Titlebook: Computational Optimization, Methods and Algorithms; Slawomir Koziel,Xin-She Yang Book 2011 Springer Berlin Heidelberg 2011 Design optimiza

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Optimization Algorithms,a diverse range of algorithms for optimization, including gradient-based algorithms, derivative-free algorithms and metaheuristics. Modern metaheuristic algorithms are often nature-inspired, and they are suitable for global optimization. In this chapter, we will briefly introduce optimization algori

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Surrogate-Based Methods,many cases, optimization of such objectives in a straightforward way, i.e., by applying optimization routines directly to these functions, is impractical. One reason is that simulation-based objective functions are often analytically intractable (discontinuous, non-differentiable, and inherently noi

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Maximum Simulated Likelihood Estimation: Techniques and Applications in Economics, chain Monte Carlo (MCMC) methods. The techniques are applicable to parameter estimation and Bayesian and frequentist model choice in a large class of multivariate econometric models for binary, ordinal, count, and censored data.We implement the methodology in a study of the joint behavior of four c

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Optimizing Complex Multi-location Inventory Models Using Particle Swarm Optimization,cessitate the introduction of simplifying assumptions, and therefore, their scope is limited. To surmount these restrictions, we use Simulation Optimization by coupling a simulator that evaluates the performance of the system with an optimizer. This idea is illustrated for a very general class of mu

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Traditional and Hybrid Derivative-Free Optimization Approaches for Black Box Functions,s is due in part to each solver having its own inherent strengths and weaknesses. For example, one approach may be global but have slow local convergence properties, while another may have fast local convergence but is unable to globally search the entire feasible region. In order to take advantage

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Simulation-Driven Design in Microwave Engineering: Methods,tain accurate responses of microwave structures. In the same time the task of microwave component design can be formulated and solved as an optimization problem where the objective function is supplied by an EM solver. Unfortunately, accurate simulations may be computationally expensive; therefore,

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An Enhanced Support Vector Machines Model for Classification and Rule Generation,th small sampling, non-linearity and high dimension. Data preprocessing, parameter selection, and rule generation influence performance of SVM models a lot. Thus, the main purpose of this chapter is to propose an enhanced support vector machines (ESVM) model which can integrate the abilities of data