This paper considers the optimum design of flexbeam cross-sections for a full-scale bearingless helicopter rotor, using an efficient hybrid optimization algorithm based on particle swarm optimization, and an improved ...
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This paper considers the optimum design of flexbeam cross-sections for a full-scale bearingless helicopter rotor, using an efficient hybrid optimization algorithm based on particle swarm optimization, and an improved geneticalgorithm, with an effective constraint handling scheme for constrained nonlinear optimization. The basic operators of the geneticalgorithm, of crossover and mutation, are revisited, and a new rank-based multi-parent crossover operator is utilized. The rank-based crossover operator simultaneously enhances both the local, and the global exploration. The benchmark results demonstrate remarkable improvements, in terms of efficiency and robustness, as compared to other state-of-the-art algorithms. The developed algorithm is adopted for two baseline flexbeam section designs, and optimum cross-section configurations are obtained with less function evaluations, and less computation time.
In this paper, we propose a credibilistic framework for portfolio selection problem using an expected value multiobjective model with fuzzy parameters. We consider short term return, long term return, risk and liquidi...
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In this paper, we propose a credibilistic framework for portfolio selection problem using an expected value multiobjective model with fuzzy parameters. We consider short term return, long term return, risk and liquidity as key financial criteria. A solution procedure comprising fuzzy goal programming and fuzzy simulation based real-coded genetic algorithm is developed to solve the model. The proposed solution approach is considered advantageous particularly for the cases where the fuzzy parameters of the problem may assume any general functional form. An empirical study is included to illustrate the usefulness of the proposed model and solution approach in real-world applications of portfolio selection.
Cold-formed steel portal frames are a popular form of construction for low-rise commercial, light industrial and agricultural buildings with spans of up to 20m. In this article, a real-coded genetic algorithm is descr...
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Cold-formed steel portal frames are a popular form of construction for low-rise commercial, light industrial and agricultural buildings with spans of up to 20m. In this article, a real-coded genetic algorithm is described that is used to minimize the cost of the main frame of such buildings. The key decision variables considered in this proposed algorithm consist of both the spacing and pitch of the frame as continuous variables, as well as the discrete section sizes. A routine taking the structural analysis and frame design for cold-formed steel sections is embedded into a geneticalgorithm. The results show that the real-coded genetic algorithm handles effectively the mixture of design variables, with high robustness and consistency in achieving the optimum solution. All wind load combinations according to Australian code are considered in this research. Results for frames with knee braces are also included, for which the optimization achieved even larger savings in cost.
In this study, the best mixed feed was prepared by using the algorithm of particle swarm optimization (PSO) and by taking into account the breeding type and method of the poultries and various farm animals (cattle, sh...
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In this study, the best mixed feed was prepared by using the algorithm of particle swarm optimization (PSO) and by taking into account the breeding type and method of the poultries and various farm animals (cattle, sheep, rabbit), their needs, ages, and feeding costs and optimizing them all. Results obtained through PSO were compared through linear programming and real-coded genetic algorithm. According to the results that were obtained, PSO produces more rapid, more stable, and optimum values.
In this paper, we propose a multiobjective credibilistic model with fuzzy chance constraints of the portfolio selection problem. The key financial criteria used are short-term return, long-term return, risk and liquid...
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In this paper, we propose a multiobjective credibilistic model with fuzzy chance constraints of the portfolio selection problem. The key financial criteria used are short-term return, long-term return, risk and liquidity. The model generates portfolios which are optimal to the extent of achieving the highest credibility values for the objective functions. The problem is solved using a hybrid intelligent algorithm that integrates fuzzy simulation with a real-coded genetic algorithm. The approach adopted here has advantage of handling the multiobjective portfolio selection problem where fuzzy parameters are characterized by general functional forms. Numerical examples are provided to demonstrate effectiveness of the solution approach and efficiency of the model. (C) 2012 Elsevier Inc. All rights reserved.
In this article, a new model for stochastic congestion management considering system uncertainties has been developed. The model utilizes chance-constrained programming to propose the stochastic formulation for the co...
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In this article, a new model for stochastic congestion management considering system uncertainties has been developed. The model utilizes chance-constrained programming to propose the stochastic formulation for the congestion management problem. In this approach, transmission constraints are considered with stochastic models instead of deterministic models. Indeed, this approach considers network uncertainties with a specific level of probability in the optimization process. Moreover, an efficient numerical approach based on the real-coded genetic algorithm and Monte Carlo technique has been proposed to solve the chance-constrained programming based congestion management scheme. Effectiveness of the proposed algorithm has been evaluated by applying the method to the IEEE 30-bus test system.
In this paper we develop a multicriteria credibilistic framework for portfolio rebalancing. We use an expected value model with fuzzy parameters considering return, risk and liquidity as key financial criteria. The tr...
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In this paper we develop a multicriteria credibilistic framework for portfolio rebalancing. We use an expected value model with fuzzy parameters considering return, risk and liquidity as key financial criteria. The transaction costs are assumed to be paid on the basis of incremental discounts and are adjusted in the net return of the portfolio. A solution procedure based on fuzzy goal programming and a hybrid intelligent algorithm that combines fuzzy simulation with a real-coded genetic algorithm is presented to solve the portfolio rebalancing problem. The approach adopted here has the advantage of handling the multicriteria portfolio rebalancing problem where the fuzzy parameters are characterized by general functional forms. An empirical study is included to demonstrate the effectiveness of the solution approach and efficiency of the model in practical applications of rebalancing an existing portfolio. (c) 2012 Elsevier B.V. All rights reserved.
Level control is an important application in process industry. Traditionally PID controller has been used for this purpose. As level process is non-linear, whenever the process is operated outside a linearized region ...
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Level control is an important application in process industry. Traditionally PID controller has been used for this purpose. As level process is non-linear, whenever the process is operated outside a linearized region the PID controller parameters need to be tuned. In this case adaptive controllers are more suited. The objective of the paper is to apply a Modified Model Reference Adaptive Controller (MRAC) to control level in a hybrid tank system in regulatory mode and compare its disturbance rejection capability with that of a standard direct MRAC and a well-designed PID controller. In this paper, a direct MRAC, two types of modified MRACs and a PID controller are designed for a hybrid tank process and their performances are compared while operated in regulatory mode. The results show that the Modified MRACs give better disturbance rejection when compared with the MRAC and the PID controller. It is concluded that the Modified MRACs can be used to obtain very good regulatory performance during the control of nonlinear processes.
Multi-machine power system stability improvement by tuning of Static Var Compensator (SVC)-based controller parameters is investigated in the proposed method. The design problem is formulated as an optimization proble...
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ISBN:
(纸本)9781467360302;9781467360272
Multi-machine power system stability improvement by tuning of Static Var Compensator (SVC)-based controller parameters is investigated in the proposed method. The design problem is formulated as an optimization problem with a time-domain simulation-based objective function and real-coded genetic algorithm (RCGA) is used for searching optimal controller parameters. A multi-machine power system model is developed using MATLAB's SIMULINK which incorporates SVC Controller. A fault is created on the transmission line. The simulation results of the multi-machine power system without SVC Controller and with SVC Controller are presented. The simulation results are analyzed which shows that the power system becomes unstable on the occurrence of the fault if SVC controller is not used. This paper shows the effectiveness of the proposed design. The proposed method enhances the multi-machine power system stability.
This paper proposes a methodology to plan an energy-saving trajectory for a toggle mechanism by a permanent magnet synchronous motor (PMSM). The system is first identified by the numerical methods. The trajectory is d...
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This paper proposes a methodology to plan an energy-saving trajectory for a toggle mechanism by a permanent magnet synchronous motor (PMSM). The system is first identified by the numerical methods. The trajectory is described by a high-degree polynomial, which is a point-to-point (PTP) function and satisfies the end conditions of displacement, velocity, acceleration and jerk at the initial and final times. The real-coded genetic algorithm (RGA) is employed to determine the coefficients of the polynomial with the fitness function, which is the inverse of the absolute value of the input electric energy. In this paper, some discussions for planning an energy-saving trajectory are made, and three cases of the fitness functions are used.
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