Derivative-free optimization involves all the methods used to minimize an objective function when its derivatives are not available and when the function is expensive. We present here a trust region algorithm based on...
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ISBN:
(纸本)0889864241
Derivative-free optimization involves all the methods used to minimize an objective function when its derivatives are not available and when the function is expensive. We present here a trust region algorithm based on radial basis functions instead of the classical second order polynomials. Actually, our surrogate of the objective function is a mixed radial and polynomial model. We present results on the medical image registration problem. On the instances of this problem, our method surpasses all the tested state-of-the-art derivative-free algorithms.
the objective of this paper to demonstrate the validity of a new concept in dynamic stability enhancement of a synchronous Generator connected to the grid via an AC transmission line, using a small power parallel DC l...
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ISBN:
(纸本)0889864241
the objective of this paper to demonstrate the validity of a new concept in dynamic stability enhancement of a synchronous Generator connected to the grid via an AC transmission line, using a small power parallel DC link. Superior ability of rapid modulation of power in a DC transmission line is used to damp the oscillations in order to enhance the stability as well as to increase the power transfer capability. A Matlab-Simulink model was developed to compare the dynamic responses of the system with and without the proposed DC damping scheme. the simulations revealed that the use of a small DC link would greatly enhance the dynamic stability and power transfer capability of the AC line alone.
Integration of artificial intelligence (AI) in solar power systems for maximum power point tracking (MPPT) is increasingly popular due to the limitations of traditional MPPT methods in locating the global maximum powe...
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ISBN:
(数字)9798331530952
ISBN:
(纸本)9798331530969
Integration of artificial intelligence (AI) in solar power systems for maximum power point tracking (MPPT) is increasingly popular due to the limitations of traditional MPPT methods in locating the global maximum power point (GMPP) under partial shading conditions. Unlike conventional techniques, AI-based algorithms excel at identifying the GMPP even when multiple local maximum power points (MPPs) exist. Compared to traditional methods, AI-based MPPT techniques like reinforcement learning and fuzzy logic typically offer higher efficiency, reduced steady-state oscillation, and faster convergence but require significant resources and investment. this paper compares two AI-based MPPT methods-Fuzzy Logic and Reinforcement Learning using simulation. Each AI approached its strengths and weaknesses, complicating on optimal method selection. It provided a detailed efficiency comparison of these AI methods by implementing them in a solar power grid system under various environmental conditions.
this paper introduces GENOSIM-p: a Generic traffic microsimulation parameter optimization tool using Parallel Genetic Algorithms (PGA), and its implementation to the St. Clair network in Downtown Toronto, Canada. GENO...
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ISBN:
(纸本)0889864241
this paper introduces GENOSIM-p: a Generic traffic microsimulation parameter optimization tool using Parallel Genetic Algorithms (PGA), and its implementation to the St. Clair network in Downtown Toronto, Canada. GENOSIM-p is the parallel version of previous optimization software GENOSIM [1]. GENOSIM-p employs PGA to calibrate traffic microsimulation models. In this research, we will use PARAMICS: a microscopic traffic simulation platform. PARAMICS consists of high performance cross-linked traffic models having multiple user-adjustable parameters. GENOSIM-p will use PGA to manipulate those control parameters and search for an optimal set of values that minimize the discrepancy between simulation output and real field data.
this paper tackles the issue of fixed-topology design problem in wireless networks using recently developed tools of computational intelligence. the design the fixed-topology in a wireless network aims at finding the ...
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ISBN:
(纸本)0889864241
this paper tackles the issue of fixed-topology design problem in wireless networks using recently developed tools of computational intelligence. the design the fixed-topology in a wireless network aims at finding the topology configuration that satisfies the traffic requirements and performance and reliability constraints with minimal cost. the new multimedia application and the dynamic and rapidly changed environment of the wireless networks make the fixed-topology design a new challenge. the population-based incremental learning, combines in an efficient way the features of genetic algorithms and competitive learning. In this work, an enhanced population-based algorithm is proposed to handle the fixed-topology design problem in wireless networks.
this paper proposes optimal operational planning for cogeneration system (CGS) using particle swarm optimization (PSO). CGS is usually connected to various facilities such as refrigerators, reservoirs, and cooling tow...
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ISBN:
(纸本)0889864241
this paper proposes optimal operational planning for cogeneration system (CGS) using particle swarm optimization (PSO). CGS is usually connected to various facilities such as refrigerators, reservoirs, and cooling towers. In order to generate optimal operational planning for CGS, startup/shutdown status and/or input/output values of the facilities for each control interval should be determined. the facilities may have nonlinear input-output characteristics. therefore, the problem can be formulated as a mixed-integer nonlinear optimization problem (MINLP) or a 0-1 integer nonlinear optimization problem (INLP). PSO can be easily expanded to be utilized for MINLP and INLP. this paper proposes optimal operational planning for cogeneration system using particle swarm optimization. the proposed method is applied to typical cogeneration planning problems with promising results.
In portfolio management, the selection of portfolio weights has received considerable interest. Considering the expected return, risk and uncertainty, the portfolio distribution is to be determined. the maximum entrop...
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ISBN:
(纸本)9780889866874
In portfolio management, the selection of portfolio weights has received considerable interest. Considering the expected return, risk and uncertainty, the portfolio distribution is to be determined. the maximum entropy (MaxEnt) principle is one of the efficient methods to find distribution of random variables. thus, in this study, the maximum entropy (MaxEnt) principle is presented as an alternative method of determination of portfolios distribution. Beside, a numerical example is also presented to illustrate this principle.
the selection of the most appropriate remediation technology is found to cleanup a chemical landfill. Multiobjective optimization is used to model the conflicting interests and priorities of the interest groups. Six p...
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ISBN:
(纸本)0889863911
the selection of the most appropriate remediation technology is found to cleanup a chemical landfill. Multiobjective optimization is used to model the conflicting interests and priorities of the interest groups. Six particular distance-based methods are used and the uncertainty of model parameters is taken into account by a special simulation approach.
Microelectronic products for automotive applications are characterized by an ever-increasing level of integration complexity concerning electronic and multi-physical subsystems. Since the functional verification of a ...
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ISBN:
(纸本)9780889866874
Microelectronic products for automotive applications are characterized by an ever-increasing level of integration complexity concerning electronic and multi-physical subsystems. Since the functional verification of a developed system is normally done by transient simulations, system engineers have to integrate models of different physical disciplines efficiently into one simulation environment. this paper discusses the parameter optimization of a simulation protocol for a multi-simulator co-simulation toot called InSiMS, using a multiple-objective evolutionary algorithm. Two objective functions are used to minimize the error for the transient signals and to maximize the simulation speedup compared to a set of nominal simulation runs. the benefit of the proposed method will be illustrated by the simulation of a fuel injection system, consisting of a hydromechanic injector and an electromagnetic controller.
this paper investigates methods of CAD optimization of single phase induction motors. the augmented Lagrangian multiplier method (ALMM) is described and compared withthe well-known interior penalty function method. T...
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ISBN:
(纸本)0889863377
this paper investigates methods of CAD optimization of single phase induction motors. the augmented Lagrangian multiplier method (ALMM) is described and compared withthe well-known interior penalty function method. the ALMM is shown to be superior, both in its ability to use infeasible starting points and in the number of required iterations to reach an optimum.
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