scholarly journals Preface to Special Issue on Design Sensitivity and Optimization of Large-scale Problems

2003 ◽  
Vol 57 (10) ◽  
pp. 1339-1339
Author(s):  
Michael J. Saran ◽  
Michal Kleiber
Keyword(s):  
Large Scale ◽  
Design Sensitivity ◽  
Special Issue ◽  
Large Scale Problems

scholarly journals Guest Editorial: Special Issue on Large-Scale Wireless-Powered Networks With Backscatter Communications

2020 ◽  
Vol 1 ◽  
pp. 1961-1964
Author(s):  
Sami Muhaidat ◽  
Paschalis C. Sofotasios ◽  
Kaibin Huang ◽  
Muhammad Ali Imran ◽  
Zhiguo Ding ◽  
...  
Keyword(s):  
Large Scale ◽  
Guest Editorial ◽  
Special Issue ◽  
Editorial Special Issue

scholarly journals Investigation of Improved Cooperative Coevolution for Large-Scale Global Optimization Problems

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 146
Author(s):  
Aleksei Vakhnin ◽  
Evgenii Sopov
Keyword(s):  
Global Optimization ◽  
Evolutionary Algorithms ◽  
Numerical Experiments ◽  
Large Scale ◽  
Optimization Problems ◽  
State Of The Art ◽  
Fixed Number ◽  
High Dimensional ◽  
Cooperative Coevolution ◽  
Large Scale Problems

Modern real-valued optimization problems are complex and high-dimensional, and they are known as “large-scale global optimization (LSGO)” problems. Classic evolutionary algorithms (EAs) perform poorly on this class of problems because of the curse of dimensionality. Cooperative Coevolution (CC) is a high-performed framework for performing the decomposition of large-scale problems into smaller and easier subproblems by grouping objective variables. The efficiency of CC strongly depends on the size of groups and the grouping approach. In this study, an improved CC (iCC) approach for solving LSGO problems has been proposed and investigated. iCC changes the number of variables in subcomponents dynamically during the optimization process. The SHADE algorithm is used as a subcomponent optimizer. We have investigated the performance of iCC-SHADE and CC-SHADE on fifteen problems from the LSGO CEC’13 benchmark set provided by the IEEE Congress of Evolutionary Computation. The results of numerical experiments have shown that iCC-SHADE outperforms, on average, CC-SHADE with a fixed number of subcomponents. Also, we have compared iCC-SHADE with some state-of-the-art LSGO metaheuristics. The experimental results have shown that the proposed algorithm is competitive with other efficient metaheuristics.

Fast Multipole Virtual Boundary Element - Least Square Method for Solving 2-D Elastic Problems

2011 ◽  
Vol 204-210 ◽  
pp. 2196-2201
Author(s):  
Yan Tao Jiang ◽  
Si Tian Chen ◽  
Cheng Hua Li
Keyword(s):  
Boundary Element ◽  
Large Scale ◽  
Fast Multipole Method ◽  
Main Idea ◽  
Least Square Method ◽  
Least Square ◽  
Fast Multipole ◽  
Large Scale Problems ◽  
Virtual Boundary ◽  
Virtual Boundary Element

In this paper, the fast multipole virtual boundary element - least square method (Fast Multipole VBE - LSM) is proposed and used to simulate 2-D elastic problems, which is based on the fast multipole method (FMM) and virtual boundary element - least square method (VBE - LSM).The main idea of the method is to change computational model by applying the FMM to conventional VBE - LSM. The memory and operations could be reduced to be of linear proportion to the degree of freedom (DOF) and large scale problems could be effectively solved on a common desktop with this method. Numerical results show that this method holds virtues of high feasibility, accuracy and efficiency. Moreover, the idea of this method can be generalized and extended in application.

Hierarchy theory as a guide to mycorrhizal research on large-scale problems

1991 ◽  
Vol 73 (3-4) ◽  
pp. 271-284 ◽  
Author(s):  
E.G. O'Neill ◽  
R.V. O'Neill ◽  
R.J. Norby
Keyword(s):  
Large Scale ◽  
Hierarchy Theory ◽  
Large Scale Problems
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