Cloud computing environment for the efficient resources allocation is an important issue in the field of cloud computing. The resources in Cloud computing application platform are distributed widely and with great div...
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ISBN:
(纸本)9781467318556;9781467318570
Cloud computing environment for the efficient resources allocation is an important issue in the field of cloud computing. The resources in Cloud computing application platform are distributed widely and with great diversity. User demands of real-time dynamic change are very difficult to predict accurately. The heuristic ant colony algorithm could be used to solve this kind of problems, but the algorithm has slow convergence speed and parameter selection problems. Aiming at this problem, this paper proposes an optimized ant colony algorithm based on particleswarm to solve cloud computing environment resources allocation problem.
This paper proposes an improved particle swarm optimization algorithm (PSO) for the global and local equilibrium problem of searching ability. It improves the iterative way of inertia weight in PSO, using non-linear d...
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ISBN:
(纸本)9783037852866
This paper proposes an improved particle swarm optimization algorithm (PSO) for the global and local equilibrium problem of searching ability. It improves the iterative way of inertia weight in PSO, using non-linear decreasing algorithm to balance, then PSO combines with simulated annealing(SA). Finally, the optimization test experiments are carried out for the typical functions with the algorithm (ULWPSO-SA), and compare with the basic PSO algorithm. Simulation experiments show that local search ability of algorithm, convergence speed, stability and accuracy have been significantly improved. In addition, the novel algorithm is used in the parameter optimization of support vector machines (ULWPSOSA-SVM), and the experimental results indicate that it gets a better classification performance compared with SVM and PSO-SVM.
As energy issues become increasingly prominent, the electrification of aviation aircraft has gradually become a research hotspot, and electric helicopters are also in a stage of rapid development. Based on the flight ...
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ISBN:
(纸本)9798350387780;9798350387797
As energy issues become increasingly prominent, the electrification of aviation aircraft has gradually become a research hotspot, and electric helicopters are also in a stage of rapid development. Based on the flight performance analysis method of fuel-powered helicopters and combined with the characteristics of electric power system, this paper proposes a calculation method for the flight performance of electric helicopters and establishes an analysis model. This model can demonstrate the performance of electric helicopters in three aspects: vertical flight, climbing flight and horizontal flight. Then, the particleswarmoptimization (PSO) algorithm is used to optimize the overall flight performance of the helicopter. It can allocate weights reasonably according to design requirements and selectively optimize key performance indicators. The results show that the adopted optimizationalgorithm has good effect and applicability to the overall parameters optimization problem of electric helicopters.
A novel cultural algorithm based on particleswarmoptimization (PSO) algorithm was proposed in this paper. After analyzing the partner selection problems of virtual enterprise, the CPSO algorithm was presented to sol...
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ISBN:
(纸本)9789881563811
A novel cultural algorithm based on particleswarmoptimization (PSO) algorithm was proposed in this paper. After analyzing the partner selection problems of virtual enterprise, the CPSO algorithm was presented to solve enterprise alliance problem within reasonable time and cost. There are certain number partners of each sub-task in virtual enterprise environment. The objective is, by selecting the optimal combination of partners, to minimize project's completion time and project's total cost. We tested the CPSO algorithm against the PSO method. Simulation results demonstrate that it can be superior to the regular PSO. We also tested the CPSO algorithm with the exhaustion method to show the algorithm's efficiency.
A niche-related particleswarm meta-heuristic algorithm for dealing with multimodal optimization problem is proposed in this paper. The inspiration and numerical algorithm are presented and the Rastrigin function with...
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ISBN:
(纸本)9783319045733;9783319045726
A niche-related particleswarm meta-heuristic algorithm for dealing with multimodal optimization problem is proposed in this paper. The inspiration and numerical algorithm are presented and the Rastrigin function with numerous local optima is adopted as the illustrative example. Proposed multimodal particleswarmoptimization (MPSO) is sensitive to predetermined multimodal numbers, particle numbers, niche radius, and convergent iterations. The results show that the proposed MPSO is accurate and stable. The presented MPSO is ready for applied engineering optimization and further application.
By introducing the adaptive inertia weight, the time factor and the structure rebuilding of particleswarmoptimization (PSO), the improvements of PSO are completed. In order to improve the accuracy and convergence sp...
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ISBN:
(纸本)9781467371896
By introducing the adaptive inertia weight, the time factor and the structure rebuilding of particleswarmoptimization (PSO), the improvements of PSO are completed. In order to improve the accuracy and convergence speed, a PSO strategy is proposed, which consists of the dynamic population structure, opposition-based learning, crossover operator and variable step integral. Combining the improvement of PSO and the optimization strategy, the modified particleswarmoptimization ( MPSO) algorithm is formed. The MPSO is applied to optimize the ascent trajectory of hypersonic vehicle. The precision and efficiency of this trajectory optimization method are demonstrated by comparing the results of PSO and MPSO. The simulation results show that the performance of MPSO is significantly superior to PSO either convergence speed or convergent accuracy.
particle swarm optimization algorithm and cuckoo search algorithm both are bionic swarmoptimizationalgorithms, which are simple and convenient. They have been applied to many fields. However, the algorithms have obv...
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ISBN:
(纸本)9781509063529
particle swarm optimization algorithm and cuckoo search algorithm both are bionic swarmoptimizationalgorithms, which are simple and convenient. They have been applied to many fields. However, the algorithms have obvious disadvantages. When they are applied to complex optimization problems, they cannot obtain the optimal solutions, so some measures must be adopted in order to improve their global search ability. In this paper, particle swarm optimization algorithm and cuckoo search algorithm evolve in parallel. At the end of each generation, the better solution of the two algorithms is selected as the global optimal solution. The simulation results show that the paralleled algorithm absorbs the advantages of the two algorithms, improves the global search ability and the average convergence speed, and enhances the robustness of the algorithm. The new algorithm is able to solve complex optimization problems more efficiently.
Fuzzy C-Means clustering, FCM, is an unsupervised learning algorithm. The algorithm is easily affected by noise points and depends on the initial values. When the sample value is large, the algorithm is easy to fall i...
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ISBN:
(纸本)9781450354141
Fuzzy C-Means clustering, FCM, is an unsupervised learning algorithm. The algorithm is easily affected by noise points and depends on the initial values. When the sample value is large, the algorithm is easy to fall into local extremum. In this study, the traditional fuzzy clustering algorithm is improved, and the particle swarm optimization algorithm with global optimization ability is applied to the FCM algorithm, and chaotic technology is added. Chaotic variables produce a chaotic sequence based on the current global optimal position, using chaotic sequence has the best fitness value of particles randomly instead of a particle of the particleswarm, the improved algorithm can effectively avoid the stagnation of particles in the iteration, fast search to the global optimal solution, avoid convergence to local extremum. Experimental results indicate that this algorithm overcomes the dependence on the initial clustering centre of FCM, which brings high robustness and segmentation accuracy, and has more faster convergence speed.
Due to the significant impact on the product quality and performance, the surface roughness of produced parts by 3D printers is one of the important factors in the 3D printing process. Then, the main objective of this...
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Due to the significant impact on the product quality and performance, the surface roughness of produced parts by 3D printers is one of the important factors in the 3D printing process. Then, the main objective of this research is to determine the optimal composition of input parameters to minimize surface roughness, using hybrid artificial neural network and particleswarmalgorithm. For this purpose, after using Central Composite Design (CCD) of experiments with five independent parameters (nozzle temperature, layers thickness, printing speed, nozzle diameter and material density) with three levels, 43 flat parts were produced with a three-dimensional printer, and roughness tests were performed on produced parts. After training experimental matrix by multilayer perceptron neural network (7-4-1) with a coefficient of 0.95, the subjected matrix was combined with the particleswarmalgorithm to determine the optimal composition of input parameters. To verify results accuracy, the optimized process parameters obtained from the combined algorithm, have been tested with experimental results. In addition, to specify the effect of input parameters on the surface roughness, a quadratic model has been developed using Response Surface Method (RSM). Based on results of the hybrid algorithm, the optimal combination of input parameters was extracted. It was inferred that, the nozzle temperature of 192.20 degrees C, the layers' thickness of 100 mu m, the printing speed of 97.06 mm/s, the nozzle diameter of 0.3 mm and the internal density of 24.88% lead to the surface roughness of 11.319. Therefore, the use of this hybrid algorithm improves the surface quality of the printed parts during the 3D printing process.
Cloud computing provides the computational machines as a support of the clients utilizing cloud organize. In cloud computing, the user inputs are executed with required machines to convey the administrations. Numerous...
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ISBN:
(纸本)9781538677995
Cloud computing provides the computational machines as a support of the clients utilizing cloud organize. In cloud computing, the user inputs are executed with required machines to convey the administrations. Numerous task scheduling methods are utilized to plan the client tasks to the machines. In this paper, another successful hybrid task scheduling is proposed to minimize the total execution time using Genetic algorithm (GA) and particleswarmoptimization (PSO) algorithms. In hybrid Genetic algorithm - particleswarmoptimization (GA-PSO) algorithm, PSO helped GA to obtain better results compare to a standard genetic algorithm, Min-Min, and Max-Min algorithms results.
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