Papers by Keyword: Evolutionary Programming

Abstract: Power transformer has been identified as crucial and vital equipment in power system. Any disturbance such as faults will result in immense impact to the whole power system. This paper presents the development of an Evolutionary Programming (EP) – Taguchi Method (TM) – Artificial Neural Network (ANN) based technique for the classification of incipient faults in power transformer using Dissolved Gas Analysis (DGA) method based on historical industrial data. It involved the development of ANN model and embedding TM and EP as the optimization techniques in order to enhance the system accuracy and efficiency. ANN is a powerful computational technique that mimics how human brain process information. It has great ability to learn from experiences and examples, hence greatly suitable for classification, pattern recognition and forecasting purposes. In designing the ANN model, there are parameters which need to be chosen wisely. However, there is no systematic ways and guidelines to select the optimal ANN parameters. It is greatly dependent on the design knowledge and experiences of the experts. The process of finding suitable parameters is become difficult, tedious and time consuming, thus optimization technique is needed to overcome the shortcoming. In this study, TM and EP were employed as the optimization techniques to improve the ANN-based model. The findings obtained from the proposed technique have proved the effectiveness of both TM and EP in optimizing the ANN model. As a result, a reliable EP-TM-ANN based system has been successfully developed that can classify incipient faults in power transformer.

Abstract: The paper presents a comparison of Computational Intelligence techniques are Evolutionary Programming Swarm Optimization (EPSO), Particle Swarm Optimization (PSO), Evolutionary Programming (EP) to optimal placement and sizing of Static Var Compensator. The technique has been implemented to minimize the transmission loss and improve the voltage profile of the system. Simulation performed on standard IEEE 118-Bus RTS and indicated that EPSO a feasible to achieve the objective function.

Abstract: Retracted paper: Recent advances in secure information and Bayesian methodologies have paved the way for randomized algorithms. Here, we verify the evaluation of multi-processors. This is an important point to understand. In order to fix this issue, we concentrate our efforts on arguing that courseware and consistent hashing can collaborate to overcome this riddle.

Abstract: Development of better wind and thermal coordination dispatch is necessary to determine the optimal dispatch scheme that can integrate wind power reliably and efficiently. In this paper hybrid Evolutionary Programming (EP) and Particle Swarm Optimization (PSO) approach is utilized to coordinate the wind and thermal generation dispatch and to minimize the total production cost considering wind power generation and valve effect of thermal units. Numerical studies have been performed for three different test systems, i.e., six, thirteen and forty generating unit systems. The simulation results demonstrate the effectiveness of the proposed approach and shows the effect of wind power generation in reducing the total fuel.

Abstract: Flexible Manufacturing Systems-FMS is a term with various types of definitions, each of them trying to describe the complexity and the generalized features. One of these features is their complexity, along with difficulties in building models that capture the system in all its important aspects. In a heterogeneous flexible system, the scheduling events or actions could be a combinatorial problem which claims a particular solution. Manufacturing scheduling process, in special for FMS, is a very difficult scheduling problem, because involves all the aspects of the processes: order, resources, transportation system i.e. automated vehicle guided, perturbation factors such as breakdowns of machine, etc. Typically, the scheduling problem is a NP-hard problem modeled in mathematical form. If we simulate n jobs or orders which have to be assigned to the m machines or resources, we will observe that the mathematical solution is a huge number that means (n!)m possibilities of solutions. The challenge of researchers is to solve this equation in a reasonable time with an optimal solution, and of course with minimal resources. Those scientists applied many solutions which became Operational Research-OR or Combinatorial Optimization-CO areas using a various methods: Local Search-LS, Artificial Intelligence-AI, heuristic method, priority rules, memetic or hybrid techniques which combine this techniques.

Abstract: Image segmentation is an important task in image analysis and processing. Many of the existing methods for segmenting a multi-component image (satellite or aerial) are very slow and require a priori knowledge of the image that could be difficult to obtain. Furthermore, the success of each of these methods depends on several factors, such as the characteristics of the acquired image, resolution limitations, intensity in-homogeneities and the percentage of imperfections induced by the process of image acquisition. Evolutionary programming(EP) has been applied with success to many numerical and combinatorial optimization problems in recent years. EP has rather slow convergence rates, however, on some function optimization problems. In this paper the new evolutionary programming is proposed to overcome the premature convergence. There are two step mutation in the new evolutionary programming. The first step is responsible for searching the whole space. The second is responsible for searching the local part in detail. The cooperation and specialization between different two step mutation are considered during the algorithm design. The new evolutionary programming can use in image segmentation and the experimental results show the new evolutionary programming is efficient.

Abstract: Industrial clusters can be found very often in the world, particularly in many developing countries. To build virtual enterprise based on an industrial cluster is one of the most important ways to improve the agility and competitiveness of manufacturing enterprises in the cluster. One of the key factors towards the success of virtual enterprises is the correct selection of cooperative partners in the virtual enterprise. An approach of order allocation and partner selection in the environment of industrial clusters is proposed. This approach is composed of two stages: task-resource matching and quantitative evaluation. In the first stage the potential candidates are identified and in the second stage evolutionary programming is applied to deal with partner selection and order allocation problem. The target function, in which the load rate of candidate enterprise is taken as the main variable, is developed, and a simplified example is used to verify the feasibility of the proposed approach. The result suggests that the proposed model and the algorithm obtain satisfactory solutions.

Abstract: The goal of this study was to analyze the possibilities of fuzzy neural networks and evolutionary programming methods for creating the human skill based stock trading systems. In stock exchange markets, the relationships between market variables are generally too complex to make rightful trading decisions and to earn stabile profits using classical system theory approach. On the other hand, there are a lot of trading experts-practicians that successfully trade stocks and achieve good results in the stock exchange markets. A useful technique for expert-knowledge extraction is the supervised learning methods, where human-experts actions are mapped using fuzzy-neural networks. In this paper we outline this procedure. Also we discuss the possibilities for improvement the proposed human skill based stock trading systems. An efficient biological system evolves slowly over the course of hundreds and housands of generations of individuals. Later generations have more fit and are more capable than earlier ones. Similarly, we have used evolutionary techniques to .evolve. the fuzzy-neural network based stock trading system, which is capable to solve the stock trading task more efficiently. Proposed procedure was tested using virtual trading system that uses historical data from US stock markets. The first results confirmed the good opportunities of the proposed approach.