Cooperative Evolution — a new algorithm for the investigation of disordered structures via Monte Carlo modelling

2005 ◽  
Vol 220 (12) ◽  
Author(s):  
Thomas Weber
Keyword(s):  
Monte Carlo ◽  
Evolutionary Algorithm ◽  
Cooperative Evolution ◽  
Disordered Structures

AbstractA new evolutionary algorithm for Monte Carlo modelling of disordered structures, called

scholarly journals Continuation Multilevel Monte Carlo Evolutionary Algorithm for Robust Aerodynamic Shape Design

2019 ◽  
Vol 56 (2) ◽  
pp. 771-786 ◽  
Author(s):  
Michele Pisaroni ◽  
Fabio Nobile ◽  
Penelope Leyland
Keyword(s):  
Monte Carlo ◽  
Evolutionary Algorithm ◽  
Shape Design ◽  
Multilevel Monte Carlo ◽  
Aerodynamic Shape

scholarly journals A Study on Many-Objective Optimization Using the Kriging-Surrogate-Based Evolutionary Algorithm Maximizing Expected Hypervolume Improvement

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Chang Luo ◽  
Koji Shimoyama ◽  
Shigeru Obayashi
Keyword(s):  
Monte Carlo ◽  
Evolutionary Algorithm ◽  
Numerical Experiments ◽  
Test Problem ◽  
Kriging Model ◽  
Monte Carlo Sampling ◽  
The Other ◽  
Expected Improvement ◽  
Design Variables ◽  
The Many

The many-objective optimization performance of the Kriging-surrogate-based evolutionary algorithm (EA), which maximizes expected hypervolume improvement (EHVI) for updating the Kriging model, is investigated and compared with those using expected improvement (EI) and estimation (EST) updating criteria in this paper. Numerical experiments are conducted in 3- to 15-objective DTLZ1-7 problems. In the experiments, an exact hypervolume calculating algorithm is used for the problems with less than six objectives. On the other hand, an approximate hypervolume calculating algorithm based on Monte Carlo sampling is adopted for the problems with more objectives. The results indicate that, in the nonconstrained case, EHVI is a highly competitive updating criterion for the Kriging model and EA based many-objective optimization, especially when the test problem is complex and the number of objectives or design variables is large.

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