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With the development of Graphics Processing Unit (GPU) and the Compute Unified Device Architecture (CUDA) platform, researchers shift their attentions to general-purpose computing applications with GPU. In this paper, we present a novel parallel approach to run artificial fish swarm algorithm (AFSA) on GPU. Experiments are conducted by running AFSA both on GPU and CPU respectively to optimize four...
A great number of dimensionality reduction methods are finally reduced to solving generalized eigenvector problems. Optimization techniques are promising ways to solve the parameter selection problems in these dimensionality reduction methods. The most important step in these optimization methods is to compute the objective function with respect to the parameter, which depends on computing the gradient...
Node placement is a crucial issue affects different performance parameters of the wireless sensor network such as energy consumption, network connectivity and some parameters related to the application of the deployed wireless network. This paper presents a multi-objective optimization methodology for node placement in wireless sensor network design. Emerging computational intelligence leads to optimization...
Particle swarm optimization (PSO) algorithms are now being practiced for more than a decade and have been extended to solve various types of optimization problems. However, straightforward application of PSO suffers from premature convergence and lacks of intensification around the local best locations. In this paper, we propose a new particle swarm optimization strategy, namely, particle swarm optimization...
This paper analyzed the enterprise process energy consumption systematically with a lot of statistic data starting from energy efficiency, and established the energy consumption prediction model based on genetic algorithm of wavelet neural network (GA-WNN). This paper made previous optimization training with genetic algorithm, which have feature of natural evolution regularity, to the weights and...
A multi-objective daily generation scheduling model for the hydropower stations is established, in which two objective functions including maximization of peak-energy capacity benefits and maximization of power generation are involved, and the hierarchy particle swarm optimization (HPSO) algorithm solving the model is proposed, the algorithm can handle the level multi-objective optimization problem...
This paper gives a new performance analysis method, which is through the Markov chain and queuing network model for Web server services thread and queue modeling, computing performance level coefficient, the different performance measurement data through the normalized integration, and presents an intuitive, quantitative results to match the best server configuration parameters. And on this basis...
This paper applies Particle Swarm Optimization algorithm (PSO) in a Multi-objective Vehicle Routing Problem with Time Window (MVRPTW). Firstly, through the problem analysis, establish a versatile mathematical model. Secondly, introduce an effective particle code to successfully implement the algorithm. Finally, examples prove that PSO can be obtained the optimal solution quickly and efficiently of...
The goal of image segmentation is to cluster pixels into salient image regions, it is the most significant step in image analysis. Thresholding is a simple but effective tool to separate objects from the background, which is one of the most popular algorithms. The artificial bee colony algorithm (ABC) is a recently presented meta-heuristic algorithm, which has been successfully applied to solve many...
A modified immune genetic algorithm for power system reactive power optimization is presented. Immune factor has been added to the basic genetic algorithm, which can effectively speed up the convergence. By using the concept of entropy and the expectation of antibody in the selection operation, the algorithm can ensure the population diversity and reduce the possibility of falling into local optimum...
This paper considers the SMT placement process optimization problem to minimize the assembly time of Multi-head surface mounting machine (SMM).A mathematical model is firstly established.Considering that the component pick-and-place sequencing and the feeder assignment are the two main important factors which determine the assembly time of a device,the optimization problem is divided into two sub-problems,called...
When the robotic belt grinding system needs to control the removal rate accurately, to optimize the grinding parameters is an important task after the model is obtained. In this paper, what is different from the previous methods is that the output of the model is not the removal rate but the workpiece feedrate vw or the normal grinding force Fn, so the reverse resolution of the model doesn't need...
Many real networks have been found to have a rich degree of symmetry, which is a universal structural property of network, we investigate the synchronization property of oscillators on symmetry network. One of fascinating problems related to symmetry network is how to enhance phase synchronization under symmetry structure. For this purpose, statistics the orbits of automorphism groups of the symmetry...
Pulse Coupled Neural Network (PCNN) with the phenomena of synchronous pulse bursts is different from traditional artificial neural networks. In this paper, the auto-wave in PCNN is used to solve combination optimization problems. The preventive feedback based on triangle inequality theorem is introduced to prevent bad solutions, and Preventive Feedback Pulse Coupled Neural Network (PFPCNN) is presented...
Two-ray model is the basic line-of-sight propagation model for radio-wave propagation. Fermat's principle of Geometrical optics theory shows that the optical ray path is the minimum distance, which can make the propagation problem of radio-wave into the optimization problem of path function. Particle Swarm Optimization (PSO) with the characteristic of better searching optimization, was used to calculate...
Knapsack problem is regarded as a difficult NP problem in computer algorithms. According to the characteristics of knapsack problems, an algorithm (called KP-KEA) for solving knapsack problems utilizing knowledge evolution principle is proposed. In this algorithm, an initial knowledge base is formed at first. The next work is to inherit excellent knowledge individuals by inheritance operator, produce...
The optimization of national air route network (ARN) becomes imperative for the increase of air traffic. The Crossing Waypoints Location Problem (CWLP) is a crucial problem in the optimal design of ARN. Although CWLP is in fact a typical multi-objective problem, most state-of-art work solve it from a single objective way, which only considers the flight efficiency and other important properties of...
An improved particle swarm optimization algorithm (PSO) combined with quantum genetic algorithm is proposed, to solve the problems that the PSO is difficult to converge for benchmark complex problems and it's parameters are hard to define. The new algorithm is used for submersible path planning and simulation on some standard test functions. The results show that the improved is superior to the standard...
Due to the fast convergence, Particle swarm optimization (PSO) has been advocated to be especially suitable for multiobjective optimization. However, there is no information-sharing of with other particles in the population, except that each particle can access the global best. Thus, the premature convergence and lacks of intensification around the local best locations are inevitable during extending...
Many VMI Supply Chains produced a set of candidate stock addresses. The set was clustered by distance and storage. Only one address was selected to found warehouse in every class. The address is selected by all VMI supply chains which decides to found warehourse in that class. All VMI Supply Chains share the storage in one stock address class. Every VMI Supply Chain makes optimization of path itself...
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