Self-adaptive Differential Evolution with Modified Multi-Trajectory Search for CEC’2010 Large Scale Optimization

2010 ◽  
pp. 1-10 ◽  
Author(s):  
Shi-Zheng Zhao ◽  
Ponnuthurai Nagaratnam Suganthan ◽  
Swagatam Das
Keyword(s):  
Differential Evolution ◽  
Large Scale ◽  
Large Scale Optimization ◽  
Adaptive Differential Evolution ◽  
Scale Optimization ◽  
Trajectory Search ◽  
Self Adaptive

Self-adaptive differential evolution with multi-trajectory search for large-scale optimization

2010 ◽  
Vol 15 (11) ◽  
pp. 2175-2185 ◽  
Author(s):  
Shi-Zheng Zhao ◽  
Ponnuthurai Nagaratnam Suganthan ◽  
Swagatam Das
Keyword(s):  
Differential Evolution ◽  
Large Scale ◽  
Large Scale Optimization ◽  
Adaptive Differential Evolution ◽  
Scale Optimization ◽  
Trajectory Search ◽  
Self Adaptive

scholarly journals Cooperative differential evolution framework with utility-based adaptive grouping for large-scale optimization

2019 ◽  
Vol 11 (3) ◽  
pp. 168781401983416
Author(s):  
Hongwei Ge ◽  
Liang Sun ◽  
Kai Zhang ◽  
Chunguo Wu
Keyword(s):  
Differential Evolution ◽  
Large Scale ◽  
Small Scale ◽  
Large Scale Optimization ◽  
Grouping Strategy ◽  
Adaptive Differential Evolution ◽  
Subproblem Optimization ◽  
Scale Optimization ◽  
Relation Matrix

Decomposing the large-scale problem into small-scale subproblems and optimizing them cooperatively are critical steps for solving large-scale optimization problem. This article proposes a cooperative differential evolution with utility-based adaptive grouping. The problem decomposition is adaptively executed by the two mechanisms of circular sliding controller and relation matrix, which consider the variable interactions on the basis of the short-term and long-term utilities, respectively. The circular sliding controller provides baselines for the subproblem optimizer. The size of the sliding window and the sliding speed in the controller are adjusted adaptively so that the variables with higher activeness can be optimized extensively. The relation matrix–based grouping strategy enables interacted variables to be grouped into the same subproblem with higher probabilities. The novelty is that decomposition is conducted as the optimization process without extra computational burden. For subproblem optimization, we use a self-adaptive differential evolution operator that adaptively adjusts the parameters to guide the search to the optimum solutions of the subproblems. Experiments on the benchmarks of CEC2008 and CEC2010, and practical problems show the effectiveness of the proposed algorithm.

Quantum differential evolution with cooperative coevolution framework and hybrid mutation strategy for large scale optimization

2021 ◽  
Vol 224 ◽  
pp. 107080
Author(s):  
Wu Deng ◽  
Shifan Shang ◽  
Xing Cai ◽  
Huimin Zhao ◽  
Yongquan Zhou ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Large Scale ◽  
Large Scale Optimization ◽  
Cooperative Coevolution ◽  
Mutation Strategy ◽  
Scale Optimization
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