Top PDF Software Assessment Parameter Optimization using Genetic Algorithm

Software Assessment Parameter Optimization using Genetic Algorithm

Software Assessment Parameter Optimization using Genetic Algorithm

Software Effort Estimation as Collective Accomplishment is proposed by Kristin Borte et al. [4] their work paper examines how a team of software professionals goes about estimating the effort of a software project using a judgment-based, bottom-up estimation approach. The findings of their work show how software effort estimation is carried out through complex series of explorative and sense-making actions, rather than by applying assumed information or routines. Finally the paper argues that to grasp the complexity of software estimation, there is a need for more research that accounts for the communicative and interactional dimensions of this activity.
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Fuzzy Controller Parameter Optimization Using Genetic Algorithm for aReal Time Controlled System

Fuzzy Controller Parameter Optimization Using Genetic Algorithm for aReal Time Controlled System

Several studies have shown FC to be an appropriate method for the control of complex or partially identified processes, many of which cannot easily be modeled in a mathematical way. Unlike a conventional controller, no rigorous mathematical model is required to design a FC and in many cases, they can be implemented easily. However, this simplicity also presents a bottleneck in their design. FC relies on heuristic knowledge that is subject to designer’s interpretation and choice. The traditional approach to fuzzy design is laborious, time consuming and in most cases specific to each application. Optimal search algorithms such as GA and ant colony optimization could solve some of these problems . Even though the choices of membership function are subjective, there are some rules for membership function
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Review of models Analysis & Techniques of Software Component and Optimization with Genetic Algorithm

Review of models Analysis & Techniques of Software Component and Optimization with Genetic Algorithm

One of the retrieval techniques is browsing, that has a requirement of well structured collection of document. The well structured document is represented as interconnected nodes network. The client can browse the required information through this network. The main advantage of this technique is when the user unable to decide the exact thing he is searching for. By using this technique, the user can go through the nodes consisting network which can help user for finding the exact match. This kind of matching doesn’t have any formal rules.
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SOFTWARE TESTING USING GENETIC ALGORITHM

SOFTWARE TESTING USING GENETIC ALGORITHM

Software testing is a phase of software development life cycle, which is used to discover the errors and to check whether the designed software fulfills the user requirement or not .Better testing leads to better software quality and reliability. There are various types of software testing which can be used to test a software. The main issue with the software testing is completeness. It is to possible to test the software completely as it involves the exhaustive number of test cases to be put under test which in turn increases the total effort and cost of the software. Different techniques and methodologies have been proposed for taking care of these issues. Use of evolutionary algorithms for automatic test generation has been an area of interest. Genetic Algorithm is one such optimization technique. The aim of the research paper is to implement Genetic Algorithm for minimization of test cases and to reduce cost , time and effort in order to deliver the good quality software.
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Optimal Design and Benefit/Cost Analysis of Reservoir Dams by Genetic Algorithms Case Study: Sonateh Dam, Kordistan Province, Iran

Optimal Design and Benefit/Cost Analysis of Reservoir Dams by Genetic Algorithms Case Study: Sonateh Dam, Kordistan Province, Iran

In a study, Yanmaz and Gunindi [8] investigated the relationship between reservoir capacity and the weight of concrete dams, RCC dams and their benefit and cost. Xie and Qian [9] employed the grey fuzzy extensive estimation technique to choose a NWL to determine quantity of qualitative indicators using the fuzzy number and the relationship of indicators being taken into account. Hou [10] displayed the application of the multi-principle assessment technique in the selection of NWL, using a real hydropower plant as an example. A four-step method for optimizing the NWL of reservoirs on the basis of a mathematical programming model involving various parameters that affect the economic viability, engineering characteristics, environmental and urban ecology was proposed by Shijun et al. [11]. Shafiei et al. [12] showed that GAs supply strong and satisfactory solutions to the levees encroachment optimization problem. Their results indicate that GA is suitable to levees optimization problems. Marcos et al. [13], presented a process to optimize the construction of mass concrete structures using GAs and their results demonstrated that the process can be employed in the design of enormous concrete structures. A comprehensive overview on the cost optimization of concrete structures given by Sarma and Adeli [14], demonstrated that most papers face up to structural optimization interested of weight minimization. A few papers on cost optimization mainly were devoted to simple elements like beams and girders, and extremely few employed costs functions that took into account the costs of placement and construction. Sharif and Wardlaw [15] developed a GA approach for the optimization of multi-reservoir systems. Salmasi [16] determined the optimal top width of gravity dam by a GA in which the top width is taken as a function of water depth. In a study, Varaei and Ahmadi Nadooshan [17] optimized the dimensions of concrete gravity dams using GA and particle swarm algorithm and then compared and evaluated the accuracy and speed of access to response. They mentioned the large impact of amounts of particle swarm algorithm in convergence. Bozorg Haddad et al. [18] addressed a possible decrease in flood damages using structural methods and determined proper locations (layout) to construct protective levees and height of levees (design of levees) in high-risk areas using GAs. Zhao Wanli et al. [19] focused on the problem of optimization method for selection of NWL of hydropower station reservoir. The results demonstrated that the method is simple and can decrease computational attempt of extensive evaluation,
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Test Case Optimization for Enhancing System Software Quality using Genetic Algorithm

Test Case Optimization for Enhancing System Software Quality using Genetic Algorithm

M.Bharathi is a Research Scholar in the Department of Computer Science, Periyar University, Salem, Tamilnadu, India. She had completed B.Sc(Computer Science) at Periyar University in the year of 1999. She had completed MCA during the year of 2005 and M. Phil (Computer Science) degree in 2008 and she has passed Tamilnadu State Eligibility Test (TNSET) 2012 of Bharathiyar University, Tamilnadu. Her research interests are software engineering and software testing. She has specialized in the area of system software, Data mining, Digital image processing and data Science. She has fourteen years teaching experience in the field of computer science. She has published several articles in peer reviewed and reputed journals. She has attended and presented several papers in various conferences organized by well esteemed institutions.
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Test Case Generation for UML Behavioral Diagram by Traversal Algorithm

Test Case Generation for UML Behavioral Diagram by Traversal Algorithm

V. CONCLUSION AND FUTURE WORK Software testing is necessary to validate the requirements and to maintain software quality. The comparison of DFS and BFS algorithm to produce automatic test cases for AD and SD has been presented in this paper. The suitable test cases are produced from both algorithms. The test cases which are produced according to AD, SD, and STG graphs are given. The activity diagram based coverage criterion is considered for generation of test cases. But the test cases cannot display the whole process like lack of information in filling of application during the testing. So it becomes onerous to process further because of not using of standards words in the diagram. The test cases which are produced from SD only screen the system section. But the produced test cases display the whole information of business process inside a system. The test cases produced from the integrated graph shows the features of AD and SD. But consist of unnecessary information. The optimization of test cases by using genetic algorithm will be combined with the current work.
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Genetic algorithm for the cargo shunting cooperation between two hub-and-spoke logistics networks

Genetic algorithm for the cargo shunting cooperation between two hub-and-spoke logistics networks

Research by Huang and Wang (2009) showed in order to reduce the risk of uncertainty in the process of network optimization, the optimal robust solution of hub-spoke network is obtained under the condition of multiple possibilities of demand and cost, and an optimization method based on multi-objective optimization genetic algorithm is proposed. Research by Gen, Lin, Yun and Inoue (2018) showed the latest progress of genetic algorithm based on hybrid priority in solving multilevel logistics or supply chain management network problems. Introduced: (1) sugar cane supply chain management network model, (2) multi-objective supply chain network model, (3) flexible multistage logistics network model, (4) multi-objective reverse logistics network model. Research by Zhang, Deng, Chan and Zhang (2013) showed a central coordination system framework with multi-criteria genetic optimization. In the previous multi-criteria optimization genetic algorithm (MCOGA), the analytic hierarchy process was used to evaluate the fitness value. And an improved MCOGA algorithm based on order preference technique similar to ideal solution is proposed. Research by Young-Bin Woo (2019) a hydrogen supply chain network problem with two modes of transport and supply cycles. Mixed integer linear programming is introduced in the process of genetic algorithm, and a mathematical model based on genetic algorithm is proposed. Research by Delavar, Hajiaghaei-Keshteli and Molla-Alizadeh-Zavardehi (2010) showed a major issue in supply chain management is the coordination of production and distribution decisions. To achieve effective logistics scheduling, the key is to integrate these two functions and coordinate planning. Taguchi experimental design method is applied to set and estimate the proper values of genetic algorithm parameters to improve the performance.
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Optimization of Agricultural BMPs Using a Parallel Computing Based Multi-Objective Optimization Algorithm

Optimization of Agricultural BMPs Using a Parallel Computing Based Multi-Objective Optimization Algorithm

Beneficial Management Practices (BMPs) are important measures for reducing agricultural non-point source (NPS) pollution. However, selection of BMPs for placement in a watershed requires optimizing available resources to maximize possible water quality benefits. Due to its iterative nature, the optimization typically takes a long time to achieve the BMP trade- off results which is not desirable in practice. In this study, an optimization model, consisting of a multi-objective genetic algorithm, ε-NSGA-II, in combination with the Soil Water and Assessment Tool (SWAT) and the parallel computation technique, is developed and tested in the Fairchild Creek watershed in southern Ontario of Canada. The two objectives are to minimize BMPs costs and maximize total phosphorous load reduction. The parallel computation allows the run of multiple SWAT models simultaneously and can reduce the ε-NSGA-II optimization time significantly to achieve the objective. The Pareto-optimal fronts generated between the two objective functions can be used to achieve desired water quality goals with minimum BMP implementation cost to support spatial watershed management and policy making.
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Optimization of Fuzzy Controller Parameter by Using A Firefly Algorithm

Optimization of Fuzzy Controller Parameter by Using A Firefly Algorithm

In this paper, fuzzy controller (Mamdani type) to control of liquid level and temperature in tanks are used. We present optimization of Mamdani-type fuzzy regulator parameters using FA. MATLAB software is used for designing and simulating. This paper is organized as follows. In Section2, system model is presented. In Section3, the controller design based on Mamdani system is given. In Section4, Simulink model is presented. In Section5,FA is described. Finally, the results, conclusion and future direction are illustrated.

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Optimal Sizing Of Transistor's Parameter Using Genetic Algorithm

Optimal Sizing Of Transistor's Parameter Using Genetic Algorithm

Next, encoding technique for chromosome will be the main consideration. Binary encryption is produce precise data, decoding will be easy and process of genetic evolution more reliable. Disadvantage of binary encryption is data too precise and lead to large mutation rate. There are three more encryption techniques such as floating point encryption, gray code encryption and symbol encryption. Different data set or application will use different encryption method and it highly influences optimization result. Choosing these techniques to encode data in chromosome will be a huge problem to create GA optimization system. Reduction of selection error in GA analysis is the second priority problem. The accuracy of GA in selecting proper data set to create best chromosome need to be investigated. When selection processes occur in GA, different techniques applied to avoid certain bad condition and guide GA select better individual to perform evolution. These are the problem faced when the genetic algorithm is developed. Accuracy of output and precision of GA in analyzing data to produce output will be focus in the developed algorithm as well.
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Optimization Procedure by Using Genetic Algorithm

Optimization Procedure by Using Genetic Algorithm

belong to class of productive systems in which the main characteristic is the simultaneous execution of several processes and sharing a finite set of resource. Nowadays, the FMS must attend the demand of the market needs for personalized products. Consequently the product life cycle tends to be shorter and a greater variety of products must be produced in a simultaneous manner. In this paper, we present a Genetic Algorithm based scheduling of Flexible manufacturing system. This work is considering multiple objectives, i.e., minimizing the idle time of the machine and minimizing the total penalty cost for not meeting the deadline concurrently. Software is developed for getting optimum sequence of operation. FMS considered in this work has 16 CNC Machine tools for processing 43 varieties of products. In this paper, various meta-heuristic methods are used for solving same scheduling problems taken from the literature. The results available for the various existing meta-heuristic methods are compared with results obtained by GA. After 1700 generations of GA the global optimum schedule is obtained.
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Code Optimization using Genetic Algorithm

Code Optimization using Genetic Algorithm

In the area of optimization, researchers are always looking to find the best/optimized solution. In this paper, we mainly focus on how the genetic algorithm helps optimize a solution. Compiler optimization means making the output of a compiler increase or decrease the impact of certain parameters of a computer program. The main aim of optimization is to find out the efficient and best value for all the given attributes in order to gain credible performance. Performance is measured based on parameters such as the time taken to execute a code or the size of the code (Kilo lines of code) etc. which also has an impact on the execution of the whole application. Most of the work done in the area of optimization has found out that evolutionary algorithms (EA) are most efficient when it comes to optimization. EA’s consider a group of the population and then carries out observations in the behavior and characteristics of the population. Many of the evolutionary algorithms mainly focussed on reducing the search time and not on the performance which was a major drawback as performance is a crucial factor. Hence genetic algorithms came into play which reduced the above-mentioned flaw by providing an optimal solution with reduced search time and high performance from a huge set of solutions Genetic algorithm comes under the concept of evolutionary computation and works most efficiently for carrying out global optimization. They are EA’s which are mainly search based and they replicate the process which naturally occurs and then performs the selection. Genetic Algorithm has 4 crucial steps or factors which are
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A Comprehensive Survey on Variants in Artificial          Bee Colony (ABC)

A Comprehensive Survey on Variants in Artificial Bee Colony (ABC)

Ahmet Ozkis et al.(2013) proposed Accelerated ABC (A- ABC) Algorithm for Continuous Optimization Problems. In A-ABC, two modifications are used on the Artificial Bee Colony (ABC) algorithm to progress its local search ability and convergence speed. Sandeep Kumar et al. (2013) introduced Crossover based ABC (CbABC) which integrates crossover operation from Genetic Algorithm (GA) with original ABC algorithm. The CbABC strengthens the exploitation phase of ABC as crossover enhances exploration of search space. Xiangyu Yu et al.(2013) introduced a sensor deployment algorithm based on modified ABC algorithm called FNF, (forgetting and neighbor factor)-BL (backward learning) ABC algorithm is proposed. In order to have a better coverage and a faster convergence speed, the onlooker bee phase and the scout bee phase of the ABC algorithm have been modified. Takeshi Nishida (2013) modified the ABC algorithm for adaptation to time-varying functions. To adjust to the change in the function, a procedure for re-evaluating the bees at each time is introduced. Dharmender Kumar et al. (2013) proposed a variant of ABC algorithm based on rectangular topology structure, namely rectangular topology based Artificial Bee Colony algorithm (RABC). RABC significantly improves the original ABC in solving high dimensional optimization problems. Alina Rakhi Ajayan et al. (2013) proposed an improved ABC algorithm to match the different characteristics of wireless sensor network deployment process, which will be optimum for real time dynamic network functioning. Muhammad shahrizan shahrudin et al (2013) presented a modified ABC algorithm to fine optimum value for optimum functions. The Hybrid ABC algorithm are applied searching mechanisms and probability functions. Ivona Brajevic et al. (2013) introduced an upgraded artificial bee colony (UABC) algorithm for constrained optimization problems. UABC algorithm enhances fine-tuning characteristics of the modification rate parameter and employs modified scout bee phase of the ABC algorithm.
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Optimization Electrical Circuit's Parameter Using Genetic Algorithms (GAs)

Optimization Electrical Circuit's Parameter Using Genetic Algorithms (GAs)

Hence, the genetic algorithm is proposed to define appropriate parameters values to meet the desired circuit performance with its ability of parallel searching through the entire solution space. In GAs implementation, the estimation of the feasible region plays an important role in the determination of the solution space volume to be searched. A search over the feasible region estimate will greatly decrease the time needed to cover all possible solutions of a problem using GAs. The proposed GAs is realized by computer program, which can provide a simplified design procedure. Furthermore, the advantage of GAs program is the capable to find a set of optimal solution for the complex and complicated electrical circuit instead of reduce the conventional design time of the circuit.
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A Study of Various Metaheuristic Techniques used for Software Testing

A Study of Various Metaheuristic Techniques used for Software Testing

Abstract: In today’s competitive world every software company wants to deliver high quality software. So software testing is essential task as it will locate errors and ensure error free software. Basically software testing is a process of validating software with requirements and testing for bugs however it is a labor intensive and very costly task. So automation of testing is needed as exhaustive testing is not possible. A properly generated test suite has a strong impact on the efficiency and effectiveness of software testing. In recent years, metaheuristic techniques are the focus of researchers. This paper enlightens on different metaheuristic techniques that are used for optimizing test suite. A brief description of genetic algorithm, particle swarm optimization, ant colony algorithm, artificial bee colony algorithm, algorithm is given along with its pseudo code to facilitate the implementation of these algorithms. This study will be beneficial for both practitioners and researchers.
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Clustering and parameter optimization in 
		MANETS using SOM and Genetic Algorithm

Clustering and parameter optimization in MANETS using SOM and Genetic Algorithm

Mobile Ad Hoc Networks or MANETS are made up of self-governing nodes which are interconnected with each other without any intermediate device or interface. Ubiquitous, Ever-changing, nomadic and Ad hoc nature are the main adjectives which may be used for them. They are easy to set up, without any help of the intermediary infrastructure [1]. MANETS are also called as the multi- hoping networks, but they may work with single hop count too. These networks usually show unstable route maintenance due to continuous changing of positions as shown in Figure-1. Due to which QoS get affected. As we can see the Figure-1(A) below in which node A changes its initial position, and move to create new route path for connecting with other nodes. Frequent movement of mobile nodes creates these sorts of problems which need resource optimisation. The capability of resource optimization for different types of data and its applications force us to use unsupervised learning Algorithm SOM or Self Organizing Maps and GA or Genetic Algorithm [1] to enhance the performance of these networks, which supports the step-by-step process to establish connections in between them. Important characteristics of any performance network are convergence, converged networks and QoS which incorporates different types of data like voice, video, and text data on a single transmission media. Due to their unstable topology and multi-variable dependency makes them more complex. A number of researches have been done in order to improve their performance using congestion control, QOS enhancement, Mobility Management, location management and Security using ML, AI and CI as discussed in [2] & [3]. In the research work [2] & [4] different learning mechanism of Supervised and unsupervised learning mechanisms are there in which learning can be observed. In these works unsupervised learning may be used to cluster a multi-dimensional data
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Optimal parameters of the SVM for temperature prediction

Optimal parameters of the SVM for temperature prediction

The principle of the support vector machine (SVM) method, which is an available method for small-scale samples, is structural minimization rather than empirical risk minimization (Gao et al., 2013). Currently, scholars use the SVM method to solve a variety of support vector prediction problems, including short-term power load forecasting, human resource management forecasts, and predicting mineral resources. Its application to predict the temperature is still relatively small, and domestic, supporting vector regression in real-time forecasting applications (Feng et al., 2005). In comparison to the SVM method, temperature prediction derived from the traditional support vector method was more accurate. However, parameter selection was a procrastination- inspiring and time-consuming process because it cannot be screened automatically. The input variables also cannot be selected effectively. Therefore, kernel function selection and parameter optimization was the research topic when SVM was applied to predict temperatures. Based on an optimization algorithm, the SVM method was set up. A grid search algorithm is the first parameter-selection method for SVM, which mainly finds the optimal by grid meshing. Although the use of a grid search could easily find the global optimal solution, it takes much more time for a larger-scale optimization. Using genetic algorithms, parameter optimization was perfect the first time, but the optimal values could only represent local optimization. Fewer GA evolutions are required for finding the optimal value range instead of the optimal values. The PSO optimization algorithm combined a speed and location search model. To find the optimal value, the speed and location of particles are constantly updated when they iterate by tracking the extreme, so their speed and position updated. However, it is easy to miss the optimal value because the convergence speed is becoming slower and swings in the vicinity during the late research period where the optimal value of the oscillation occurs. Thus, based on an improved genetic algorithm, the detailed grid mesh optimization was set up to find the optimal parameter range (Wang et al., 2012). According to the developed genetic algorithm, three-dimensional parameters were optimised at a spatial angle to find the optimal parameter range, and it was verified using the grid method.
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APPLICATION OF GA, PSO AND PSO-BFGS FOR THE INVERSE ESTIMATION PROBLEM

APPLICATION OF GA, PSO AND PSO-BFGS FOR THE INVERSE ESTIMATION PROBLEM

This paper deals with the estimation of unknown parameter using evolutionary algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). A hybrid algorithm is also proposed with the combination of PSO and Broyden Fletcher Goldfarb Shanno Algorithm (BFGS). These algorithms are not only used as optimization techniques but also applied for the estimation of unknown parameters or function appearing in the mathematical model. Initially, the proposed evolutionary algorithms are validated with the available benchmarks in literature. A mathematical model that represents a lumped system in heat transfer is considered to be the forward solution. An inverse problem is proposed to estimate the unknown parameters appearing in the forward model. In order to generate measurement data, the temperatures obtained by solving the forward model for the known parameters are then added with noise at different levels. The unknown parameters appearing in the mathematical model is successfully estimated using these algorithms.
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Synchronous generator parameters identification on line using small population based particle swarm optimization

Synchronous generator parameters identification on line using small population based particle swarm optimization

The parameters of synchronous generators are the basis of power system analysis and operating control. Parameters identification of synchronous generator plays a key role for the power system stability analysis. In this paper, a small population-based particle swarm optimization (SPPSO) approach is used to acquire synchronous generator on-line model quickly and accurately. In the proposed approaches, three operations are introduced to improve the performance of the algorithm, namely mutation operation, DE-acceleration operation and migration operation. Furthermore, the synchronous generator practical model and the PMU data are adopted. The simulation results of the model obtained by SPPSO have been compared with hybrid genetic algorithm and PSO. The SPPSO algorithm shows better performance on the convergence as well as computation time and effort.
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