study, the selection schemes namely Stochastic Universal Sampling (SUS), Roulette Wheel Selection (RWS), Rank Based Roulette Wheel Selection (RRWS) and Binary Tournament Selection (BTS) were chosen for implementation. The characteristics of chosen selection mechanisms of Genetic algorithm for solving the job shop scheduling problem were analyzed. The Genetic algorithm with four different selection schemes is tested on instances of 7 benchmark problems of different size. The result shows that the each of the four selection schemes of Genetic algorithm have been successfully applied to the job shop scheduling problems efficiently and the performance of Stochastic Universal Sampling selection method is better than all other four selection schemes. Keywords: Genetic Algorithm, Makespan, Selection schemes
Project Risk management is an integral part for business survival. This research paper focuses on determining project risk factors using genetic algorithm and fuzzy logic base on the demerits of conventional approaches. Genetic algorithm help optimise the parameters data items while fuzzy logic handle imprecisions. Unified Modelling Language was utilized for modelling the software system, depicting clearly the interaction between various components and the dynamic aspect of the system. This paper demonstrates the practical application of metric based soft computing techniques in the health sector in determining patient’s satisfaction
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.
Abstract: Wireless location finding is one of the key technologies for wireless sensor networks. GPS is the technology used but it can be used for the outdoor location. When we deal with the indoor locations GPS does not work. Indoor locations include buildings like supermarkets, big malls, parking, universities, and locations under the same roof. In these areas the accuracy of the GPS location is greatly reduced. Location showed on the map in not correct when the GPS is used under the indoor environments. But for the indoor localization it requires the higher accuracy sp GPS is not feasible for the current view. And also when the GPS is used in the mobile device it consumes a lot of the mobile battery to run the application which causes the drainage of the mobile battery within some hours. So to find out the accurate location for indoor environment we use the RSSI based trilateral localization algorithm. The algorithm has the low cost and the algorithm does not require any additional hardware support and moreover the algorithm is easy to understand. The algorithm consumes very less battery as compared to the battery consumption of the GPS. Because of these this algorithm has become the mainstream localization algorithm in the wireless sensor networks. With the development of the wireless sensor networks and the smart devices the WIFI access points are also increasing. The mobile smart devices detect three or more known WIFI hotspots positions. And using the values from the WIFI routers it calculates the current location of the mobile device. In this paper we have proposed a system so that we can find out the exact location of the mobile device under the indoor environment and can navigate to the destination using the navigation function and also can enable the low consumption of the smart mobile battery for the tracking purpose.
Goals:
1. Useful at the places where GPS cannot work
2. Reduces the battery consumption
3. Routers are used.
4. Provides the path as well as the information of the location as per the requirement of user.
Design and Implementation of Maximum Power Point Tracking Using Fuzzy Logic C...paperpublications3
Abstract: The maximum power point tracking (MPPT) is a process which tracks maximum power point from array input, varying the ratio between the voltage and current delivered to get the most power it can. This paper proposes Maximum Power Point Tracking (MPPT) of a photovoltaic system using Fuzzy Logic Algorithm. For efficient utilization of solar energy, the PV panel should track the maximum power point (MPP) under various weather conditions. Boost converter increases output voltage of the solar panel and converter output voltage depends upon the duty cycle of the MOSFET present in the boost converter. The change in the duty cycle is done by Fuzzy logic controller by sensing the power output of the solar panel. Fuzzy logic controller (FLC) provides an adaptive nature, fast response, good performance and ability to handle non-linear characteristics. The proposed controller is aimed at adjusting the duty cycle of the DC-DC converter switch to track the maximum power of a solar cell array. MATLAB/SIMULINK is used to develop and design the PV array system equipped with the proposed MPPT controller using fuzzy logic.
Performance Comparison of FFT, DHT and DCT Based OFDM Systems with BPSK as A ...paperpublications3
Abstract: Today, OFDM has grown to be the most popular communication system in high-speed communications. OFDM is becoming the chosen modulation technique for wireless communications. OFDM can provide large data rates with sufficient robustness to radio channel impairments. Different type of transform techniques such as Discrete Hartley transform (DHT), Discrete Cosine Transform (DCT), Fast Fourier Transform (FFT) are used to perform the modulation and demodulation operations for the OFDM system. In this paper these three transforms are used in OFDM and study the comparison between these different transforms techniques used in OFDM system based on Bit Error Rate (BER) performances.
A Novel Approach for Allocation of Optimal Capacitor and Distributed Generati...paperpublications3
Abstract: Distributed generation (DG) units, based on their interfacing technology are divided into synchronous generator interfaced DGs, asynchronous generator interfaced DGs and inverter interfaced DGs. This paper presents two algorithms for allocation of optimal capacitor and distributed generation on radial distribution system. These algorithms predict requirement of reactive vars and real power and supplied via capacitor banks and distributed generation. This arrangement reduces transmission losses and voltage stability problem. Developed algorithm has been implemented on two IEEE 69 nodes and 52 nodes systems.
Abstract: Traditionally electrical appliances in a home are controlled via switches that regulate the electricity to these devices. As the world gets more and more technologically advanced, we find new technology coming in deeper and deeper into our personal lives even at home. Home automation is becoming more and more popular around the world and is becoming a common practice. The process of home automation works by making everything in the house automatically controlled using technology to control and do the jobs that we would normally do manually. Home automation takes care of a lot of different activities in the house. this project we propose a unique System for Home automation utilizing Dual Tone Multi Frequency (DTMF) that is paired with a wireless module to provide seamless wireless control over many devices in a house. This user console has many keys, each corresponding to the device that needs to be activated. The encoder encodes the user choice and sends via a FM transmitter. The FM receiver receives the modulated signal and demodulates it and the user choice is determined by the DTMF decoder. Based upon this the required appliance is triggered. Dtmf access the control unit, the user should send an authentication code (DTMF) along with their quire/desired function/action to his/her home control system via Global System for Mobile communication (GSM). Upon being properly authenticated, the cell phone-based interface at home (control unit) would relay the commands to a microcontroller that would perform the required function/action, and return a function completion code that would be sent to the source of the original command (user’s cell phone).
Hardware Unit for Edge Detection with Comparative Analysis of Different Edge ...paperpublications3
Abstract: An edge in an image is a contour across which the brightness of the image changes abruptly. In image processing, an edge is often interpreted as one class of singularities. Edge detection is an important task in image processing. It is a main tool in pattern recognition, image segmentation, and scene analysis. An edge detector is basically a high pass filter that can be applied to extract the edge points in an image. This topic has attracted many researchers and many achievements have been made. Many researchers provided different approaches based on mathematical calculations which some of them are either robust or cost effective. A new algorithm will be proposed to detect the edges of image with increased robustness and throughput. Using this algorithm we will reduce the time complexity problem which is faced by previous algorithm. We will also propose hardware unit for proposed algorithm which will reduce the area, power and speed problem. We will compare our proposed algorithm with previous approach. For image quality measurement we will use some scientific parameters those are PSNR, SSIM, FSIM. Implementation of proposed algorithm will be done by Matlab and hardware implementation will be done by using of Verilog on Xilinx 14.1 simulator. Verification will be done on Model sim.
RELIABILITY BASED POWER DISTRIBUTION SYSTEMS PLANNING USING CREDIBILITY THEORYpaperpublications3
Abstract: This paper presents an analytical technique for distribution system planning based on reliability evaluation using credibility theory. With the development of economy and society power planning is facing with the influence of much uncertainty, which are mainly power distribution network. Power system especially at the distribution level is prone to failures and disturbances as many devices are responsible for the successful operation of a radial distribution system. We also mention whether the work has been done at the strategic level, i.e. if it concerns the planning of power distribution system based on reliability and uncertainty.
Design of Shunt Active Power Filter to eliminate harmonics generated by CFLpaperpublications3
Abstract: The use of non-linear loads; such as TV sets and computer, microwave ovens, multiple low power diode rectifier, fluorescent lamps and electric drives, draw very distorted currents. These non-linear loads lead to generation of current/voltage harmonics and draw reactive power. This paper presents the three-phase shunt active power filter (SAPF) to compensate harmonics generated by non-linear load (compact fluorescent lamp). The instantaneous active and reactive power theory (called p-q theory) is used to design the control of SAPF. The harmonic distortion and the active filter control scheme have been verified by MATLAB simulation.
A Comparative Performance Analysis DCR and DAR Squirrel Cage 3-Phase Inductio...paperpublications3
Abstract: The importance of energy saving in induction motor was emphasized about 15 years ago, in academic area, but the motor manufacturer’s interest is focussed only on maximum benefit. As a customer, it is better to take into account not only the motor price, but also the cost of the used energy during the whole lifetime of the motor. The new requirement to improve the motor efficiency is a serious research subject, which must be about the possibility of loss minimization in the induction motor.
On an average the cost of energy consumed by the motor is nearly 80 – 100 times the initial cost of the initial manufacturing cost of the motor. So the efficiency of motor is of great importance whether during the selection or during the operation. Small increase in motor efficiency can make an overall significant difference in total energy consumption.
The slightly higher initial cost of DCR motors is often misunderstood as a demerit. It is not all true. The increase in initial cost is offset by the energy saving.
This paper presents the comparative analysis of performance of a DCR motor and DAR motor. The method of analysis is based upon testing results. The only change in design is that the Die cast aluminium rotor is replaced by die cast copper rotor. The other design parameters like stator core, winding, air gap length etc are remains same.
Abstract: This paper is focused on the description of the possible benefits for the electric utilities and residential customers from the Automatic meter reading system usage. Major benefits of the AMR, mentioned in this paper are power quality monitoring, distribution network management, theft detection and so on. The paper also gives the idea about the reliability indices, communication topologies, AT&C losses concept in distribution system, present and last situations of the AMR integration in power utilities.
Importance of Antennas for Wireless Communication Devicespaperpublications3
Abstract: The extensive demand for mobile communication and information exchange through wireless devices has lead to major achievements in antenna designing. The purpose of the paper is to give a frame of reference, understanding, and overview of antennas used in wireless communication devices. In this paper we will be discussing various antennas, their advantages and drawbacks. Also a brief framework of comparisons between various antennas is presented on the basis of various parameters. This paper also summarizes the benefits and use of PIFA for USB dongle to cover the Wi MAX bands.
Abstract: We need energy for every day today work of our life. There are many conventional methods of energy generation but these are depleting very fastly hence non-conventional energy system is very essential at this time to our nation. So an alternate method of non conventional energy generation is proposed in this project. In this project we are generating electrical power as non-conventional method by simply walking or running on foot step.Here Dynamometer is used for converting mechanical energy into electrical energy. The voltage generated by this sensor is stored in battery which will be later on transmitted wirelessly to charge the mobiles.
A Literature Review on Experimental Study of Power Losses in Transmission Lin...paperpublications3
Abstract: The flexible Ac transmission system (FACTS) controllers can play an important role in the power system security enhancement. However, due to high capital investment, it is necessary to locate these controllers optimally in the power system. FACTS devices can regulate the active and reactive power control as well as adaptive to voltage-magnitude control simultaneously because of their flexibility and fast control characteristics. Placement of these devices in suitable location can lead to control in line flow and maintain bus voltages in desired level and so improve voltage stability margins. In the previous paper three type of FACTS devices used in transmission lines for improvement of voltage profile in the power system. This paper describes the simulation result of flexible Alternative Current Transmission Systems (FACTS) devices used in the disturbed power systems. Out of three types of FACTS device UPFC performances is considered to be best comparatively with respect to each of the three devices.
Abstract: Energy efficiency in all the aspects of human life has become a major concern, due to significant environment impact as well as it economic importance. Information and Communication Technology (ICT) estimated 2-10% of the global consumption but is also expected to enable global energy efficiency through new technologies tightly dependent on networks. Specially, a network model based on G-network quening theory is built, which can incorporate all the important parameters of power consumption together with traditional performance metric and routing control capability. Our goal is to control both power configuration of pipeline and way to distribute traffic flow among them. Optimization policy having best tradeoff between power consumption and packet latency times. The achieved results demonstrate how the proposed model can effectively represent energy and network-aware performance indexes.
Abstract: The Major contributor to power dissipation using signal switching of high frequency and strong pipeline designs. Total power consumption using clock tree accounts and 20-40% of power is consumed in synchronous circuits. Therefore, by decreasing the clock-tree power, the chip power reduces. In this proposed system a circuit is implemented which is a frequency synthesizer for generating the clock tree. Then the gated latch technique is applied to this circuit for reducing the power consumption. The resulting output is the generation of the clock tree. The clock power can be controlled through gating. Primary goal is to reduce the power in the clock tree. Thus the clock tree synthesis is performed and gated latch technique is applied to the out coming clock signal from the clock tree. The gated latch is the concept of gating the control signal. The gated signal output plays the role of the enable in the latches. Thus whenever we need we can switch off the clock signal and we can use the clock signal for clock tree synthesis. These are all controlled by the control signal. The power consumed is 0.052.
Abstract: In this paper three phase load flow analysis on four bus system using Mi Power software is reformed. As power system never operates under steady state condition therefore single phase load flow analysis doesn’t provide accurate results. Hence three phase load flow analysis which can be performed under different contingencies, provide data when system is unbalanced. The system is analysing on the basis of parameter values in MW & MVAR for transmission line and generator buses. Harmonic values of resistance, reactance, and susceptance can predict the condition of small and large kind of system network. This type of analysis is useful for solving the power flow problem in different power systems which will useful to calculate the unknown parameter.
Abstract: Watermarking is mainly projected for copy right protection, data safeguard, and data thrashing, etc. Nowadays all the communication requires protection. Estimation of video quality has a major role in today’s video distribution, communication control and e-commerce. Consumer fulfillment is achieved by providing good quality. Here the video input is changed into frames and the image set as watermark is embedded into the frames. The embedding process is carried out using DWT, then the embedded frame and other remaining frames are again changed into video file and it is transmitted. At the receiver side watermark image is extracted from the video. Finally, by using metrics such as TDR, PSNR the quality of watermark image is estimated under distortion. All experiments and tests are carried out using MATLAB.
MODELLING AND IMPLEMENTATION OF AN IMPROVED DSVM SCHEME FOR PMSM DTCpaperpublications3
Abstract: A very widely used drive strategy for PMSM is the field oriented control (FOC), which was proposed in 1971 for induction motors (IMs). However, the FOC scheme is quite complex due to the reference frame transformation and its high dependence upon the motor parameters and speed. To mitigate these problems, a new control strategy for the torque control of induction motor was developed by Takahashi known as the direct torque control (DTC) and by Depenbrock as the direct self control (DSC). The basic direct torque control (DTC) scheme may cause undesired torque, flux and current ripples because of the small number of applicable voltage vectors. The control system should be able to generate any voltage vector, implying the use of space vector modulation (SVM) which complicates the control scheme. The discrete space vector modulation (DSVM) method was proposed for DTC to overcome this problem which replaces the simple switching table by several switching tables, to apply a combination of three voltage vectors in the same sampling period. In this paper, after a brief review of the primary concept of DSVM DTC technique, a new scheme of DSVM DTC for PMSM is proposed with a new set of switching tables taking into account the motor speed and the absolute values of torque and flux feedback errors. In one fixed sampling time interval, three vectors are applied to the motor including the two null vectors. Comparisons of the basic DTC and the improved DSVM DTC schemes are made based on the system performance and switching loss. For this purpose the DSVM technique uses prefixed time intervals within a sampling cycle to synthesize a higher number of voltage vectors than the basic DTC scheme. A set of switching table is carried out to minimize the torque error. An optimal vector selector is developed to reduce the switching loss and make the system more stable. The sampling period does not need to be doubled in order to achieve a mean switching frequency practically equal to that of the basic DTC scheme. For a comparable performance, the switching loss of the proposed scheme is less than that of the basic DTC method. The vector application sequence is investigated and an optimal algorithm is developed to reduce the switching loss and torque ripple. Simulation and experiments on the improved DSVM DTC are carried out and compared with those on the basic DTC scheme.
A Study On Double Gate Field Effect Transistor For Area And Cost Efficiencypaperpublications3
Abstract: Proposal for a field effect transistor had been presented, with numerical device simulations to verify the title in every manner possible. The two transitional field effect transistors like pMOS and nMOS functions are simultaneously performed, working as one or as the other according to the voltage applied to the gate terminal. Increase in the circuit speed is observed when this technology is implemented on the device suggested with respect to the standard CMOS technology, presented a drastic reduction of number devices and associated parasitic capacitances. In addition to it IC obtained with the proposed device are fully compatible with the standard CMOS technology and the fabrication processes. Fabrication of Static Ram cells with three transistors only with minimum dimensions and a single bit line by saving silicon area and increasing the memory performance with respect to standard CMOS technologies. It is also presented that the fully compatible CMOS process can be used to successfully manufacture the new FET structure.
SMS Based Automatic Vehicle Accident Information Systempaperpublications3
Abstract: In highly populated Countries like India, during accidents, people lose their lives due to unavailability of proper medical facilities at the right time. This project senses any accident in the vehicle and intimates pre-programmed numbers like the owner of the vehicle, ambulance, police etc. Whenever any accident takes place, the vehicle will experience a huge amount of impulse, this impulse is then been measured with the help of the force sensor which is connected to the vehicle. The impulse that is measured is then converted to electrical voltage unit which helps the microcontroller as it is interfaced with GSM. This change in digital signal will activate the GSM module installed in the vehicle and it will automatically send the saved SMS to the required numbers so that the proper medical aid can be provided to the person.
COMPARISON BETWEEN THE GENETIC ALGORITHMS OPTIMIZATION AND PARTICLE SWARM OPT...IAEME Publication
Close range photogrammetry network design is referred to the process of placing a set of
cameras in order to achieve photogrammetric tasks. The main objective of this paper is tried to find
the best location of two/three camera stations. The genetic algorithm optimization and Particle
Swarm Optimization are developed to determine the optimal camera stations for computing the three
dimensional coordinates. In this research, a mathematical model representing the genetic algorithm
optimization and Particle Swarm Optimization for the close range photogrammetry network is
developed. This paper gives also the sequence of the field operations and computational steps for this
task. A test field is included to reinforce the theoretical aspects.
For three decades, many mathematical programming methods have been developed to solve optimization problems. However, until now, there has not been a single totally efficient and robust method to coverall optimization problems that arise in the different engineering fields.Most engineering application design problems involve the choice of design variable values that better describe the behaviour of a system.At the same time, those results should cover the requirements and specifications imposed by the norms for that system. This last condition leads to predicting what the entrance parameter values should be whose design results comply with the norms and also present good performance, which describes the inverse problem.Generally, in design problems the variables are discreet from the mathematical point of view. However, most mathematical optimization applications are focused and developed for continuous variables. Presently, there are many research articles about optimization methods; the typical ones are based on calculus,numerical methods, and random methods.
The calculus-based methods have been intensely studied and are subdivided in two main classes: 1) the direct search methods find a local maximum moving a function over the relative local gradient directions and 2) the indirect methods usually find the local ends solving a set of non-linear equations, resultant of equating the gradient from the object function to zero, i.e., by means of multidimensional generalization of the notion of the function’s extreme points from elementary calculus given smooth function without restrictions to find a possible maximum which is to be restricted to those points whose slope is zero in all directions. The real world has many discontinuities and noisy spaces, which is why it is not surprising that the methods depending upon the restrictive requirements of continuity and existence of a derivative, are unsuitable for all, but a very limited problem domain. A number of schemes have been applied in many forms and sizes. The idea is quite direct inside a finite search space or a discrete infinite search space, where the algorithms can locate the object function values in each space point one at a time. The simplicity of this kind of algorithm is very attractive when the numbers of possibilities are very small. Nevertheless, these outlines are often inefficient, since they do not complete the requirements of robustness in big or highly-dimensional spaces, making it quite a hard task to find the optimal values. Given the shortcomings of the calculus-based techniques and the numerical ones the random methods have increased their popularity.
In real world applications, most of the optimization problems involve more than one objective to
be optimized. The objectives in most of engineering problems are often conflicting, i.e., maximize
performance, minimize cost, maximize reliability, etc. In the case, one extreme solution would not satisfy
both objective functions and the optimal solution of one objective will not necessary be the best solution
for other objective(s). Therefore different solutions will produce trade-offs between different objectives
and a set of solutions is required to represent the optimal solutions of all objectives. Multi-objective
formulations are realistic models for many complex engineering optimization problems. Customized
genetic algorithms have been demonstrated to be particularly effective to determine excellent solutions to
these problems. A reasonable solution to a multi-objective problem is to investigate a set of solutions, each
of which satisfies the objectives at an acceptable level without being dominated by any other solution. In
this paper, an overview is presented describing various multi objective genetic algorithms developed to
handle different problems with multiple objectives.
Multi-Population Methods with Adaptive Mutation for Multi-Modal Optimization ...ijscai
This paper presents an efficient scheme to locate multiple peaks on multi-modal optimization problems by
using genetic algorithms (GAs). The premature convergence problem shows due to the loss of diversity,
the multi-population technique can be applied to maintain the diversity in the population and the
convergence capacity of GAs. The proposed scheme is the combination of multi-population with adaptive
mutation operator, which determines two different mutation probabilities for different sites of the
solutions. The probabilities are updated by the fitness and distribution of solutions in the search space
during the evolution process. The experimental results demonstrate the performance of the proposed
algorithm based on a set of benchmark problems in comparison with relevant algorithms.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
This paper presents a set of methods that uses a genetic algorithm for automatic test-data generation in
software testing. For several years researchers have proposed several methods for generating test data
which had different drawbacks. In this paper, we have presented various Genetic Algorithm (GA) based test
methods which will be having different parameters to automate the structural-oriented test data generation
on the basis of internal program structure. The factors discovered are used in evaluating the fitness
function of Genetic algorithm for selecting the best possible Test method. These methods take the test
populations as an input and then evaluate the test cases for that program. This integration will help in
improving the overall performance of genetic algorithm in search space exploration and exploitation fields
with better convergence rate.
Artificial Intelligence in Robot Path Planningiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
In recent years, consumers and legislation have been pushing companies to optimize their activities in such a way as to reduce negative environmental and social impacts more and more. In the other side, companies
must keep their total supply chain costs as low as possible to remain competitive.This work aims to develop a model to traveling salesman problem including environmental impacts and to identify, as far as possible, the contribution of genetic operator’s tuning and setting in the success and
efficiency of genetic algorithms for solving this problem with consideration of CO2 emission due to transport. This efficiency is calculated in terms of CPU time consumption and convergence of the solution. The best transportation policy is determined by finding a balance between financial and environmental
criteria.Empirically, we have demonstrated that the performance of the genetic algorithm undergo relevant
improvements during some combinations of parameters and operators which we present in our results part.
Survey on evolutionary computation tech techniques and its application in dif...ijitjournal
In computer science, 'evolutionary computation' is an algorithmic tool based on evolution. It implements
random variation, reproduction and selection by altering and moving data within a computer. It helps in
building, applying and studying algorithms based on the Darwinian principles of natural selection. In this
paper, studies about different evolutionary computation techniques used in some applications specifically
image processing, cloud computing and grid computing is carried out briefly. This work is an effort to help
researchers from different fields to have knowledge on the techniques of evolutionary computation
applicable in the above mentioned areas.
Optimizing Mobile Robot Path Planning and Navigation by Use of Differential E...IOSR Journals
Abstract: Path planning and navigation is essential for an autonomous robot which can move avoiding the
static obstacles in a real world and to reach the specific target. Optimizing path for the robot movement gives
the optimal distance from the source to the target and save precious time as well. With the development of
various evolutionary algorithms, the differential evolution is taking the pace in comparison to genetic algorithm.
Differential evolution has been deployed quite successfully for solving global optimization problem. Differential
evolution is a very simple yet powerful metaheuristics type problem solving method. In this paper we are
proposing a Differential Evolution based path navigation algorithm for mobile path navigation and analyze its
efficiency with other developed approaches. The proposed algorithm optimized the robot path and navigates the
robot to the proper target efficiently.
Prediction of Euro 50 Using Back Propagation Neural Network (BPNN) and Geneti...AI Publications
Modeling time series is often associated with the process forecasts certain characteristics in the next period. One of the methods forecasts that developed nowadays is using artificial neural network or more popularly known as a neural network. Use neural network in forecasts time series can be a good solution, but the problem is network architecture and the training method in the right direction. One of the choices that might be using a genetic algorithm. A genetic algorithm is a search algorithm stochastic resonance based on how it works by the mechanisms of natural selection and genetic variation that aims to find a solution to a problem. This algorithm can be used as teaching methods in train models are sent back propagation neural network. The application genetic algorithm and neural network for divination time series aim to get the weight optimum. From the training and testing on the data index share price euro 50 obtained by the RMSE testing 27.8744 and 39.2852 RMSE training. The weight or parameters that produced by has reached an optimum level in second-generation 1000 with the best fitness and the average 0.027771 the fitness of 0.0027847.Model is good to be used to give a prediction that is quite accurate information that is shown by the close target with the output.
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.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Performance Analysis of Genetic Algorithm as a Stochastic Optimization Tool in Engineering Design Problems
1. ISSN 2349-7815
International Journal of Recent Research in Electrical and Electronics Engineering (IJRREEE)
Vol. 2, Issue 4, pp: (71-76), Month: October - December 2015, Available at: www.paperpublications.org
Page | 71
Paper Publications
Performance Analysis of Genetic Algorithm as
a Stochastic Optimization Tool in Engineering
Design Problems
Raju Basak1
, Amarnath Sanyal2
, Arabinda Das3
, Avik Ghosh4
, Asmita Poddar5
1
Research Scholar, 113/3, Dakshindari Road, Dinesh apartment, Flat – A2/4, Sreebhumi,
Kolkata – 700 048; West Bengal, India
2
Professor, Calcutta Institute of Engineering and Management, 24/1A, Chandi Ghosh Road,; West Bengal, India,
Kolkata -700040;
3
Associate Professor, Electrical engineering department, Jadavpur University; Kolkata-700032; West Bengal., India
4,5
Assistant Professor, Elecreical Engineering Departent, Ideal Institute of Engineering, Kalyani, Nadia, West Bengal
Abstract: Engineering design problems are complex by nature because of their critical objective functions involving
many variables and Constraints. Engineers have to ensure the compatibility with the imposed specifications
keeping the manufacturing costs low. Moreover, the methodology may vary according to the design problem.
The main issue is to choose the proper tool for optimization. In the earlier days, a design problem was optimized
by some of the conventional optimization techniques like gradient Search, evolutionary optimization, random
search etc. These are known as classical methods.
The method is to be properly Chosen depending on the nature of the problem- an incorrect choice may sometimes
fail to give the optimal solution. So the methods are less robust.
Now-a-days soft-computing techniques are being widely used for optimizing a function. These are more robust.
Genetic algorithm is one such method. It is an effective tool in the realm of stochastic optimization (non-classical).
The algorithm produces many strings and generation to reach the optimal point.
The main objective of the paper is to optimize engineering design problems using Genetic Algorithm and to
analyze how the algorithm reaches the optima effectively and closely. We choose a mathematical expression for the
objective function in terms of the design variables and optimize the same under given constraints using GA.
Keywords: Engineering Optimization, Genetic Algorithm, Objective function, convergence, Engineering
Application.
I. INTRODUCTION
Optimization is the process of maximizing or minimizing a function consisting of number of variables under given
constraints. It means solving problems in which one seeks to minimize or maximize a real function by systematically
choosing the values of real or integer variables from within a set [1,2].
A real world problem may have many feasible solutions. Optimal design is the best possible design out of many feasible
designs, generally in presence of a number of inequality constraints.
Various tools are available to reach the optimal solution- classical and non-classical. The non-classical techniques based
on soft computing, have now become much popular.
The modern approach is to search and choose the best design method for specific tasks. Engineering optimization deals
with the optimal solution in all engineering fields.
2. ISSN 2349-7815
International Journal of Recent Research in Electrical and Electronics Engineering (IJRREEE)
Vol. 2, Issue 4, pp: (71-76), Month: October - December 2015, Available at: www.paperpublications.org
Page | 72
Paper Publications
Now use of Design Optimization is rapidly growing in almost all the engineering disciplines [2, 3], like mechanical, civil,
electrical, energy and off-shore engineering etc. This is due to the increase of manufacturer’s competition and the
development of strong and efficient techniques in order to achieve best product against minimum cost [4].
Engineering systems are represented by sophisticated numerical models. They involve several interacting disciplines that
must be considered simultaneously to obtain efficient designs [5].
Multidisciplinary optimization problem involve complex systems including subsystems. The main challenges are to
develop efficient numerical tools.
Some physical phenomena naturally describe an optimization problem, when the "equilibrium" is attained at the minimum
of an energy level [6].
II. GENETIC ALGORITHM
Genetic algorithm has been developed by John Holland, established by Holland and Dejong, and popularized by
Goldberg. Several researchers have contributed in many ways on various aspects and applications of Genetic algorithms.
GA has found its applications almost in every branch of engineering and still in an area of active research [7].
GA searches the natural genetics. Genetic algorithms (GA) are processed by three operators: reproduction, crossover and
mutation. GA creates an initial single population or species by randomly encoded chromosomes where each chromosome
representing a possible solution. Encoded chromosomes undergo natural selection for recombination through the
crossover operator whereby improved off-springs are generated in successive generations.
Roulette wheel selections for reproduction, single point crossover and probabilistic bit mutation are the basic mechanisms
suggested for the operations of the simple Genetic Algorithm. As GA is a stochastic search process a good solution
detected in early generations may not be selected for the latter generations (due to genetic drift). This has been referred to
as the generation gap. Generation gap may lead the GA to a non-optimal solution. Elitist GA has been suggested to
overcome this problem. In case of Elitist GA a solution having high fitness value is copied in the next population thus
ensuring the presence of the best solution detected in the final generations. Different Elitist schemes have been suggested.
Elitism has both advantages and disadvantages. By forcing the presence of some pre-selected solution strings in the next
population, we apply the so called selection pressure. Higher selection pressure reduces the variations in the population
which may lead to premature convergence of GA. Premature convergence occurs when the population strings become
identical before the optimum solution is detected [8].
Crossover is a method of exchanging information between two chromosomes. Most calculus based optimization methods
are based on exploitation (hill climbing) of the search space. Random search algorithms allow exploration of the search
space. GAs is robust as they find solutions by exploitation and exploration of the hyper planes of the search space. GA
exploits through the process of selection and explores through crossover and mutation.
As the mutation rate is increased mutation becomes more disruptive (explorative) until the exploitative effects of selection
are completely overwhelmed. A low mutation rate on the other hand, allows the algorithm to exploit a particular hyper
plane. Setting the mutation rate high allow the algorithm to explore different hyper planes. Crossover also helps
exploration. But, the amount of exploration through crossover is also dependent upon the selection. With increased
selection pressure, crossover can hardly bring any difference in the child population. Increased exploitation by selection
leads to decreased exploration by crossover.
A single allele mutation of an individual can also be thought of as a local search (exploitation) in an area surrounding that
individual in a multidimensional space. When the GA converges prematurely a higher mutation rate can be helpful.
Many real life problems, especially of engineering, are characterized by several goals. Each of these goals belongs to the
optimum of an objective function to be optimized. Most of the cases, the objective functions are in conflict. A common
approach to deal with this kind of problems is to amalgam multi objective functions into a single one by giving different
weighing factors to different objectives and then solving the weighted objective function. One can select the most
dominant feature as the objective function and other as constraints. The designer must give the priority to each objective
function according to their merits [7, 8].
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For a multi objective optimization, the problem is stated as given below:
Minimize
T
1 2( ), ( ),..................... ( )mf f x f x f x
--------- [1]
Subject to:
T T
1 2( ), g ( ),..................... ( ) 0, 0, .............0 ,ng g x x g x
--------- [2]
Where,
T
1 2, ,..................... kx x x x
--------- [2a]
In GA, although the binary coded algorithm can be better explained by biological heredities there exist some problems
such as, discretization and code conversion in solving continuous optimization. Various crossover and mutation
techniques have been developed for real coding of Genetic algorithm. One of the simplest crossover techniques is the
arithmetic crossover where the child chromosomes are produced as follows.
1 1 2
2 2 1
C = λP +(1-λ)P
C = λP +(1-λ)P ,
[3]
where 0 1 , P1, P2 are the parents and C1, C2 are the children.
Fig.1. Flow chart for genetic algorithm
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III. THE OPTIMIZING FUNCTION
Objective function given below has been taken arbitrarily. It may be taken as the mathematical expression for cost
function of some electrical equipment, having four variables with upper and lower bounds. The function is to be
optimized by Genetic Algorithm having four variables:x1, x2, x3 and x4.It has been found that the optimal values of the
variables are 1.6, 4, 10, 5 respectively and the minimum value of the objective function is 257.3199999999998 257.32.
The objective function is given as:
y = 100(x1
2
– x2) + (1 – x1)2
+x1+x3x4-x3/x4
Mathematical programming techniques or MATLAB software can also be used for finding out the optimal solution [9,
10]. Several authors have made use of GA or its improved for reaching the optimum solution for a design problem [11,
12].
IV. RESULTS
The results are given below in tabular form, which shows the convergence:
Table-1
Generation f(x) Generation f(x)
1 727.330 29 260.844
2 484.650 30 259.191
3 479.441 31 259.191
4 358.179 32 258.469
5 298.915 33 258.469
6 298.915 34 258.468
7 298.760 35 258.468
8 298.760 36 258.462
9 298.760 37 258.462
10 298.760 38 258.462
11 298.760 39 258.462
12 298.760 40 258.461
13 280.372 41 258.459
14 279.993 42 258.459
15 270.090 43 257.898
16 270.090 44 257.895
17 270.090 45 257.895
18 270.09 46 257.895
19 268.791 47 257.891
20 268.791 48 257.891
21 268.791 49 257.350
22 262.859 50 257.350
23 262.747 51 257.329
24 260.844 52 257.329
25 260.844 53 257.329
26 260.844 54 257.324
27 260.844 55 257.324
28 260.844 55 257.324
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The graphical plot of generation is given in fig. 2
Fig.2. Graphical plot of generation
V. CONCLUSION
The mathematical expression which has been optimized in this paper may be taken as the objective function- it may be the
cost function of some electrical equipment.
The main objective in this paper is focused on the Genetic Algorithm a non-classical stochastic tool for optimization- how
efficiently and effectively it can optimize when applied to an engineering design problem.
0 20 40 60 80 100
0
2
4
x10
4
Generation
Fitnessvalue
Best:257.3239Mean:257.3567
1 2 3 4
0
5
10
Numberofvariables(4)
Currentbestindividual
CurrentBestIndividual
20 40 60 80 100
0
10
20
30
Generation
AvergaeDistance
AverageDistanceBetweenIndividuals
257 257.2 257.4 257.6 257.8 258
0
2
4
6
Rawscores
Expectation
FitnessScaling
0 10 20 30 40 50
0
10
20
Generation
Individual
20 40 60 80 100
0
5
10
x10
4
Generation
Best,Worst,andMeanScores
257 257.2 257.4 257.6 257.8 258
0
10
20
ScoreHistogram
Score(range)
Numberofindividuals
0 5 10 15 20
0
100
200
300
FitnessofEachIndividual
0 5 10 15 20
0
5
10
SelectionFunction
Individual
Numberofchildren
Bestfitness
Meanfitness
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It may be observed from the table of convergence and the graphical plot that GA reaches the optimal solution very fast
and steady, creating so many generations and uses large search space.
Engineering Design problem requires global minima; otherwise optimization cannot give fruitful result on which the vale
of the objective function depends.
It can be very much effective for mass production. So, Genetic Algorithm may be chosen as one of the best tools for
optimizing an engineering design problem.
REFERENCES
[1] Kalyanmoy Deb, “Optimization for engineering design”, PHI Pvt.Ltd. 1998.
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[11] L. Hui, H. Li, H. Bei, and Y. Shunchang, “Application research based on improved genetic algorithm for optimum
design of power transformers,” in Proc. 5th Int. Conf. Electrical Machines and Systems, Vol. 1, pp. 242–245, 2001
[12] Li Hui, Han Li, He Bei, Y.Shunchang, “Application research based on Improved Genetic Algorithm for Optimum
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