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.
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.
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 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
AUTOMATIC GENERATION AND OPTIMIZATION OF TEST DATA USING HARMONY SEARCH ALGOR...csandit
Software testing is the primary phase, which is performed during software development and it is
carried by a sequence of instructions of test inputs followed by expected output. The Harmony
Search (HS) algorithm is based on the improvisation process of music. In comparison to other
algorithms, the HSA has gain popularity and superiority in the field of evolutionary
computation. When musicians compose the harmony through different possible combinations of
the music, at that time the pitches are stored in the harmony memory and the optimization can
be done by adjusting the input pitches and generate the perfect harmony. The test case
generation process is used to identify test cases with resources and also identifies critical
domain requirements. In this paper, the role of Harmony search meta-heuristic search
technique is analyzed in generating random test data and optimized those test data. Test data
are generated and optimized by applying in a case study i.e. a withdrawal task in Bank ATM
through Harmony search. It is observed that this algorithm generates suitable test cases as well
as test data and gives brief details about the Harmony search method. It is used for test data
generation and optimization
Improvement of genetic algorithm using artificial bee colonyjournalBEEI
Genetic algorithm (GA) is a part of evolutionary computing that simulates the theory of evolution and natural selection, where this technique depends on a heuristic random search. This algorithm reflects the operation of natural selection, where the fittest individuals are chosen for reproduction so that they produce offspring of the next generation. This paper proposes a method to improve GA using artificial bee colony (GABC). This proposed algorithm was applied to random number generation (RNG), and travelling salesman problem (TSP). The proposed method used to generate initial populations for GA rather than the random generation that used in traditional GA. The results of testing on RNG show that the proposed GABC was better than traditional GA in the mean iteration and the execution time. The results of testing TSP show the superiority of GABC on the traditional GA. The superiority of the GABC is clear in terms of the percentage of error rate, the average length route, and obtaining the shortest route. The programming language Python3 was used in programming the proposed methods.
Genetic Algorithm based Optimization of Machining ParametersAngshuman Pal
Optimization of a process output with reference to multiple input parameters is an important aspect of any manufacturing or machining process. This project examines and formulates a mechanism to optimize a given process output using the optimization technique of Genetic Algorithm. A new code for this purpose is formulated in MATLAB environment. The code is tested against some standardized functions and some case studies are performed to validate its performance against existing literature. The algorithm is then run over some real process performance data obtained from milling operation, and the optimum input parameters under given constraints required for achieving minimum surface roughness is proposed.
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.
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 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
AUTOMATIC GENERATION AND OPTIMIZATION OF TEST DATA USING HARMONY SEARCH ALGOR...csandit
Software testing is the primary phase, which is performed during software development and it is
carried by a sequence of instructions of test inputs followed by expected output. The Harmony
Search (HS) algorithm is based on the improvisation process of music. In comparison to other
algorithms, the HSA has gain popularity and superiority in the field of evolutionary
computation. When musicians compose the harmony through different possible combinations of
the music, at that time the pitches are stored in the harmony memory and the optimization can
be done by adjusting the input pitches and generate the perfect harmony. The test case
generation process is used to identify test cases with resources and also identifies critical
domain requirements. In this paper, the role of Harmony search meta-heuristic search
technique is analyzed in generating random test data and optimized those test data. Test data
are generated and optimized by applying in a case study i.e. a withdrawal task in Bank ATM
through Harmony search. It is observed that this algorithm generates suitable test cases as well
as test data and gives brief details about the Harmony search method. It is used for test data
generation and optimization
Improvement of genetic algorithm using artificial bee colonyjournalBEEI
Genetic algorithm (GA) is a part of evolutionary computing that simulates the theory of evolution and natural selection, where this technique depends on a heuristic random search. This algorithm reflects the operation of natural selection, where the fittest individuals are chosen for reproduction so that they produce offspring of the next generation. This paper proposes a method to improve GA using artificial bee colony (GABC). This proposed algorithm was applied to random number generation (RNG), and travelling salesman problem (TSP). The proposed method used to generate initial populations for GA rather than the random generation that used in traditional GA. The results of testing on RNG show that the proposed GABC was better than traditional GA in the mean iteration and the execution time. The results of testing TSP show the superiority of GABC on the traditional GA. The superiority of the GABC is clear in terms of the percentage of error rate, the average length route, and obtaining the shortest route. The programming language Python3 was used in programming the proposed methods.
Genetic Algorithm based Optimization of Machining ParametersAngshuman Pal
Optimization of a process output with reference to multiple input parameters is an important aspect of any manufacturing or machining process. This project examines and formulates a mechanism to optimize a given process output using the optimization technique of Genetic Algorithm. A new code for this purpose is formulated in MATLAB environment. The code is tested against some standardized functions and some case studies are performed to validate its performance against existing literature. The algorithm is then run over some real process performance data obtained from milling operation, and the optimum input parameters under given constraints required for achieving minimum surface roughness is proposed.
Comparison of Dynamic Scheduling Techniques in Flexible Manufacturing SystemIJERA Editor
Scheduling is an important tool in the manufacturing area since productivity is inherently linked to how well the resources are used to increase efficiency and reduce waste. The present article analyzes and provides comparison of modern techniques used for solving dynamic scheduling problem in flexible manufacturing system. These techniques are often impractical in dynamic real world environments where there are complex constraints and a variety of unexpected disruptions. This paper defines the modern techniques of dynamic scheduling and provides a literature survey of scheduling which are presented in recent few years. The principles of several dynamic scheduling techniques, namely dispatching rules, heuristics, genetic algorithms and artificial intelligence techniques are describe in details and comparison of their potential.
Application of Genetic Algorithm in Software Engineering: A ReviewIRJESJOURNAL
Abstract. The software engineering is comparatively new and regularly changing field. The big challenge of meeting strict project schedules with high quality software requires that the field of software engineering be automated to large extent and human resource intervention be minimized to optimum level. To achieve this goal the researcher have explored the potential of machine learning approaches as they are adaptable, have learning ability. In this paper, we take a look at how genetic algorithm (GA) can be used to build tool for software development and maintenance tasks.
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.
Pareto-Optimal Search-Based Software Engineering (POSBSE): A Literature SurveyAbdel Salam Sayyad
Paper presented at the 2nd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE’13), San Francisco, USA. May 2013.
Configuration Navigation Analysis Model for Regression Test Case Prioritizationijsrd.com
Regression testing has been receiving increasing attention nowadays. Numerous regression testing strategies have been proposed. Most of them take into account various metrics like cost as well as the ability to find faults quickly thereby saving overall testing time. In this paper, a new model called the Configuration Navigation Analysis Model is proposed which tries to consider all stakeholders and various testing aspects while prioritizing regression test cases.
Software Quality Assurance (SQA) teams play a critical role in the software development process to ensure the absence of software defects. It is not feasible to perform exhaustive SQA tasks (i.e., software testing and code review) on a large software product given the limited SQA resources that are available. Thus, the prioritization of SQA efforts is an essential step in all SQA efforts. Defect prediction models are used to prioritize risky software modules and understand the impact of software metrics on the defect-proneness of software modules. The predictions and insights that are derived from defect prediction models can help software teams allocate their limited SQA resources to the modules that are most likely to be defective and avoid common past pitfalls that are associated with the defective modules of the past. However, the predictions and insights that are derived from defect prediction models may be inaccurate and unreliable if practitioners do not control for the impact of experimental components (e.g., datasets, metrics, and classifiers) on defect prediction models, which could lead to erroneous decision-making in practice. In this thesis, we investigate the impact of experimental components on the performance and interpretation of defect prediction models. More specifically, we investigate the impact of the three often overlooked experimental components (i.e., issue report mislabelling, parameter optimization of classification techniques, and model validation techniques) have on defect prediction models. Through case studies of systems that span both proprietary and open-source domains, we demonstrate that (1) issue report mislabelling does not impact the precision of defect prediction models, suggesting that researchers can rely on the predictions of defect prediction models that were trained using noisy defect datasets; (2) automated parameter optimization for classification techniques substantially improve the performance and stability of defect prediction models, as well as they change their interpretation, suggesting that researchers should no longer shy from applying parameter optimization to their models; and (3) the out-of-sample bootstrap validation technique produces a good balance between bias and variance of performance estimates, suggesting that the single holdout and cross-validation families that are commonly-used nowadays should be avoided.
Evaluation of Process Capability Using Fuzzy Inference SystemIOSR Journals
In many industrial instances product quality depends on a multitude of dependent characteristics and as a consequence, attention on capability indices shifts from univariate domain to multivariate domain. In this research fuzzy inference system is used to determine the process capability index. Fuzzy sets can represent imprecise quantities as well as linguistic terms. Fuzzy inference system (FIS) is a method, based on the fuzzy theory, which maps the input values to the output values. The mapping mechanism is based on some set of rules, a list of if-then statements. In this research Mamdani fuzzy inference system is used to derive the overall output process capability when subjected to six crisp input and one output. This paper deals with a novel approach to evaluating process capability based on readily available information using fuzzy inference system.
Design and Implementation of a Multi-Agent System for the Job Shop Scheduling...CSCJournals
Job shop scheduling is one of the strongly NP-complete combinatorial optimization problems. Developing effective search methods is always an important and valuable work. Meta-heuristic methods such as genetic algorithms are widely applied to find optimal or near-optimal solutions for the job shop scheduling problem. Parallelizing genetic algorithms is one of the best approaches that can be used to enhance the performance of these algorithms. In this paper, we propose an agent-based parallel genetic algorithm for job shop scheduling problem. In our approach, initial population is created in an agent-based parallel way then an agent-based method is used to parallelize genetic algorithm. Experimental results showed that the proposed approach enhances the performance.
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.
Comparison of Dynamic Scheduling Techniques in Flexible Manufacturing SystemIJERA Editor
Scheduling is an important tool in the manufacturing area since productivity is inherently linked to how well the resources are used to increase efficiency and reduce waste. The present article analyzes and provides comparison of modern techniques used for solving dynamic scheduling problem in flexible manufacturing system. These techniques are often impractical in dynamic real world environments where there are complex constraints and a variety of unexpected disruptions. This paper defines the modern techniques of dynamic scheduling and provides a literature survey of scheduling which are presented in recent few years. The principles of several dynamic scheduling techniques, namely dispatching rules, heuristics, genetic algorithms and artificial intelligence techniques are describe in details and comparison of their potential.
Application of Genetic Algorithm in Software Engineering: A ReviewIRJESJOURNAL
Abstract. The software engineering is comparatively new and regularly changing field. The big challenge of meeting strict project schedules with high quality software requires that the field of software engineering be automated to large extent and human resource intervention be minimized to optimum level. To achieve this goal the researcher have explored the potential of machine learning approaches as they are adaptable, have learning ability. In this paper, we take a look at how genetic algorithm (GA) can be used to build tool for software development and maintenance tasks.
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.
Pareto-Optimal Search-Based Software Engineering (POSBSE): A Literature SurveyAbdel Salam Sayyad
Paper presented at the 2nd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE’13), San Francisco, USA. May 2013.
Configuration Navigation Analysis Model for Regression Test Case Prioritizationijsrd.com
Regression testing has been receiving increasing attention nowadays. Numerous regression testing strategies have been proposed. Most of them take into account various metrics like cost as well as the ability to find faults quickly thereby saving overall testing time. In this paper, a new model called the Configuration Navigation Analysis Model is proposed which tries to consider all stakeholders and various testing aspects while prioritizing regression test cases.
Software Quality Assurance (SQA) teams play a critical role in the software development process to ensure the absence of software defects. It is not feasible to perform exhaustive SQA tasks (i.e., software testing and code review) on a large software product given the limited SQA resources that are available. Thus, the prioritization of SQA efforts is an essential step in all SQA efforts. Defect prediction models are used to prioritize risky software modules and understand the impact of software metrics on the defect-proneness of software modules. The predictions and insights that are derived from defect prediction models can help software teams allocate their limited SQA resources to the modules that are most likely to be defective and avoid common past pitfalls that are associated with the defective modules of the past. However, the predictions and insights that are derived from defect prediction models may be inaccurate and unreliable if practitioners do not control for the impact of experimental components (e.g., datasets, metrics, and classifiers) on defect prediction models, which could lead to erroneous decision-making in practice. In this thesis, we investigate the impact of experimental components on the performance and interpretation of defect prediction models. More specifically, we investigate the impact of the three often overlooked experimental components (i.e., issue report mislabelling, parameter optimization of classification techniques, and model validation techniques) have on defect prediction models. Through case studies of systems that span both proprietary and open-source domains, we demonstrate that (1) issue report mislabelling does not impact the precision of defect prediction models, suggesting that researchers can rely on the predictions of defect prediction models that were trained using noisy defect datasets; (2) automated parameter optimization for classification techniques substantially improve the performance and stability of defect prediction models, as well as they change their interpretation, suggesting that researchers should no longer shy from applying parameter optimization to their models; and (3) the out-of-sample bootstrap validation technique produces a good balance between bias and variance of performance estimates, suggesting that the single holdout and cross-validation families that are commonly-used nowadays should be avoided.
Evaluation of Process Capability Using Fuzzy Inference SystemIOSR Journals
In many industrial instances product quality depends on a multitude of dependent characteristics and as a consequence, attention on capability indices shifts from univariate domain to multivariate domain. In this research fuzzy inference system is used to determine the process capability index. Fuzzy sets can represent imprecise quantities as well as linguistic terms. Fuzzy inference system (FIS) is a method, based on the fuzzy theory, which maps the input values to the output values. The mapping mechanism is based on some set of rules, a list of if-then statements. In this research Mamdani fuzzy inference system is used to derive the overall output process capability when subjected to six crisp input and one output. This paper deals with a novel approach to evaluating process capability based on readily available information using fuzzy inference system.
Design and Implementation of a Multi-Agent System for the Job Shop Scheduling...CSCJournals
Job shop scheduling is one of the strongly NP-complete combinatorial optimization problems. Developing effective search methods is always an important and valuable work. Meta-heuristic methods such as genetic algorithms are widely applied to find optimal or near-optimal solutions for the job shop scheduling problem. Parallelizing genetic algorithms is one of the best approaches that can be used to enhance the performance of these algorithms. In this paper, we propose an agent-based parallel genetic algorithm for job shop scheduling problem. In our approach, initial population is created in an agent-based parallel way then an agent-based method is used to parallelize genetic algorithm. Experimental results showed that the proposed approach enhances the performance.
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.
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.
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
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.
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.
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.
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.
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
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.
Performance Analysis of Genetic Algorithm as a Stochastic Optimization Tool i...paperpublications3
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.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
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Unsubscribed: Combat Subscription Fatigue With a Membership Mentality by Head...
I045046066
1. Md Amanullah et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 5( Version 4), May 2014, pp.60-66
www.ijera.com 60 | P a g e
Optimization of PID Parameter In Control System Tuning With
Multi-Objective Genetic Algorithm.
Md Amanullah*, Pratibha Tiwari (Assistant Professor )**
*Department of Electrical Engineering (Control & Instrumentation), SHIATS, Deemed to be University,
Allahabd-211007)
** (Department of Electrical Engineering, SHIATS , Deemed to be University, Allahabd-211007)
ABSTRACT
Way of playing advancement is the out-standing design of the study of PID control and frequently research
work has been guided for this aspiration. The Proportional plus Integral plus Derivative (PID), controllers are
most sweepingly used in control theory as well as industrial plants owing to their ease of execution and
sturdiness way of playing. The aspiration of this deed representation capable and apace tuning approach using
Genetic Algorithm (GA) to obtain the optimized criterion of the PID controller so as to acquire the essential
appearance designation of the technique below meditation. The make perfect achievement about multiple plants
have in relation to the established tuning approach, to consider the ability of intended approach. Mostly, the
whole system’s performance powerfully depends on the controller’s proficiency and thus the tuning technique
plays a key part in the system’s behavior.
Keywords - PID controller; Optimization; Genetic Algorithm; Tuning methods;
I. INTRODUCTION
Now a day’s world wide PID controller
sweepingly used for an optimum solution gives a
superior efficiency. For obtaining the better
efficiency the absolute output needed to match set
output. For this aspiration requirement of a controller.
PID controller is the widely used in the process
industry like petrochemical, paper, pulp, oil & gas, as
well as missile control systems, because of its easy
design and robust implementation in a broad range of
operational condition. Unluckily, it was completely
complex tune properly the gains of the PID
controllers because various industrial plants are
frequently loaded down with difficulties such as high
order, time delays, and nonlinearities. During the
long time various heuristic program procedures have
been proposed during the tuning of PID controllers.
The first technique put-upon the excellent
tuning rules intended by Ziegler and Nichols. In
general, it is frequently complex to assign optimal or
near optimal PID criterion by the Ziegler-Nichols
method in various industrial plants. During these
reasons, it is extremely beneficial to increase the
capabilities of PID controllers by adding new
features. That is also energetic for several of the
plants where oscillations and overshoot is generally
not In demand. This led Tyreus and Luyben to
recommended new famous conventional tuning
method for further conservative process loops. As for
Cohen–Coon recommended a new tuning method
which was based on a process reaction curve. In
Kitamori also recommended a new technique about
PID tuning. These conventional procedures are very
Famous amid control engineers because one and only
can use them, especially as no or small observation
about the plant under control is available. These
procedures provide stable, healthy and completely
great Achievers in spite of this the gains are not at all
assured of being optimal. Even, those conventional
tuning process frequently breaks down to accomplish
suitable Achievement in the case of plants having
nonlinearity, higher order or time delay. Thus,
intelligence techniques have been introduced by the
researchers according to established the tuning an
easier one. As for a latest scheme of PID tuning is
recommended based on the Fuzzy gain programming
approach. A neural networks tuned PID controller
with the help of fuzzy criteria is presented.As for this
paper presents a PID tuning approach founded with
respect to Multi-objective Genetic Algorithm
(MOGA) and his performance is matched by
conventional techniques of tuning. The MOGA tuned
PID (MOGA-PID) controller is well-tried on several
sophisticated techniques frequently based on control
system literatures. Hence, the next chapters explain
by the sophisticated control techniques used in this
paper for trial the performance about Multi-Objective
Genetic Algorithm (MOGA) PID controller.
II. Investigation of the Plant Problems
To explain the influence about the presented
technique three systems are considered. G1(s) is a
certain time delay second order system, G2(s) is a
third order system and G3(s) is a fourth order system.
As for process control, these systems are almost
generally observed, represented as:
RESEARCH ARTICLE OPEN ACCESS
2. Md Amanullah et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 5( Version 4), May 2014, pp.60-66
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Equation (1) is a second order system
(2)
Equation (2) is a third order system
Equation (3) is a fourth order system
Due to the time delay definition with respect to
process , a second order pade approximation.
The pade approximation is authentic only at low
frequencies, and accommodates better frequency-
domain approximation than a time domain
approximation.
III. About Genetic Algorithm:-
John Holland was the father of genetic
algorithm who discovered in early 1970. Genetic
algorithms (Gas) are adaptive heuristic search
algorithm based on the evolutionary ideas of natural
expansion. As such the denote and intellectual
victimization of a random search used to optimization
problems. Although randomized, GAs are absolutely
not in random, instead the deed of historical
knowledge to direct the search into the reason of
excellent performance in the search space. The key
technique of the GAs is designed to fabricate
processes in general system mandatory for evolution,
mainly those follow on the ethics first laid down by
Charles Darwin of “survival of the fittest.” GA has
been studied as a satisfactory and effective method
for finding out difficult optimization problems. By
exactly avoiding local minima, it converges to global
minima. It originates from an initial population
obtaining a number of chromosomes where for each
one correlate to a result of the given problem. Then
the achievements of each original are calculated by
using a correct fitness function. Essentially, GA
consists of five significant steps: initial population,
fitness function, Selection, Crossover and Mutation.
These are also known as GA drivers. The application
of five basic operators confesses the formation of
new children, which may be located excellent than
their parents. This algorithm is repeated for several
generations and finally stops when meeting product
that is denoted the optimum result of the problem.
(a) Initial population:-
Its starts with Randomly Originated states,
these states are satisfactory to the problem. The
population size of its create on the nature of the
problem, even so typically consist of various
hundreds or thousands of feasible solutions.
Commonly, the population is originated randomly;
admit the perfect range of possible solutions.
Infrequently, the solutions can be "sown" in the
range, where optimal solutions are possible to be
established.
(b) Fitness function:-
A fitness function is a certain type
of objective function that is familiar with summarize,
because a single figure of merit, however close to a
given design solution is to complete the set of goals.
In the range of genetic programming and genetic
algorithms, a single design solution is denoted by a
string of numbers (specified as a chromosome). After
every overall testing or simulation, the concept is to
remove the 'n' worst design solutions, and to create 'n'
new ones as from the best design solutions. Each and
every design solution have to be rewarded a figure of
merit, to illustrate how close it comes to meet the
overall requirement, and it is developed by put into
use the fitness function to the test or simulation,
solutions are obtained from that solution.
Two main parts of fitness functions survive:
the one where the fitness function does not change
while optimizing a fixed function or testing with a
fixed set of test cases and another one where the
fitness function is changeable, while niche
separation or develop the set of test cases.
(c) Selection:-
Two pairs are selected at random to
reproduce. They are selected based on their fitness
function score. One may be selected more than one,
whereas one may not be selected at all. Make a copy
the selected programs to the new population. The
regeneration process may be subdivided into two
parts, first is Fitness Evaluation and second is
Selection. The fitness function is how is operated the
evolutionary process and its view is to identify how
well a string (particular) solves the problem,
admitting as an evaluation of the respective
performance of each population member. Basically
four most common methods of the selection:
1. Tournament Selection
2. Normalized geometric selection
3. Roulette Wheel selection
4. Stochastic Universal is sampling
(d) Crossover:-
For each pair to be mated, a crossover point
is preferred at random from within the bit string. The
offspring is developed by interchange between the
parents at the crossover point. Population is different
early in the process, these reasons the crossover to be
large in the beginning. However, it will settle down
in future generations. Hence, There are several types
of crossover operators like single point crossover,
two point crossover, arithmetic crossover etc.
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(e) Mutation:-
Mutation is a genetic operator familiar with a cure
for genetic difference from one generation of a
population about genetic algorithm chromosomes to
the next. It is related to biological mutation. Mutation
change more than one gene values in a chromosome
from its initial state. In mutation, the solution may
completely change from the last solution. Hence, GA
may come to a better solution by using mutation.
Mutation occurs during evolution by a user-definable
probability. This has probably been set low. If it is set
too high, the search will become an original random
search.
Genetic Algorithm performs the following steps:
1. Originate an initial population, randomly or
heuristically.
2. Calculate and save the fitness for each particular
in the current population.
3. Specify the selection probability at single that it
is reciprocal to its fitness
4. Originate the next current population by most
probabilities selecting the individuals from the
earliest current population, in order to goods
product of genetic operators.
5. Repeat step 2 until a sufficient result is obtained.
A flow chart of the general scheme of the
implementation of the GA is shown in Figure 1.
Parent selection
Initialization
Termination
Survivor selection
Figure 1:- flow chart of the general scheme of the
implementation of the Genetic Algorithm.
IV. Fundamental of PID control Action:-
The PID controller has been brodly used
since it invented in 1910. The combination of
proportional control action, integral control action
and Derivative control Action is Termed proportional
plus Integral plus derivative control Action. It
improves both the transient and steady state response
characteristics. It is similar to lead lag compensator
or band reject filter, it reduces the rise time. It
increases bandwidth and also increases stability of
the system. The peak overshoot depends on properly
tuned values of Ti and Td. It eliminates the steady
state error between input and output. It increases the
TYPE and ORDER by the system is One. The
transfer function of a PID controller as given below.
(4)
Where, = proportional gain, Integral gain,
= Derivative gain.
We can specify another Equivalent Form of the PID
control has the following form.
(5)
Where, = and Ti and are called as
Integral and Derivative time constants, respectively.
The basic block diagram plant controlled by PID
controller is shown in given below.
Figure 2:- Block diagram of the simplest closed loop
system
Where, = set point, = error signal, =
controller output, = plant output.
Typical structure:-
The typical operation of the PID control is shown in
Figure 3. The signal error, , enters the PID
control block and the resulting action signal is the
sum of the error signal modified by the proportional,
integral and derivative actions.
Figure 3:- Block Diagram of Typical PID control
Scheme.
is called as error signal and
Population
Recombination
Mutation
Mutation
Parents
Children
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is the controller output for this specific error
signal.
(6)
Also, the equation (6) can be rewritten in Laplace
form as:
= /s+ ) ( 7)
Finally, under the above strategy the transfer function
of the PID controller or the control law is established
by
= = (8)
Tuning of PID by MOGA:-
The fitness criterion is a mathematical performance
of the problem’s high level demand. That is, our
fitness criterion tries to optimize for the integral of
the square error (ISE) for these step input and also to
optimize for maximum awareness. The fitness
function is a more significant consideration while
tuning the PID controller by MOGA. Several multi-
objective fitness functions are considered in this
study represented as follows:
(9)
(10)
(11)
(12)
(13)
Where, is the settling time within 5 percent, is
rise time, OS is percentage overshoot and ISE is
Integral of the square error which can be defined as
follows:
Commonly, the PID controller scheme
method using the integrated absolute error (IAE),
integral of squared-error (ISE), integrated of time-
weighted-squared-error (ITSE) is frequently occupied
with control system design on account of it can be
classified experimentally in the frequency domain.
(14)
(15)
(16)
It is the preferred of the control engineer so
that which individual criterion of the control system
requirement additional consideration. So as per the
demand a higher weight can be allowed, although
given the other requirement specification at the same
time. Yet, the total sum of the weights in an objective
function must be equal to one, in order to the total
performance of the system may be confirmed. It
shows the resilience in PID tuning while applying
MOGA.
Optimization function
Control input
Control Output
Figure 4:- MOGA based PID Tuning Scheme.
No
Yes
Figure 5:- Flow chart of MOGA-PID Controller.
MOGA
PID Controller
Selection
Converged
Best PID Values
Crossover
Mutation
Next
Generation
Stop
Simulate the Process &
Evaluate functions
Check fitness
Generate Initial Population for
Start
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Eliminate the negative error components by the help
of . In this simulation, the
objective is to decrease the cost function. During that
motive the objective function is preferred for the
Integral Square Error (ISE). The ISE squares the
error to eliminate negative error components. Multi
Objective Genetic Algorithm (MOGA) PID
Controller performance depends on the convergence
rate.the criteria like population type, population size,
creation, function, selection and much more are also
affecting the convergence rate. Hence, its order to
decline precocious convergence of Genetic
Algorithm, so this extremely strategies to select the
suitable operators and criteria for it. Table I display
the criteria and operators of GA that are accomplish
by accurate experimental research of this work.
GA Parameter Value/Method
Population type Double Vector
Population size 20
Creation function Feasible population
Selection Tournament
Mutation Adaptive feasible
Crossover Arithmetic crossover
Generation 65
Accepting above two stopping criterion have
been used in this paper, the first one is the number of
generations and the second one is function tolerance
which is .
V. Simulation and Result
In order to that the authenticate ability of the
optimization strategy, a simulation criterion test is
implemented in MATLAB Simulink software. The
results are MOGA based PID tuning and Compared
with Z-N, Kitamori, T-L have been well-tried on a
different types of plants which are generally initiated
in the process control system. Entire these
simulations are performed on a laptop having an
Intel® Core (TM) 2Duo CPU processor operating at
2.20GHZ, 32 bit RAM and Installed with
MATLAB® 7.10.0 (R2010a). Figure 6,7,8 shows the
step response of the process
respectively controlled by several conventional and
MOGA- PID controller.
Figure 6: - shows the Step response of the process
(S)
Figure 7:- shows the Step response of the process
(S)
Figure 8:- shows the Step response of the process
(S)
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Table 2:-Overview of Relative Study
Table 2: Method for tuning applications. Denoted the
overall performance study of the MOGA-PID
controller and several conventional PID controllers.
In this relative study of the results achieved by
Kitamori’s and Ziegler-Nichols PID controller. The
parameters of Tyreus and Luyben PID controller
have been determined by = /2.1 =2.2 and
= /6.2 where is the ultimate gain and is the
ultimate period of sustained oscillation. While tuning
of PID by MOGA, The fitness function is
considered. THE behavior of MOGA-PID controller
is better than the conventionally tuned PID controller.
Fig. 9 shows the correlation of step response of the
system controlled with MOGA-PID controller
in which various fitness functions have been
exploited for the objective of tuning.
Figure 9:-MOGA-PID step response for several
Objective function
Table 3:-Comparison of MOGA-PID for several
objective function
Hence, the exact surveillance of the table 3 fair
denoted by the MOGA-PID controller gives the
information to the control engineer tuning in
demanding needs. Here, suitable weight is appointed
to a particular specification in this case target is
degrade the overshoot it is clear from the Table 3.
Proce
ss
Index Kitamori Z-N T-L MOGA
-PID
2.211 2.80
7
2.1
327
2.1245
2.039 1.65 7.1
95
2.3328
0.518 0.41 0.5
18
0.5556
6.7 32 0 0
2.38 4.17 16.
04
1.58
2.211 2.17 1.6
58
2.3504
2.039 1.04 4.5
31
2.6757
0.518 0.25
7
0.3
269
0.4470
6.7 18 0 0
2.38 5.46 12.
01
4.22
2.356 3.07
1
2.3
271
1.9826
1.648 1.35
2
5.9
489
1.8418
0.415 0.33
9
0.4
291
0.4532
11 32.9 0
2.2 3.72
1
13.
08
1.51
Process Index
2.3347 2.216
8
1.6
374
2.10
67
1.1160 1.353
8
0.7
779
0.89
31
1.4531 1.687
1
0.7
454
1.87
81
%OS
5.22 5.25 0 0.42
74
1.31 1.21 2.2
3
3.31
0.67 0.61 1.3
9
0.58
Objective
function 0.8157 1.051
5
0.3
211
1.13
91
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VI. Conclusion
The Designed with MOGA-PID controller has much
faster response than the conventional tuned PID
controller.Result showing that the achievement of
MOGA based PID controller is much better.it is a
effective tuning strategy for PID controllers. The
great utility of using MOGA is that it is absolutely
autonomous from the complicated nature of the
objective function under consideration. The MOGA
designed PID is much better in terms of the rise time
and settling time than the Z-N Technique.in this
manner when tuning a PID controller, the controlling
process can be considered several objectives slightly
cramped to the single objective for the conventional
tuning methods.
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