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Global Optimum (global + optimum)

Distribution by Scientific Domains

Engineering	33%
Chemistry	25%

Selected Abstracts

Bi-level Programming Formulation and Heuristic Solution Approach for Dynamic Traffic Signal Optimization

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 5 2006
Dazhi Sun
Conventional methods of signal timing optimization assume given traffic flow pattern, whereas traffic assignment is performed with the assumption of fixed signal timing. This study develops a bi-level programming formulation and heuristic solution approach (HSA) for dynamic traffic signal optimization in networks with time-dependent demand and stochastic route choice. In the bi-level programming model, the upper level problem represents the decision-making behavior (signal control) of the system manager, while the user travel behavior is represented at the lower level. The HSA consists of a Genetic Algorithm (GA) and a Cell Transmission Simulation (CTS) based Incremental Logit Assignment (ILA) procedure. GA is used to seek the upper level signal control variables. ILA is developed to find user optimal flow pattern at the lower level, and CTS is implemented to propagate traffic and collect real-time traffic information. The performance of the HSA is investigated in numerical applications in a sample network. These applications compare the efficiency and quality of the global optima achieved by Elitist GA and Micro GA. Furthermore, the impact of different frequencies of updating information and different population sizes of GA on system performance is analyzed. [source]

Using parallelization and hardware concurrency to improve the performance of a genetic algorithm

CONCURRENCY AND COMPUTATION: PRACTICE & EXPERIENCE, Issue 4 2007
Vijay Tirumalai
Abstract Genetic algorithms (GAs) are powerful tools for solving many problems requiring the search of a solution space having both local and global optima. The main drawback for GAs is the long execution time normally required for convergence to a solution. This paper discusses three different techniques that can be applied to GAs to improve overall execution time. A serial software implementation of a GA designed to solve a task scheduling problem is used as the basis for this research. The execution time of this implementation is then improved by exploiting the natural parallelism present in the algorithm using a multiprocessor. Additional performance improvements are provided by implementing the original serial software GA in dedicated reconfigurable hardware using a pipelined architecture. Finally, an advanced hardware implementation is presented in which both pipelining and duplicated hardware modules are used to provide additional concurrency leading to further performance improvements. Copyright © 2006 John Wiley & Sons, Ltd. [source]

Constructing nurse schedules at large hospitals

INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH, Issue 3 2003
Tiago M. Dias
Several heuristics, based on evolutive algorithms and local search, are used to solve the nurse scheduling problem at a large hospital. Due to several intricate and specific restrictions imposed on the schedules, the problem is a difficult one to solve by hand. Moreover, some of the restrictions have a subjective value attached to them, and this constrains the use of exact methods that search for global optima. In order to facilitate the use of the solver modules by the hospital staff, a user interface was also implemented. [source]

On,off minimum-time control with limited fuel usage: near global optima via linear programming

OPTIMAL CONTROL APPLICATIONS AND METHODS, Issue 3 2006
Brian J. Driessen
Abstract A method for finding a global optimum to the on,off minimum-time control problem with limited fuel usage is presented. Each control can take on only three possible values: maximum, zero, or minimum. The simplex method for linear systems naturally yields nearly such a solution for the re-formulation presented herein because the simplex method always produces an extreme point solution to the linear program. Numerical examples for the benchmark linear flexible system are presented. Copyright © 2006 John Wiley & Sons, Ltd. [source]

Dual CD system-modified MEEKC method for the determination of clemastine and its impurities

ELECTROPHORESIS, Issue 19 2010
Serena Orlandini
Abstract A dual system of CDs was used for the first time in MEEKC with the aim of determining clemastine and its three main related impurities in both drug substances and tablets. The addition of methyl-,-cyclodextrin and heptakis(2,6-di- O -methyl)-,-cyclodextrin to the microemulsion pseudo-stationary phase was essential to increase the resolving power of the system to obtain a baseline separation among the compounds. The best microemulsion composition was identified by mixture design and the effects of the factors concentrations of CDs and voltage were investigated by a response surface study applying a Central Composite Design. In both cases, Derringer's desirability function made it possible to find the global optimum, which corresponded to the following combination: microemulsion, 89.8% 10,mM borate buffer pH 9.2, 1.5% n -heptane and 8.7% of SDS/n -butanol in 1:2 ratio; 18,mM methyl-,-cyclodextrin, 38,mM heptakis(2,6-di- O -methyl)-,-cyclodextrin, 17,kV. By applying these conditions, the separation was completed in about 5.5,min. The method was validated following International Conference on Harmonisation guidelines and was applied to a real sample of clemastine tablets. [source]

An investigation of genetic algorithms for the optimization of multi-objective fisheries bioeconomic models

INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH, Issue 1 2000
S.J. Mardle
Abstract The use of genetic algorithms (GA) for optimization problems offers an alternative approach to the traditional solution methods. GA follow the concept of solution evolution, by stochastically developing generations of solution populations using a given fitness statistic, for example the achievement function in goal programs. They are particularly applicable to problems which are large, non-linear and possibly discrete in nature, features that traditionally add to the degree of complexity of solution. Owing to the probabilistic development of populations, GA do not distinguish solutions, e.g. local optima from other solutions, and therefore cannot guarantee optimality even though a global optimum may be reached. In this paper, a non-linear goal program of the North Sea demersal fisheries is used to develop a genetic algorithm for optimization. Comparisons between the GA approach and traditional solution methods are made, in order to measure the relative effectiveness. General observations of the use of GA in multi-objective fisheries bioeconomic models, and other similar models, are discussed. [source]

A New Immune Genetic Algorithm and Its Application in Redundant Manipulator Path Planning

JOURNAL OF FIELD ROBOTICS (FORMERLY JOURNAL OF ROBOTIC SYSTEMS), Issue 3 2004
Xiaoping Luo
In this paper, first the immune system is analyzed in a relatively deeper and all-sided point of view reflecting the fresh research in biology. Second, based on the previous statements, a new optimization method, the immune genetic algorithm (IGA), is presented by simulating the behavior of the biological immune system and is proved to converge to the global optimum with probability 1. Third, a new method on the multi-object optimization that is transformed into a single-object one is proposed based on the joints' best compliance in the redundant robot path planning using IGA. Last, the experiment results show that the method of this article behaves more successfully. © 2004 Wiley Periodicals, Inc. [source]

A kriging method for the solution of nonlinear programs with black-box functions

AICHE JOURNAL, Issue 8 2007
Eddie Davis
Abstract In this article, a new methodology is developed for the optimization of black-box systems lacking a closed-form mathematical description. To properly balance the computational cost of building the model against the probability of convergence to global optimum, a hybrid methodology is proposed. A kriging approach is first applied to provide information about the global behavior of the system considered, whereas a response surface method is considered close to the optimum to refine the set of candidate local optima and find the global optimum. The kriging predictor is a global model employing normally distributed basis functions, so both an expected sampling value and its variance are obtained for each test point. The presented work extends the capabilities of existing response surface techniques to address the refinement of optima located in regions described by convex asymmetrical feasible regions containing arbitrary linear and nonlinear constraints. The performance of the proposed algorithm is compared to previously developed stand-alone response surface techniques and its effectiveness is evaluated in terms of the number of function calls required, number of times the global optimum is found, and computational time. © 2007 American Institute of Chemical Engineers AIChE J, 2007 [source]

Deterministic global optimization of nonlinear dynamic systems

AICHE JOURNAL, Issue 4 2007
Youdong Lin
Abstract A new approach is described for the deterministic global optimization of dynamic systems, including optimal control problems. The method is based on interval analysis and Taylor models and employs a type of sequential approach. A key feature of the method is the use of a new validated solver for parametric ODEs, which is used to produce guaranteed bounds on the solutions of dynamic systems with interval-valued parameters. This is combined with a new technique for domain reduction based on the use of Taylor models in an efficient constraint propagation scheme. The result is that an ,-global optimum can be found with both mathematical and computational certainty. Computational studies on benchmark problems are presented showing that this new approach provides significant improvements in computational efficiency, well over an order of magnitude in most cases, relative to other recently described methods. © 2007 American Institute of Chemical Engineers AIChE J, 2007 [source]

Upper bounds for single-source uncapacitated concave minimum-cost network flow problems

NETWORKS: AN INTERNATIONAL JOURNAL, Issue 4 2003
Dalila B. M. M. Fontes
Abstract In this paper, we describe a heuristic algorithm based on local search for the Single-Source Uncapacitated (SSU) concave Minimum-Cost Network Flow Problem (MCNFP). We present a new technique for creating different and informed initial solutions to restart the local search, thereby improving the quality of the resulting feasible solutions (upper bounds). Computational results on different classes of test problems indicate the effectiveness of the proposed method in generating basic feasible solutions for the SSU concave MCNFP very near to a global optimum. A maximum upper bound percentage error of 0.07% is reported for all problem instances for which an optimal solution has been found by a branch-and-bound method. © 2003 Wiley Periodicals, Inc. [source]

On,off minimum-time control with limited fuel usage: near global optima via linear programming

Optimization of a model IV fluidized catalytic cracking unit

THE CANADIAN JOURNAL OF CHEMICAL ENGINEERING, Issue 4 2001
Rein Luus
Abstract Maximization of a profit function related to a fluidized catalytic cracking unit model was carried out by Luus-jaakola optimization procedure. A 7-dimensional search is carried out on a FCC unit described by 113 nonlinear algebraic equations and 9 differential equations. Despite the low sensitivity and the existence of several local optima, the global optimum was obtained with reasonable amount of computational effort. At the optimum, the profit function is 1% higher than when the air blowers are constrained to operate at their maximum capacity. On a réalisé par la méthode d'optimisation de Luus-jaakola la maximisation d'une fonction de profit relativement à un modèle d'unité de craquage catarytique fluidisé (FCC). Une recherche en sept dimensions est menée sur une unité FCC décrite par 113 équations algébriques non linéaires et 9 équations différentielles. Malgré la faible sensibilité et l'existence de plusieurs optimums locaux, l'optimum global a été atteint avec des efforts raisonnables en termes de calcul. À l'optimum, la fonction de profit est de 1% supérieure à celle obtenue lorsqu'on force les ventilateurs soufflants à fonctionner à leur capacité maximum. [source]