Stochastic Optimization Methods

2005 ◽  
Keyword(s):  
Stochastic Optimization ◽  
Optimization Methods

scholarly journals Adaptive Gene Level Mutation

Algorithms ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 16
Author(s):  
Jalal Al-afandi ◽  
Horváth András
Keyword(s):  
Genetic Algorithms ◽  
Stochastic Optimization ◽  
Specific Problem ◽  
Traditional Approach ◽  
Fitness Function ◽  
Optimization Methods ◽  
Analytic Solutions ◽  
Problem Representation ◽  
Travelling Salesman ◽  
Gene Level

Genetic Algorithms are stochastic optimization methods where solution candidates, complying to a specific problem representation, are evaluated according to a predefined fitness function. These approaches can provide solutions in various tasks even, where analytic solutions can not be or are too complex to be computed. In this paper we will show, how certain set of problems are partially solvable allowing us to grade segments of a solution individually, which results local and individual tuning of mutation parameters for genes. We will demonstrate the efficiency of our method on the N-Queens and travelling salesman problems where we can demonstrate that our approach always results faster convergence and in most cases a lower error than the traditional approach.

Optimization Design of Trusses Based on Covariance Matrix Adaptation Evolution Strategy Algorithm

2012 ◽  
Vol 215-216 ◽  
pp. 133-137
Author(s):  
Guo Shao Su ◽  
Yan Zhang ◽  
Zhen Xing Wu ◽  
Liu Bin Yan
Keyword(s):  
Stochastic Optimization ◽  
Covariance Matrix ◽  
Optimization Problems ◽  
Fitness Function ◽  
Optimization Methods ◽  
Evolution Strategy ◽  
Covariance Matrix Adaptation ◽  
Study Results ◽  
Highly Nonlinear ◽  
Adaptation Evolution

Covariance matrix adaptation evolution strategy algorithm (CMA-ES) is a newly evolution algorithm. It has become a powerful tool for solving highly nonlinear multi-peak optimization problems. In many real-world optimization problems, the location of multiple optima is often required in a search space. In order to evaluate the solution, thousands of fitness function evaluations are involved that is a time consuming or expensive processes. Therefore, conventional stochastic optimization methods meet a special challenge for a very large number of problem function evaluations. Aiming to overcome the shortcoming of stochastic optimization methods in the high calculation cost, a truss optimal method based on CMA-ES algorithm is proposed and applied to solve the section and shape optimization problems of trusses. The study results show that the method is feasible and has the advantages of high accuracy, high efficiency and easy implementation.

Application of Stochastic Optimization Methods in the Gas Turbine Projection Problems

2010 ◽  
Author(s):  
V. E. Afanasjevska ◽  
A. A. Tronchuk ◽  
M. L. Ugryumov
Keyword(s):  
Stochastic Optimization ◽  
Gas Turbine ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Operating Conditions ◽  
Technical System ◽  
Design Stage ◽  
Problem Solution ◽  
Operating Characteristics ◽  
Stochastic Optimization Problem

When projecting the gas turbine an important problem is an ensuring the high values of gas turbine parameters and required gas turbine operating characteristics on the different operating conditions. These requirements can be reached by engine function units system perfecting on base of multicriterion stochastic optimization problems solution. Three stochastic optimization problems definitions were formulated. Each problem has own features and can be used for different application solution. These applied problems are: M-problem can be used on the technical system unit conceptual design stage; V-problem can be used for the problem solution of tolerancing during the technical system unit production; P-problem can be used for interval analysis of technical system functional unit. The multicriterion stochastic optimization problem rational decision is realized by the evolutional method. This method makes it possible to find the solution with given accuracy by attraction the less information recourses than standard methods. In the stochastic optimization problems definitions the input data random character is taken into account. It makes it possible to find the optimal values of desired parameters. These parameters ensure the maximal probability of finding the objective function in given range.

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