A multi-vehicle search strategy based on the simplex algorithm is proposed. The strategy is mapped into a hierarchical scheme with three layers. The upper layer is described by a discrete-event system. The output of t...
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A multi-vehicle search strategy based on the simplex algorithm is proposed. The strategy is mapped into a hierarchical scheme with three layers. The upper layer is described by a discrete-event system. The output of this layer is a set of way-points for the vehicles and it is used by the middle layer in order to coordinate the motion of the vehicles. The lower layer drives each vehicle to a way-point. The paper compares two possible discrete coordination strategies: one minimizes the total travelled distance and the other prevents trajectories to intersect. Minimization of vehicle intercommunication is also studied.
A continuous global optimization heuristic for a stochastic approximation of an objective function, which itself is not globally convex, is introduced. The objective function arises from the simulation based indirect ...
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A continuous global optimization heuristic for a stochastic approximation of an objective function, which itself is not globally convex, is introduced. The objective function arises from the simulation based indirect estimation of the parameters of agent based models of financial markets. The function is continuous in the variables but non-differentiable. Due to Monte Carlo variance, only a stochastic approximation of the objective function is available. The algorithm combines features of the Nelder-Mead simplex algorithm with those of a local search heuristic called threshold accepting. The Monte Carlo variance of the simulation procedure is also explicitly taken into account. We present details of the algorithm and some results of the estimation of the parameters for a specific agent based model of the DM/US-$ foreign exchange market. (C) 2002 Elsevier Science B.V. All rights reserved.
Docking of metalloproteinase inhibitors remains a challenge due to the zinc multiple coordination geometries and the lack of appropriate force field parameters to model the metal/ligand interactions. In this study, we...
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Docking of metalloproteinase inhibitors remains a challenge due to the zinc multiple coordination geometries and the lack of appropriate force field parameters to model the metal/ligand interactions. In this study, we explore the docking accuracy and scoring reliability for the docking of matrix metalloproteinase (MMP) inhibitors using AutoDock 3.0. Potential problems associated with zinc ion were investigated by docking 16 matrix metalloproteinase ligands to their crystal structures. A good coordination between the zinc binding group (ZBG) and the zinc was shown to be a prerequisite for the ligand to fit the binding site. A simplex optimization of zinc parameters, including zinc radius, well depth, and zinc charges, was performed utilizing the 14 MMP complexes with good docking. The use of optimized zinc parameters (zinc radius: 0.87 Angstrom;well depth: 0.35 kcal/mol;and zinc charges: +0.95 e) shows improvement in both docking accuracy at the zinc binding site and the prediction of bindin free energies. Although further improvement in the docking procedure, particularly the scoring function is needed, optimization of zinc parameters provides an efficient way to improve the performance of AutoDock as a drug discovery tool. (C) 2003 Elsevier Inc. All rights reserved.
We address the problem of design optimization for individual and population pharmacokinetic studies. We develop Splus generic functions for pharmacokinetic design optimization: IFIM, a function for individual design o...
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We address the problem of design optimization for individual and population pharmacokinetic studies. We develop Splus generic functions for pharmacokinetic design optimization: IFIM, a function for individual design optimization similar to the ADAPT II software, and PFIM_OPT;a function for population design optimization which is an extension of the Splus function PFIM for population design evaluation. Both evaluate and optimise designs using the simplex algorithm. IFIM optimizes the sampling times in continuous intervals of times;PFIM OPT optimizes either, for a given group structure of the population design, only the sampling times taken in some given continuous intervals or, both the sampling times and the group structure, performing then statistical optimization. A combined variance error model can be supplied with the possibility to include parameters of the error model as parameters to be estimated. The performance of the optimization with the simplex algorithm is demonstrated with two pharmacokinetic examples: by comparison of the optimized designs to those of the ADAPT II software for IFIM, and to those obtained using a grid search or the Fedorov - Wynn algorithm for PFIM OPT: The influence of the variance error model on design optimization was investigated. For a given total number of samples, different group structures of a population design are compared, showing their influence on the population design efficiency. The functions IFIM and PFIM OPT offer new efficient solutions for the increasingly important task of optimization of individual or population pharmacokinetic designs.
A heuristic approach is developed for supply chain planning modeled as multi-item multi-level capacitated lot sizing problems. The heuristic combines Lagrangian relaxation (LR) with local search. Different from existi...
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A heuristic approach is developed for supply chain planning modeled as multi-item multi-level capacitated lot sizing problems. The heuristic combines Lagrangian relaxation (LR) with local search. Different from existing LR approaches that relax capacity constraints and/or inventory balance constraints, our approach only relaxes the technical constraints that each 0-1 setup variable must take value 1 if its corresponding continuous variable is positive. The relaxed problem is approximately solved by using the simplex algorithm for linear programming, while Lagrange multipliers are updated by using a surrogate subgradient method that ensures the convergence of the dual problem in case of the approximate resolution of the relaxed problem. At each iteration, a feasible solution of the original problem is constructed from the solution of the relaxed problem. The feasible solution is further improved by a local search that changes the values of two setup variables at each time. By taking the advantages of a special structure of the lot-sizing problem, the local search can be implemented by using a modified simplex algorithm, which significantly reduces its computation time. Numerical experiments show that our approach can find very good solutions for problems of realistic sizes in a short computation time and is more effective than an existing commercial optimization code.
We analyze a randomized pivoting process involving one line and n points in the plane. The process models the behavior of the RANDOM-EDGE simplex algorithm on simple polytopes with it facets in dimension n - 2. We obt...
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We analyze a randomized pivoting process involving one line and n points in the plane. The process models the behavior of the RANDOM-EDGE simplex algorithm on simple polytopes with it facets in dimension n - 2. We obtain a tight O(log(2) n) bound for the expected number of pivot steps. This is the first nontrivial bound for RANDOM-EDGE, which goes beyond bounds for specific polytopes. The process itself can be interpreted as a simple algorithm for certain 2-variable linear programming problems, and we prove a tight Theta(n) bound for its expected runtime. (C) 2003 Wiley Periodicals, Inc.
The concept and details of implementation, user interface, and file formats of the Coarse-Graining OPTimization "CGOPT" (version 2.0) software package are described. It enables the user to map a full-detaile...
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The concept and details of implementation, user interface, and file formats of the Coarse-Graining OPTimization "CGOPT" (version 2.0) software package are described. It enables the user to map a full-detailed atomistic model of a polymer system to a coarse-grained mesoscale model (roughly one bead per chemical repeat unit). The features include a control script, an implementation of the simplex algorithm and an implementation of a structure difference algorithm based on potential inversions from distribution functions. Taken together, this machinery is able to automatically modify and optimize the force field by way of user-selected potential functions for mesoscale polymer simulations. (C) 2002 Elsevier Science B.V. All rights reserved.
Column generation is a well-known and widely practiced technique for solving linear programs with too many variables or constraints to include in the initial formulation explicitly. Instead, the required column inform...
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Column generation is a well-known and widely practiced technique for solving linear programs with too many variables or constraints to include in the initial formulation explicitly. Instead, the required column information is generated at each iteration of the simplex algorithm. This paper shows that, even if the number of variables is low enough for explicit inclusion in the model with the available technology, it may still be more efficient to resort to column generation for some class of problems.
The Weibull distribution is used to model wind speeds at four locations in Oman. The scale and shape parameters were estimated using three methods, the Chi-square method, method of moments and regression method. It wa...
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The Weibull distribution is used to model wind speeds at four locations in Oman. The scale and shape parameters were estimated using three methods, the Chi-square method, method of moments and regression method. It was observed that the estimates using the Chi-squared method gave the best overall fit to the distribution of the wind data. Both the scale and shape parameters varied widely over the months. (C) 2002 Elsevier Science Ltd. All rights reserved.
The paper presents a study, on the reliability and limits of the downhill simplex algorithm used to reconstruct optical parameters of double-layer silica waveguides from the measured effective indexes. It provides a n...
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The paper presents a study, on the reliability and limits of the downhill simplex algorithm used to reconstruct optical parameters of double-layer silica waveguides from the measured effective indexes. It provides a number of surface plots of the merit function and explains the behavior of the algorithm in practical situations where the measured effective index data are affected by noise.
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