This paper investigates moral hazard issues using Markov processes with payoffs and strategy options, an algorithm developed by Howard [Howard, R.A., 1960. Dynamic Programming and Markov Processes. MIT Technology Pres...
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This paper investigates moral hazard issues using Markov processes with payoffs and strategy options, an algorithm developed by Howard [Howard, R.A., 1960. Dynamic Programming and Markov Processes. MIT Technology Press/John Wiley & Sons, NY]. An option consists of a probability vector and an expected payoff for a given state. Each state may have one or more options. Choice of options for each state, called "a strategy", must be fixed by the manager at the start. An "n-period" manager tries to maximize his/her cumulative payoff (undiscounted or discounted) over n periods. As n -> infinity, the manager's strategy becomes in line with owners' interest as the firm lasts indefinitely. Managerial implications of the analyses are examined. (c) 2006 Elsevier B.V. All rights reserved.
A function minimization algorithm that updates solutions based on approximated derivative information is proposed. The algorithm generates sample points with Gaussian white noise, and approximates derivatives based on...
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A function minimization algorithm that updates solutions based on approximated derivative information is proposed. The algorithm generates sample points with Gaussian white noise, and approximates derivatives based on stochastic sensitivity analysis. Unlike standard trust region methods which calculate gradients with n or more sample points, where n is the number of variables, the proposed algorithm allows the number of sample points M to be less than n. Furthermore, it ignores small amounts of noise within a trust region. This paper addresses the following two questions: how does the derivative approximation become worse when the number of sample points is small? Can the algorithm find good solutions with inexact derivative information when the objective landscape is noisy? Through intensive numerical experiments using quadratic functions, the algorithm is shown to be able to approximate derivatives when M is about n/10 or more. The experiments using a formulation of the traveling salesman problem show that the algorithm can find reasonably good solutions for noisy objective landscapes with inexact derivatives information. (C) 2002 Elsevier Science Ltd. All rights reserved.
An adaptive filtering model is designed using Hybrid Particle Swam optimization (HPSO). Confirmation principle and method of model parameters is studied. HPSO has high convergence speed and search accuracy. The method...
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An adaptive filtering model is designed using Hybrid Particle Swam optimization (HPSO). Confirmation principle and method of model parameters is studied. HPSO has high convergence speed and search accuracy. The method proved effective in the computer simulation results.
Immune evolutionary algorithm is proposed based on the evolutionary principle in the immune system. In the algorithm, two new parameters of expansion radius and mutation radius are defined to construct a small neighbo...
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ISBN:
(纸本)0780378652
Immune evolutionary algorithm is proposed based on the evolutionary principle in the immune system. In the algorithm, two new parameters of expansion radius and mutation radius are defined to construct a small neighborhood and a large neighborhood. Then expansion and mutation operations are designed to perform local and global search respectively by using the two neighborhoods, thus, two-level neighborhood search mechanism is realized. The results of multi-modal function optimization show that the algorithm has nice global and local searching performances. The algorithm is employed to design a neuro-fuzzy controller for real-time control of an inverted pendulum. In order to avoid the combinatorial explosion of fuzzy rules due to multivariable inputs, state variable synthesis scheme is suggested to reduce the number of fuzzy rules. Experimental results show that the designed controller can control actual inverted pendulum successfully.
A function minimization algorithm that updates solutions based on approximated derivative information is proposed. The algorithm generates sample points with Gaussian white noise, and approximates derivatives based on...
详细信息
A function minimization algorithm that updates solutions based on approximated derivative information is proposed. The algorithm generates sample points with Gaussian white noise, and approximates derivatives based on stochastic sensitivity analysis. Unlike standard trust region methods which calculate gradients with n or more sample points, where n is the number of variables, the proposed algorithm allows the number of sample points M to be less than n. Furthermore, it ignores small amounts of noise within a trust region. This paper addresses the following two questions: how does the derivative approximation become worse when the number of sample points is small? Can the algorithm find good solutions with inexact derivative information when the objective landscape is noisy? Through intensive numerical experiments using quadratic functions, the algorithm is shown to be able to approximate derivatives when M is about n/10 or more. The experiments using a formulation of the traveling salesman problem show that the algorithm can find reasonably good solutions for noisy objective landscapes with inexact derivatives information. (C) 2002 Elsevier Science Ltd. All rights reserved.
When Simulated Annealng (SA) is applied to continuous optimization problems, the design of the neighborhood used in SA becomes important. Many experiments are necessary to determine an appropriate neighborhood range i...
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ISBN:
(纸本)9781424400225
When Simulated Annealng (SA) is applied to continuous optimization problems, the design of the neighborhood used in SA becomes important. Many experiments are necessary to determine an appropriate neighborhood range in each problem, because the neighborhood range corresponds to distance in Euclidean space and is decided arbitrarily. We propose Multi-point Simulated Annealing with Adaptive Neighborhood (MSA/AN) for continuous optimization problems, which determines the appropriate neighborhood range automatically. The proposed method provides a neighborhood range from the distance and the design variables of two search points, and generates candidate solutions using a probability distribution based on this distance in the neighborhood, and selects the next solutions from them based on the energy. In addition, a new acceptance judgment is proposed for multi-point SA based on the Metropolis criterion. The proposed method shows good performance in solving typical test problems.
Design optimization using high-fidelity computational fluid dynamics simulations is becoming increasingly popular, sustaining the desire to make these methods more computationally efficient. A reduction in problem dim...
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Design optimization using high-fidelity computational fluid dynamics simulations is becoming increasingly popular, sustaining the desire to make these methods more computationally efficient. A reduction in problem dimensions as a result of improved parameterization techniques is a common contributor to this efficiency. The focus of this paper is on the high-fidelity aerodynamic design of airfoil shapes. A multifidelity design search method is presented which uses a parameterization of the airfoil pressure distribution followed by inverse design, giving a reduction in the number of design variables used in optimization. Although an expensive analysis code is used in evaluating airfoil performance, computational cost is reduced by using a low-fidelity code in the inverse design process. This method is run side by side with a method which is considered to be a current benchmark in design optimization. The two methods are described in detail, and their relative performance is compared and discussed. The newly presented method is found to converge towards the optimum design significantly more quickly than the benchmark method, providing designs with greater performance for a given computational expense.
A hybrid trajectory optimization procedure for a class of solar-electric-propulsion, gravity-assist, outer-planet missions is presented. The parameter space of a target mission is often nonconvex and a calculus-of-var...
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A hybrid trajectory optimization procedure for a class of solar-electric-propulsion, gravity-assist, outer-planet missions is presented. The parameter space of a target mission is often nonconvex and a calculus-of-variations-based optimization algorithm suffers difficulties efficiently exploring this space. A hybrid procedure using a genetic algorithm to drive a calculus-of-variations program is developed to automate searching over a reduced parameter space. Employing the hybrid procedure, the delivered mass profiles of a Uranus and Pluto mission are generated more quickly than by using the calculus-of-variations optimization algorithm alone.
This paper investigates the ability of the largest producer in an electricity market to manipulate both the electricity and emission allowances markets to its advantage. A Stackelberg game to analyze this situation is...
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This paper investigates the ability of the largest producer in an electricity market to manipulate both the electricity and emission allowances markets to its advantage. A Stackelberg game to analyze this situation is constructed in which the largest firm plays the role of the leader, while the medium-sized firms are treated as Cournot followers with price-taking fringes that behave competitively in both markets. Since there is no explicit representation of the best-reply function for each follower, this Stackelberg game is formulated as a large-scale mathematical program with equilibrium constraints. The best-reply functions are implicitly represented by a set of nonlinear complementarity conditions. Analysis of the computed solution for the Pennsylvania-New Jersey-Maryland electricity market shows that the leader can gain substantial profits by withholding allowances and driving up NOx allowance costs for rival producers. The allowances price is higher than the corresponding price in the Nash-Cournot case, although the electricity prices are essentially the same.
The solution of the minimum-state (MS) approximation of the unsteady aerodynamic forces is brought in this work into the form of a nonlinear optimization problem. This new formulation takes as design variables all of ...
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The solution of the minimum-state (MS) approximation of the unsteady aerodynamic forces is brought in this work into the form of a nonlinear optimization problem. This new formulation takes as design variables all of the aerodynamic lag terms (known also as aerodynamic roots) as well as the two matrices that directly operate on these lag terms. This new formulation enables the explicit determination of the remaining matrices that form the MS approximation, and it does not require enforcing any equality constraints. Furthermore, it also permits the derivation of simple analytical expressions for the gradients of the least-square (LS)-type objective function. This combination of explicit expressions for both the gradients and some of the unknown matrices leads to a dramatic reduction in computational labor. It is also shown that by appropriately scaling the tabulated aerodynamic matrix a significantly accelerated rate of convergence is obtained during the process of optimization, whereas a general weighting scheme might considerably slow down this convergence. It is also shown that the preceding scaling of the tabulated aerodynamic matrix can also significantly reduce the computational labor required by current methods of solution that are based on iterative LS analysis. The new formulation presented in this work leads to better results (i.e., lower values for the objective function) than those obtained using current iterative LS-based methods that use preassumed values for the aerodynamic lag terms. At the same time, the computer CPU time required by these two methods of solution is of the same order, with only slightly higher CPU values needed for the new formulation. On the other hand, current methods based on iterative LS analysis that attempt to optimize the aerodynamic roots rather than use preassumed values need extensive added computational labor to the extent that makes them practically unattractive.
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