A Novel Mutation and Crossover Operator for Multi-objective Differential Evolution

2018 ◽  
pp. 138-147
Author(s):  
Qingxia Li ◽  
Wenhong Wei
Keyword(s):  
Differential Evolution ◽  
Novel Mutation ◽  
Crossover Operator ◽  
Multi Objective

NARMAX Model Identification Using Multi-Objective Optimization Differential Evolution

2018 ◽  
Vol 10 (7) ◽  
Author(s):  
Mohd Zakimi Zakaria ◽  
◽  
Zakwan Mansor ◽  
Azuwir Mohd Nor ◽  
Mohd Sazli Saad ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Model Identification ◽  
Multi Objective Optimization ◽  
Multi Objective

PERTURBATION PARAMETERS TUNING OF MULTI-OBJECTIVE OPTIMIZATION DIFFERENTIAL EVOLUTION AND ITS APPLICATION TO DYNAMIC SYSTEM MODELING

2015 ◽  
Vol 75 (11) ◽  
Author(s):  
Mohd Zakimi Zakaria ◽  
Hishamuddin Jamaluddin ◽  
Robiah Ahmad ◽  
Azmi Harun ◽  
Radhwan Hussin ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Dynamic System ◽  
Dynamic Systems ◽  
System Modeling ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Structure Selection ◽  
Dynamic System Modeling ◽  
Perturbation Parameters ◽  
Model Structure Selection

This paper presents perturbation parameters for tuning of multi-objective optimization differential evolution and its application to dynamic system modeling. The perturbation of the proposed algorithm was composed of crossover and mutation operators.  Initially, a set of parameter values was tuned vigorously by executing multiple runs of algorithm for each proposed parameter variation. A set of values for crossover and mutation rates were proposed in executing the algorithm for model structure selection in dynamic system modeling. The model structure selection was one of the procedures in the system identification technique. Most researchers focused on the problem in selecting the parsimony model as the best represented the dynamic systems. Therefore, this problem needed two objective functions to overcome it, i.e. minimum predictive error and model complexity.  One of the main problems in identification of dynamic systems is to select the minimal model from the huge possible models that need to be considered. Hence, the important concepts in selecting good and adequate model used in the proposed algorithm were elaborated, including the implementation of the algorithm for modeling dynamic systems. Besides, the results showed that multi-objective optimization differential evolution performed better with tuned perturbation parameters.

An easy‐to‐implement differential evolution approach for multi‐objective optimizations

2006 ◽  
Vol 23 (2) ◽  
pp. 124-138 ◽  
Author(s):  
Hui‐Yuan Fan ◽  
Jouni Lampinen ◽  
Yeshayahou Levy
Keyword(s):  
Differential Evolution ◽  
Multi Objective ◽  
Evolution Approach

Differential Evolution Multi-Objective for Optimization of Isoflux Antenna Arrays

2015 ◽  
Vol 33 (2) ◽  
pp. 105-114 ◽  
Author(s):  
Miguel Ibarra ◽  
Marco A. Panduro ◽  
Angel G. Andrade
Keyword(s):  
Differential Evolution ◽  
Antenna Arrays ◽  
Multi Objective

scholarly journals Multi-Objective On-Line Optimization Approach for the DC Motor Controller Tuning Using Differential Evolution

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 20393-20407 ◽  
Author(s):  
Miguel G. Villarreal-Cervantes ◽  
Alejandro Rodriguez-Molina ◽  
Consuelo-Varinia Garcia-Mendoza ◽  
Ollin Penaloza-Mejia ◽  
Gabriel Sepulveda-Cervantes
Keyword(s):  
Differential Evolution ◽  
Dc Motor ◽  
Optimization Approach ◽  
Controller Tuning ◽  
Multi Objective ◽  
Motor Controller ◽  
On Line
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