Papers by Keyword: Particle Swarm Optimization (PSO)

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Authors: Ching Tang Hsieh, Chia Shing Hu, Meng Shian Shih
Abstract: Conventional 2D face recognition methods often struggle when a subject's head is turned even slightly to the side. In this study, a face recognition system based on 3D head modeling that is able to tolerate facial rotation angles was constructed by leveraging the Open source graphic library (OpenGL) framework. To minimize the extensive angle searching time that often occurs in conventional 3D modeling, Particle Swarm Optimization (PSO) was used to determine the correct facial angle in 3D. This reduced the angle computation time to 6 seconds, which is significantly faster than other methods. Experimental results showed that successful ID recognition can be achieved with a high recognition rate of 90%.
Authors: Ting Zhuang, Xu Tang Zhang, Zhen Xiu Hou
Abstract: In order to reuse 3D models and design knowledge efficiently, a number of 3D model retrieval algorithms based on content features of models have been proposed in recent years. Although, the features-based methods have achieved some progress, there are two limitations stilly. The first, single content feature cant be suit for all kinds of 3D models; different features have different strengths and weakness. The second, semantic gap, the semantic of model is independent from low-level characteristics. For those two issues, we present a 3D engineering model retrieval algorithm based on relevance feedback and features combination in this paper. The proposed method takes advantage of multiple features by allying them with weights. In the retrieval process, our method utilizes the Particle Swarm Optimization to update the weights dynamically based on users relevance feedback information in order to narrowing the gap between high-level semantic knowledge and low-level content features. The Experiments, based on publicly available 3D model database Engineering Shape Benchmark (ESB) developed by Purdue University, suggested that the proposed approach has better retrieval ability than traditional ones.
Authors: Cheng Jun Xia, Yun Zhou, Hao Yu Huang
Abstract: The chaos particle swarm optimization algorithm was presented to solving optimal power flow. The proposed OPF considers the total cost of generators as the objective functions. To enhance the performance of algorithm, a premature convergence strategy was proposed. The strategy can be divided into two parts. In the first part, a method is introduced to judge premature convergence, while another part provides an advance method to improve the performance of algorithm with searching the solution in total feasible region. The control strategy used to prevent premature convergence will obtain starting values for initial particle before program iterating, so it can provide bitter probability of detecting global optimum solution. The simulation results on standard IEEE 30-bus system minimizing fuel cost of generator show the effectiveness of the chaos particle swarm optimization algorithm, and can obtain a good solution.
Authors: Zhi Gang Lian, Ye Jun Gao, Chun Lei Ji, Xue Wu Wang
Abstract: This paper proposes a combined local best particle swarm optimization algorithm (CLBPSO) which combined with local optimum particle information. And it gives three ways of combination local information. Experimental results indicate that the CLBPSO algorithm improves the search performance on the benchmark functions significantly. On the basis of experimental results, we will also compare these three methods with each other to find the best one.
Authors: Song Chai, Yu Bai Li, Chang Wu, Jian Wang
Abstract: Real-time task schedule problem in Chip-Multiprocessor (CMP) receives wide attention in recent years. It is partly because the increasing demand for CMP solutions call for better schedule algorithm to exploit the full potential of hardware, and partly because of the complexity of schedule problem, which itself is an NP-hard problem. To address this task schedule problem, various of heuristics have been studied, among which, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Simulated Annealing (SA) are the most popular ones. In this paper, we implement these 3 schedule heuristics, and compare their performance under the context of real-time tasks scheduling on CMP. According to the results of our intensive simulations, PSO has the best fitness optimization of these 3 algorithms, and SA is the most efficient algorithm.
Authors: Xiao Ling Yao, Yan Ni Wang
Abstract: Based on the simple transfer function design, this thesis presents a technology for complicated transfer function design. It converts complicated transfer function design problem into the fusing of several simple transfer function. The keystone is to formulate the transfer function fusing problem into searching for optimal fusing proportion, and to generate the new fusing proportion using a similarity evaluation method, which is based on expectation fitness. To a large extent, it simplifies the design process of complicated transfer function.
Authors: Zhi Qiang Gao, Li Xia Liu, Wei Wei Kong, Xiao Hong Wang
Abstract: A novel composite framework of Cuckoo Search (CS) and Particle Swarm Optimization (PSO) algorithm called CS-PSO is proposed in this paper. In CS-PSO, initialization is substituted by chaotic system, and then Cuckoo shares optimums in the global best solutions pool with particles in PSO to improve parallel cooperation and social interaction. Furthermore, Cloud Model, famous for its outstanding characteristics of the process of transforming qualitative concepts to a set of quantitative numerical values, is adopted to exploit the surrounding of the local solutions obtained from the global best solution pool. Benchmark test results show that, CS-PSO can converge to the global optimum solution rapidly and accurately, compared with other algorithms, especially in high dimensional problems.
Authors: Ming Gang Dong, Xiao Hui Cheng, Qin Zhou Niu
Abstract: To solve constrained optimization problems, an Oracle penalty method-based comprehensive learning particle swarm optimization (OBCLPSO) algorithm was proposed. First, original Oracle penalty was modified. Secondly, the modified Oracle penalty method was combine with comprehensive learning particle swarm optimization algorithm. Finally, experimental results and comparisons were given to demonstrate the optimization performances of OBCLPSO. The results show that the proposed algorithm is a very competitive approach for constrained optimization problems.
Authors: Na Li, Yuan Xiang Li
Abstract: A new particle swarm optimization algorithm (a diversity guided particles swarm Optimization), which is guided by population diversity, is proposed. In order to overcome the premature convergence of the algorithm, a metric to measure the swarm diversity is designed, the update of velocity and position of particles is controlled by this criteria, and the four sub-processes are introduced in the process of updating in order to increase the swarm diversity, which enhance to the ability of particle swarm optimization algorithm (PSO) to break away from the local optimum. The experimental results exhibit that the new algorithm not only has great advantage of global search capability, but also can avoid the premature convergence problem effectively.
Authors: Shuang Wei, De Fu Jiang, Yang Gao
Abstract: This paper presents a diversity-guided Particle swarm optimization (PSO) algorithm to resolve the Blind source separation (BSS) problem. Because the independent component analysis (ICA) approach, a popular method for the BSS problem, has a shortcoming of premature convergence during the optimization process, the proposed PSO algorithm aims to improve this issue by using the diversity calculation to avoid trapping in the local optima. In the experiment, the performance of the proposed PSO algorithm for the BSS problem has been investigated and the results are compared with the conventional PSO algorithm. It shows that the proposed PSO algorithm outperforms the conventional PSO algorithm.
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