Immune Detector Optimization Algorithm with Co-evolution and Monte Carlo

This paper presents an optimization algorithm for designing linear concentrating solar collectors using stochastic programming. A Monte Carlo technique is used to quantify the performance of the collector design in terms of an objective function, which is then minimized using a modified Kiefer–Wolfowitz algorithm that uses sample size and step size controls. This process is more efficient than traditional “trial-and-error” methods and can be applied more generally than techniques based on geometric optics. The method is validated through application to the design of three different configurations of linear concentrating collector.

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An application of the multi-stage Monte Carlo optimization algorithm to aggregate production planning

International Journal of Production Economics ◽

10.1016/0925-5273(94)90083-3 ◽

1994 ◽

Vol 35 (1-3) ◽

pp. 207-213 ◽

Cited By ~ 2

Author(s):

M.A. Bakir ◽

M.D. Byrne

Keyword(s):

Monte Carlo ◽

Production Planning ◽

Optimization Algorithm ◽

Aggregate Production Planning ◽

Monte Carlo Optimization ◽

Aggregate Production ◽

Multi Stage

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Convergence of a global stochastic optimization algorithm with partial step size restarting

Advances in Applied Probability ◽

10.1239/aap/1013540175 ◽

2000 ◽

Vol 32 (2) ◽

pp. 480-498 ◽

Cited By ~ 5

Author(s):

G. Yin

Keyword(s):

Monte Carlo ◽

Global Optimization ◽

Simulated Annealing ◽

Weak Convergence ◽

Stochastic Optimization ◽

Optimization Algorithm ◽

Step Size ◽

Wide Range ◽

Stochastic Optimization Algorithm ◽

Annealing Algorithms

This work develops a class of stochastic global optimization algorithms that are Kiefer-Wolfowitz (KW) type procedures with an added perturbing noise and partial step size restarting. The motivation stems from the use of KW-type procedures and Monte Carlo versions of simulated annealing algorithms in a wide range of applications. Using weak convergence approaches, our effort is directed to proving the convergence of the underlying algorithms under general noise processes.

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On Zero-Order Optimization in Problem of the Pressure Computing in Finite Elastic-Creep Deformations

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.685.300 ◽

2016 ◽

Vol 685 ◽

pp. 300-304 ◽

Cited By ~ 1

Author(s):

Maxim Anop ◽

Evgenii Murashkin ◽

Marina V. Polonik

Keyword(s):

Monte Carlo ◽

Stress State ◽

Monte Carlo Method ◽

Optimization Algorithm ◽

Simple Zero ◽

External Pressure ◽

Zero Order ◽

The Monte Carlo Method ◽

Strain Stress ◽

Creep Deformations

The present study is devoted to the problem of optimal loading pressure identification by the prescribed displacements vector. The framework of finite elastocreep strains is used. The problem of deformation of the material in the vicinity of microdefect was considered. Integro-differential equations for the external pressure, irreversible deformations and displacements were derived. The simple zero-order optimization algorithm like the Monte Carlo method for considering problem was proposed. The optimal strain-stress state parameters were computed and analyzed.

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Interval Reliability Analysis of Parameters of Section of Main Beam

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.644-650.4991 ◽

2014 ◽

Vol 644-650 ◽

pp. 4991-4994

Author(s):

Jian Fang Zhou ◽

Guang Ran Lu

Keyword(s):

Monte Carlo ◽

Reliability Analysis ◽

Optimization Algorithm ◽

Reliability Index ◽

Main Beam ◽

One Dimensional ◽

Advantages And Disadvantages ◽

Interval Reliability ◽

Dimensional Optimization

In the calculation of the reliability of hydraulic, due to extensive uncertainties, there are lots of errors in probabilistic reliability analysis. In this paper, by comparing the non-probabilistic reliability index of different function, we get the advantages and disadvantages of one-dimensional optimization algorithm and method of Monte Carlo, and compare the functions of each variable of main beam to obtain the influence of the precision of the variables.

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