Automatic termination of parallel optimization runs of global stochastic optimization methods by upper limit criterion

Natalja Bulipopa; Jurijs Sulins

doi:10.11592/bit.130504

Automatic termination of parallel optimization runs of global stochastic optimization methods by upper limit criterion

Biosystems and Information technology ◽

10.11592/bit.130504 ◽

2013 ◽

Vol 2 (1) ◽

pp. 15-18 ◽

Cited By ~ 1

Author(s):

Natalja Bulipopa ◽

Jurijs Sulins

Keyword(s):

Stochastic Optimization ◽

Optimization Methods ◽

Parallel Optimization ◽

Upper Limit

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Adaptive Gene Level Mutation

Algorithms ◽

10.3390/a14010016 ◽

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.

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.215-216.133 ◽

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.

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