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Reactive power optimization in power system is a complex nonlinear combinatorial optimization problem with multiple constrained conditions. However, direct neural dynamic programming (direct NDP) approach based on on-line measurements can be employed in this situation, which is independent of models. In this paper, on the basis of applicable analysis to reactive power optimization, this algorithm...
This paper proposes a novel scheme that we call the opposition based comprehensive learning particle swarm optimizers (OCLPSO), which employs opposition based learning (OBL) for population initialization and also for exemplar selecting. This scheme enables the swarm to explore and exploit with the more diversity and not to be premature convergence. Experiments were conducted on benchmark functions...
To incorporate existing and new information tools, in particular optimization methods and software, an integrated resource management system was proposed for ferry companies. The system adopts a hierarchical system architecture which incorporates all the modules in the three layers: the supporting layer, the application layer, and the management and control layer. The blank ticket rolls were considered...
To enhance the local search capability of quantum-inspired evolutionary algorithm, a novel memetic algorithm based on real-observation quantum-inspired evolutionary algorithms (MArQ) was proposed. MArQ is a hybrid algorithm combining QIEA with local search techniques. In MArQ, QIEA was used to explore the whole solution space and tabu search was employed to exploit the neighboring domains of the searched...
Parameters setting is an important problem of evolution algorithms, include differential evolution algorithm. It has an effect on the performance of evolution algorithms. Although there is only three control parameters in differential evolution (DE) algorithm, the parameters setting is also a difficult problem. Self-adaptation is highly beneficial for adjusting the control parameters, especially when...
This paper is aimed to present a genetic algorithm focusing on the sexual selection used the Pareto based approach for solving multi-objective optimization problems. It uses a concept of sexual selection with different types of gender and mutation rates based on the sex to produce offspring. Its performance was evaluated by the well-known benchmark functions as well as also tested with a networking...
Trough binocular vision the fast disparity image is often desired for many applications, but, most algorithms could not easily be application because of complexity. We present an image-processing technique that can fast estimate depth image from binocular vision images. By finding out the lines which present the best matched area in the disparity space image, the depth can be estimated. When detecting...
Floorplanning is an important problem in the very large integrated circuit (VLSI) design automation. It??s an NP-hard combinatorial optimization problem. The particle swarm optimization (PSO) has been proved to be a good optimization algorithm with outstanding global performance. However, PSO cannot be directly used in the combinatorial optimization problem due to its continuous characteristic. In...
Bacterial foraging optimization (BFO) is a recently developed nature-inspired optimization algorithm, which is based on the foraging behavior of E. coli bacteria. However, BFO possesses a poor convergence behavior over complex optimization problems as compared to other nature-inspired optimization techniques like genetic algorithm (GA) and particle swarm optimization (PSO). This paper first analyzes...
One of the key factors that limit support vector machines (SVMs) application in large sample problems is that the large-scale quadratic programming (QP) that arises from SVMs training cannot be easily solved via standard QP technique. The sequential minimal optimization (SMO) is current one of the major methods for solving SVMs. This method, to a certain extent, can decrease the degree of difficulty...
It is an important issue to embrace market opportunities, and optimize the management decision for enterprises. This paper presents an automatic selection principle of combinatorial forecast based on rule reasoning, and marketing mix tactics. An optimization model of marketing mix based on forecast is established. It is of practical significance to improve the ability of taking market opportunities,...
A large number of multi-objective optimization evolutionary algorithms(MOEAs) have been developed in the past two decades. To compare these methods rigorously, or to measure the performance of a particular MOEA quantitatively, a variety of performance measures have been proposed. In this paper, some existing widely-used performance measures are briefly reviewed and compared according different properties...
The connection analysis of pipeline network is one of the most important functions of pipeline spatial analysis. In the abstract, that problem is the minimum spanning tree calculation problem -- the combination optimization problem. Traditional method can get only one tree concerning one factor. In this paper, genetic algorithm is used to solve minimum spanning tree to get a group solution, from which...
Estimation of Distribution Algorithms (EDAs) is new kinds of colony evolution algorithms. It produces its new generation by constructing probability distribution model through counting excellent information of individuals of present colony EDAs first, and then sampling the model. To solve the NP-Hard question as EDAs searching optimum network structure, a new Maximum Entropy Estimation of Distribution...
Part-building orientation (PBO) and scanning direction of path planning (SDPP) are two tasks of process plan in rapid prototyping technology (RPT). Through investigating the geometric issues of STL model and process planning of RPT. This paper establishes optimizing model based on the considerations of staircase effect, support area and production time. And then, through analyzing the hatching characteristic...
With the development of computer technology and artificial intelligence in automatic control field, all kinds of parameters tuning methods of PID controller have emerged in endlessly, which bring much energy for the study of PID controller, but many advanced tuning methods behave not so perfect as to be expected. GA and chaos optimizing was integrated, by use of the chaos serial's property of "ergodicity,...
Many different network types have been promoted for use in control systems. ControlNet network is a well known member of the family of protocols - the CIP (Control an Information Protocol). It has been developed by Rockwell Automation. The ControlNet network??s mission is to provide reliable, high-speed transport of two basic types of application information: control and I/O data; non-time critical...
Forest harvesting adjustment is a decision-making which is large and complex system. In this paper, we analysis the shortcomings of the traditional harvest adjustment problems, and establish the model of multi-target harvest adjustment. As intelligent optimization, adaptive genetic algorithm has the parallel mechanism and the inherent global optimization characteristics which are suitable for multi-objective...
Provided with plenty of data (experience), data mining techniques are widely used to extract suitable management skills from the data. Nevertheless, in the early stages of a manufacturing system, only rare data can be obtained, and built scheduling knowledge is usually fragile. In most research process of production scheduling based on simulation conditions, the waiting time of every essential step...
Under the application background of network security evaluation, a mechanism for situation element extraction based on Particle Swarm Optimization (PSO) and Fuzzy Neural Network (FNN) is proposed. Firstly, the input dataset of historical situation element is pre-fuzzed and then transformed into fuzzy logic rule which can be mapped between neural network layers. Meanwhile, PSO is used to achieve global...
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