Gaussian adaptation based parameter adaptation for differential evolution

2014 ◽  
Author(s):  
R. Mallipeddi ◽  
Guohua Wu ◽  
Minho Lee ◽  
P. N. Suganthan
Keyword(s):  
Differential Evolution ◽  
Parameter Adaptation ◽  
Gaussian Adaptation

scholarly journals Differential Evolution with Population and Strategy Parameter Adaptation

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
V. Gonuguntla ◽  
R. Mallipeddi ◽  
Kalyana C. Veluvolu
Keyword(s):  
Differential Evolution ◽  
Population Size ◽  
Optimization Problems ◽  
Search Space ◽  
Benchmark Problems ◽  
Parameter Adaptation ◽  
De Algorithm ◽  
Probabilistic Technique ◽  
Number Of Individuals ◽  
Strategy Parameters

Differential evolution (DE) is simple and effective in solving numerous real-world global optimization problems. However, its effectiveness critically depends on the appropriate setting of population size and strategy parameters. Therefore, to obtain optimal performance the time-consuming preliminary tuning of parameters is needed. Recently, different strategy parameter adaptation techniques, which can automatically update the parameters to appropriate values to suit the characteristics of optimization problems, have been proposed. However, most of the works do not control the adaptation of the population size. In addition, they try to adapt each strategy parameters individually but do not take into account the interaction between the parameters that are being adapted. In this paper, we introduce a DE algorithm where both strategy parameters are self-adapted taking into account the parameter dependencies by means of a multivariate probabilistic technique based on Gaussian Adaptation working on the parameter space. In addition, the proposed DE algorithm starts by sampling a huge number of sample solutions in the search space and in each generation a constant number of individuals from huge sample set are adaptively selected to form the population that evolves. The proposed algorithm is evaluated on 14 benchmark problems of CEC 2005 with different dimensionality.

Distance based parameter adaptation for Success-History based Differential Evolution

2019 ◽  
Vol 50 ◽  
pp. 100462 ◽  
Author(s):  
Adam Viktorin ◽  
Roman Senkerik ◽  
Michal Pluhacek ◽  
Tomas Kadavy ◽  
Ales Zamuda
Keyword(s):  
Differential Evolution ◽  
Parameter Adaptation

scholarly journals An Improved Multioperator-Based Constrained Differential Evolution for Optimal Power Allocation in WSNs

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6271
Author(s):  
Wei Li ◽  
Wenyin Gong
Keyword(s):  
Differential Evolution ◽  
Power Allocation ◽  
Sensor Nodes ◽  
Optimal Power Allocation ◽  
Parameter Adaptation ◽  
Selection Probability ◽  
Optimal Power ◽  
Adaptive Differential Evolution ◽  
The Status ◽  
Efficient Alternative

Optimal power allocation (OPA), which can be transformed into an optimization problem with constraints, plays a key role in wireless sensor networks (WSNs). In this paper, inspired by ant colony optimization, an improved multioperator-based constrained adaptive differential evolution (namely, IMO-CADE) is proposed for the OPA. The proposed IMO-CADE can be featured as follows: (i) to adaptively select the proper operator among different operators, the feedback of operators and the status of individuals are considered simultaneously to assign the selection probability; (ii) the constrained reward assignment is used to measure the feedback of operators; (iii) the parameter adaptation is used for the parameters of differential evolution. To extensively evaluate the performance of IMO-CADE, it is used to solve the OPA for both the independent and correlated observations with different numbers of sensor nodes. Compared with other advanced methods, simulation results clearly indicate that IMO-CADE yields the best performance on the whole. Therefore, IMO-CADE can be an efficient alternative for the OPA of WSNs, especially for WSNs with a large number of sensor nodes.

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