This paper deals with the optimum design of a permanent magnet synchronous machine (PMSM) used for the propulsion of a light electric vehicle. The optimization is employed based on evolutionary algorithm by combining ...
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ISBN:
(纸本)9781467376389
This paper deals with the optimum design of a permanent magnet synchronous machine (PMSM) used for the propulsion of a light electric vehicle. The optimization is employed based on evolutionary algorithm by combining the simulated annealing with the particle swarm approach. The optimized solution is validated numerically through finite element method (FEM). Furthermore, a prototype of the optimized machine is constructed and the control of the motor operation is tested, showing that the obtained results are validating the employed approach and the prototype corresponds to the application needs.
Pointing at optimization of the distribution transformer's maintenance based on risk, firstly this paper proposed an equipment failure rate model based on Multi-state Markov process that is suitable for Condition ...
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ISBN:
(纸本)9781479950324
Pointing at optimization of the distribution transformer's maintenance based on risk, firstly this paper proposed an equipment failure rate model based on Multi-state Markov process that is suitable for Condition Based Maintenance. And then an age reduction factor is used to modify the failure rate model. A Distribution Transformer Maintenance optimization Model whose objective function is the minimum of the risk of grid operation is established basically. The model is established from the perspective of maintenance accounting the maintenance risk and failure risk. And a hybrid optimization algorithm is proposed to solve the optimized problem. Finally using a case validates the effectiveness of this proposed method.
Fast convergence-rate, low computation-complexity and good stability are important goals in the researching area of neural network learning algorithm. A kind of parallel computing lagged-start hybridoptimization algo...
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ISBN:
(纸本)9780769538174
Fast convergence-rate, low computation-complexity and good stability are important goals in the researching area of neural network learning algorithm. A kind of parallel computing lagged-start hybrid optimization algorithm is studied, it not only integrates the basic gradient-method and the unconstrained optimizationalgorithm to realize the supplement of their advantages, but also makes full use of the high-performance computer's parallel computing features to complete the algorithm switching from one to another on time, which improves the efficiency of algorithm learning and meets the neural network system's online learning or real-time control. Combined a typical test function, a Microsoft Visual C# program is edit for the performance testing and validation of the proposed algorithm, the results is satisfied as expected.
A novel efficient transmission, toroidal drive, was introduced into the field of wind power. A novel MW (Million Watt) wind power generation speed-up machine (WPGSM) was designed to replace the gear speed-up machine. ...
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ISBN:
(纸本)9783037859520
A novel efficient transmission, toroidal drive, was introduced into the field of wind power. A novel MW (Million Watt) wind power generation speed-up machine (WPGSM) was designed to replace the gear speed-up machine. The structure scheme of the MW WPGSM was designed and the design platform for the MW WPGSM was developed by UG/OPEN, VC++ and hybrid optimization algorithm based on the unigraphics (UG) software.
This paper presents a new hybridalgorithm based on the particle swarm optimization (PSO) and the gravitational search algorithm (GSA) for solving the optimal power flow (OPF) in power systems. Performance of this app...
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ISBN:
(纸本)9781479974627
This paper presents a new hybridalgorithm based on the particle swarm optimization (PSO) and the gravitational search algorithm (GSA) for solving the optimal power flow (OPF) in power systems. Performance of this approach for the OPF problem is studied and evaluated on the standard IEEE 30-bus test system with different objective functions. Simulation results on the OPF problem show that the hybrid PSOGSA algorithm provides effective and robust high-quality solution.
Shuffled frog leaping algorithm(SFLA) is a meta-heuristic algorithm, which combines the social behavior technique and the global information exchange of memetic algorithms. But the SFLA has the shortcoming of low conv...
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Shuffled frog leaping algorithm(SFLA) is a meta-heuristic algorithm, which combines the social behavior technique and the global information exchange of memetic algorithms. But the SFLA has the shortcoming of low convergence speed while solving complex optimization problems. Particle swarm optimization(PSO) is a fast searching algorithms, but easily falls into the local optimum for the diversity scarcity of particles. In the paper, a new hybridoptimization called SFLA-PSO is proposed, which introduced PSO to SFLA by combining the fast search strategy of PSO and global search strategy of SFLA. Six benchmark functions are selected to compare the performance of SFLA-PSO, basic PSO, w PSO and SFLA. The simulation results show that the proposed algorithm SFLA-PSO possesses outstanding performance in the convergence speed and the precision of the global optimum solution.
The paper deals with an application of a hybrid artificial immune system (HAIS) to the identification problems. The HAIS is applied to identify complex impedances of room walls. This approach is based on the mechanism...
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The paper deals with an application of a hybrid artificial immune system (HAIS) to the identification problems. The HAIS is applied to identify complex impedances of room walls. This approach is based on the mechanism discovered in biological immune systems. The numerical example demonstrates that the method based on immune computation is an effective technique for solving computer aided in identification problem.
There exist some variations of the particle swarm optimization - simulated annealing optimization technique (PSOSA) hybridalgorithm for solving the PID control design problem, however most of these algorithms use the...
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ISBN:
(纸本)9781467314336
There exist some variations of the particle swarm optimization - simulated annealing optimization technique (PSOSA) hybridalgorithm for solving the PID control design problem, however most of these algorithms use the simulated annealing as a tool to escape local minimums that the PSO algorithm may get trapped in and also these algorithms initialize the particles within the solution space randomly. In this paper, the effects of initializing the particles strategically within the solution space along with the application of the SA algorithm to the hybridalgorithm at each iteration are explored. To test the effectiveness of the proposed modifications the algorithms are compared on common benchmark functions before the modified hybridalgorithm (MPSOSA) is used to design a PID controller for the inverted Pendulum problem.
Purpose - The purpose of this paper is to present the application of a novel hybridalgorithm, called MeTEO (Metric-Topological-Evolutionary-optimization), based on the combination of three heuristics inspired by arti...
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Purpose - The purpose of this paper is to present the application of a novel hybridalgorithm, called MeTEO (Metric-Topological-Evolutionary-optimization), based on the combination of three heuristics inspired by artificial life to the solution of optimization problems of a real electronic vacuum device. Design/methodology/approach - The Particle Swarm optimization (PSO), the Flock-of-Starlings optimization (FSO) and the Bacterial Chemotaxis algorithm (BCA) were adapted to implement a novel meta-heuristic MeTEO the FSO has been powerfully employed for exploring the whole space of solutions, whereas the PSO is used to explore local regions where FSO had found solutions, and BCA to refine the solutions found by PSO, thanks its better performances in local search. Findings - The optimization of the focusing magnetic field of a Travelling Wave Tubes (TWT) collector is presented in order to show the effectiveness of MeTEO, in combination with COLLGUN FE simulator and equivalent source representation. The optimization of the focusing magnetic structure is obtained by using a maximum of 100 steps for each heuristic. Practical implications - The paper describes the development of a novel efficient parallel method for the solution of electromagnetic device optimization problems. Originality/value - The paper shows the capabilities of a novel combination of optimization methods inspired by "artificial life" which allows us to achieve effective solutions of multimodal optimization problems, typical of the electromagnetic device optimization, with an acceptable computational cost, thanks also to its natural parallel implementation.
Chaos optimizationalgorithm (COA), which has the features of easy implementation, short execution time and robust mechanisms of escaping from the local optimum, is a promising tool for the engineering applications. T...
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Chaos optimizationalgorithm (COA), which has the features of easy implementation, short execution time and robust mechanisms of escaping from the local optimum, is a promising tool for the engineering applications. The design of approaches to improve the convergence of the COA is a challenging issue. Improved mutative-scale parallel chaotic optimizationalgorithm (MPCOA) are proposed in this paper, and three ways of improvements for MPCOA are investigated in detail: MPCOA combined with simplex search method, MPCOA based on competitive/cooperative inter-communication, MPCOA combined with harmony search algorithm. Several simulation results are used to show the effective performance of these chaos optimizationalgorithms. (C) 2012 Elsevier Inc. All rights reserved.
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