Global Optimization in Action

Pintér, János D.

doi:10.1007/978-1-4757-2502-5

Cited by 401 publications

(195 citation statements)

References 293 publications

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“…Further explanation of exact and heuristic methods can be explained by [5] whereby exact method guarantee on an optimum solution of the problem whereas heuristic method does not promise an optimum solution. In addition, exact method includes naive approaches, complete (enumerative) search strategies, homotopy (parameter continuation), successive approximation (relaxation), branch and bound algorithms, Bayesian search (partition) algorithms and adaptive stochastic research algorithms [9]. As for heuristic method, there are decomposition methods, inductive methods, reduction methods, constructive methods and local search methods [10].…”

Section: Introductionmentioning

confidence: 99%

Extended basic integer programming models for multiple scheduling problems

Aizam¹,

Sim²

2016

AIP Conference Proceedings

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Abstract. Numerous approaches have been done to solve scheduling problems from various fields. Many researchers tend to restrict their models to its' own field. Thus, researchers have to repeat the whole procedure to create a new model to solve another field or aspect of the situation. This whole repetition will be costly and time consuming. The main objective of this study is to develop general basic models to solve multiple scheduling problems. The extended general models formed were based on three types of scheduling problems; nurse, university course and examination with the common requirement obtained in the literature. Additional general constraints were also added to the model. All together there are 6 constraints employed in this research. A 0-1 integer programming is used to construct and solve these models with the support of AIMMS mathematical software. The results obtained showed that the models formed were applicable to all type of scheduling problems with consideration of all similar constraints.

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Section: Introductionmentioning

confidence: 99%

Extended basic integer programming models for multiple scheduling problems

Aizam¹,

Sim²

2016

AIP Conference Proceedings

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“…The theoretical foundations leading to LGO and some key implementation aspects are summarized in [18]. Platform-specific software implementation versions, numerous illustrative applications and case studies are discussed e.g.…”

Section: A Brief Introduction To Lgomentioning

confidence: 99%

“…Platform-specific software implementation versions, numerous illustrative applications and case studies are discussed e.g. in [18][19][20][21][22][23].…”

Section: A Brief Introduction To Lgomentioning

confidence: 99%

Benchmarking the LGO Solver Suite within the COCO Test Environment

Hatwagner

Pintér²

2014

Acta Tech Jaur

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Optimization problems often arise in the context of scientific-engineering research and practice. In many situations that require optimization, there is no need to develop new, highly customized software for a new problem, because there are readily usable optimization packages to choose from. Benchmarking and testing software environments can greatly assist the process of choosing an appropriate optimization tool. The benchmarking environments typically include a substantial collection of well-known and widely used test functions; they also offer a properly defined methodology to compare the solvers considered. One of these benchmarking environments is called COCO (an abbreviation that stands for "COmparing Continuous Optimizers"). COCO has been used at the annual BBOB (Black-Box Optimization Benchmarking) workshops. COCO assesses the capabilities of optimization solvers based on a given collection of test problems and evaluation criteria, under identical and fully reproducible circumstances. The goal of our present study is to benchmark the LGO (Lipschitz Global Optimizer) solver suite and to compare it to several other solvers using COCO.

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“…Numerous efforts have been directed since the 1960s towards efficient determination of the optimum values of the parameters of such models. Good references on earlier work on various methods of optimization can be found in Törn & Zilinskas (1989), Pintér (1995) and Duan (2003). The direct-search methods, also called enumerative methods, use the values of the objective function only (Spendley et al, 1962;Nelder & Mead, 1965;Rosenbrock, 1960;Hooke & Jeeves, 1969), whereas the gradient or calculus-based methods also involve the evaluation of the derivatives of the function (Levenberg, 1944;Marquardt, 1963;Fletcher & Powell, 1963;Fletcher, 1970).…”

Section: Introductionmentioning

confidence: 99%

Comparative assessment of six automatic optimization techniques for calibration of a conceptual rainfall—runoff model

Goswami

O’Connor

2007

Hydrological Sciences Journal

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In this application-based study, six automated strategies of parameter optimization are used for calibration of the conceptual soil moisture accounting and routing (SMAR) model for rainfall-runoff simulation in two catchments, one small and the other large. The methods used are: the genetic algorithm, particle swarm optimization, Rosenbrock's technique, shuffled complex evolution of the University of Arizona, simplex search, and simulated annealing. A comparative assessment is made using the Nash-Sutcliffe model efficiency index and the mean relative error (MRE) to evaluate the performance of each optimization method. It is found that the degree of variation of the values of the water balance parameters is generally less for the small catchment than for the large one. In the case of both catchments, the probabilistic global population-based optimization method of simulated annealing is considered best in terms of having the least variability of parameter values in successive tests, thereby alleviating the phenomenon of equifinality in parameter optimization, and also in producing the lowest MRE in verification.Key words optimization; SMAR; genetic algorithm; Rosenbrock; simplex; particle swarm optimization; simulated annealing; shuffled complex evolution Evaluation comparative de six techniques automatiques d'optimisation pour le calage d'un modèle conceptuel pluie-débitRésumé Dans cette étude de mise en application, six stratégies automatiques d'optimisation de paramètres sont utilisées pour le calage du modèle conceptuel "soil moisture accounting and routing" (SMAR) pour la simulation pluie-débit dans deux bassins versants, l'un petit et l'autre grand. Les méthodes utilisées sont: l'algorithme génétique, l'optimisation par essaim de particules, la technique de Rosenbrock, la technique "shuffled complex evolution" de l'Université d'Arizona, la méthode du simplex et celle du recuit simulé. Une estimation comparative est menée à l'aide de l'indice d'efficience de modélisation de Nash-Sutcliffe et de l'erreur relative moyenne pour évaluer la performance de chaque méthode d'optimisation. Il apparaît que le degré de variation des valeurs des paramètres du bilan hydrologique est généralement inférieur pour le petit bassin versant que pour le grand. Dans le cas de l'ensemble des deux bassins, la méthode d'optimisation du recuit simulé, probabiliste globale basée sur la population, apparaît être la meilleure en termes de moindre variabilité des valeurs des paramètres au cours de tests successifs, ce qui atténue le phénomène d'équifinalité vis-à-vis de l'optimisation des paramètres et également de la minimisation de l'erreur relative moyenne lors de la vérification.

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Global Optimization in Action

Cited by 401 publications

References 293 publications

Extended basic integer programming models for multiple scheduling problems

Extended basic integer programming models for multiple scheduling problems

Benchmarking the LGO Solver Suite within the COCO Test Environment

Comparative assessment of six automatic optimization techniques for calibration of a conceptual rainfall—runoff model

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