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

Show more
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.

Show more
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,

Show more
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.

Show more
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.

Show more
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.

Show more
17 Read more

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.

Show more
12 Read more

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.

19 Read more

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.

Show more
24 Read more

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.

Show more
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

Show more
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**.

Show more
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.

Show more
24 Read more

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.

Show more
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

Show more
11 Read more

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.

Show more
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.

Show more
13 Read more

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.

Show more