scholarly journals Decomposition Multi-Objective Evolutionary Algorithm Based on Adaptive Neighborhood Adjustment Strategy

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 78639-78651
Author(s):  
Liping Wang ◽  
Mengna Xu ◽  
Wei Yu ◽  
Qicang Qiu ◽  
Feng Wu
Keyword(s):  
Evolutionary Algorithm ◽  
Multi Objective ◽  
Adjustment Strategy ◽  
Adaptive Neighborhood

scholarly journals Using the Decomposition-Based Multi-Objective Evolutionary Algorithm with Adaptive Neighborhood Sizes and Dynamic Constraint Strategies to Retrieve Atmospheric Ducts

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2230
Author(s):  
Yanbo Mai ◽  
Hanqing Shi ◽  
Qixiang Liao ◽  
Zheng Sheng ◽  
Shuai Zhao ◽  
...  
Keyword(s):  
Evolutionary Algorithm ◽  
Dynamic Change ◽  
Optimal Solution ◽  
Satellite System ◽  
Propagation Loss ◽  
Observation Data ◽  
Multi Objective ◽  
Adaptive Neighborhood ◽  
Global Navigation Satellite ◽  
Atmospheric Ducts

The traditional method of retrieving atmospheric ducts is to use the special sensor of weather balloons or rocket soundings to obtain information intelligently, and it is very expensive. Today, with the development of technology, it is very convenient to retrieve the atmospheric ducts from Global Navigation Satellite System (GNSS) phase delay and propagation loss observation data, and then the GNSS receiver on the ground forms an automatic receiving sensor. This paper proposes a hybrid decomposition-based multi-objective evolutionary algorithm with adaptive neighborhood sizes (EN-MOEA/ACD-NS), which dynamically imposes some constraints on the objectives. The decomposition-based multi-objective evolutionary algorithm (MOEA/D) updates the solutions through neighboring objectives, the number of which affects the quality of the optimal solution. Properly constraining the optimization objectives can effectively balance the diversity and convergence of the population. The experimental results from the Congress on Evolutionary Computation (CEC) 2009 on test instances with hypervolume (HV), inverted generational distance (IGD), and average Hausdorff distance ∆2 metrics show that the new method performs similarly to the evolutionary algorithm MOEA/ACD-NS, which considers only the dynamic change of the neighborhood sizes. The improved algorithm is applied to the practical problem of jointly retrieving atmospheric ducts with GNSS signals, and its performance further demonstrates its feasibility and practicability.

Multi-objective evolutionary algorithm based on neighborhood

2008 ◽  
Vol 28 (6) ◽  
pp. 1570-1574
Author(s):  
Mi-qing LI ◽  
Jin-hua ZHENG ◽  
Biao LUO ◽  
Jun WU ◽  
Shi-hua WEN
Keyword(s):  
Evolutionary Algorithm ◽  
Multi Objective

scholarly journals A Multi Objective Evolutionary Algorithm for the Parameters Extraction of Organic Thin Film Transistors Models

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 939
Author(s):  
Rosario Schiano Lo Moriello ◽  
Davide Ruggiero ◽  
Leopoldo Angrisani ◽  
Enzo Caputo ◽  
Francesco de Pandi ◽  
...  
Keyword(s):  
Thin Film ◽  
Evolutionary Algorithm ◽  
Design Stage ◽  
Model Parameters ◽  
Organic Transistors ◽  
Organic Thin Film ◽  
Multi Objective ◽  
Application Fields ◽  
Channel Lengths ◽  
P Type

Thanks to their peculiar features, organic transistors are proving to be a valuable alternative to traditional semiconducting devices in several application fields; however, before releasing their exploitation, simulating their behaviour through adequate circuital models could be advisable during the design stage of electronic circuits and/or boards. Consequently, accurately extracting the parameter value of those models is fundamental to developing useful libraries for hardware design environments. To face the considered problem, the authors present a method based on successive application of Single- and Multi-Objective Evolutionary Algorithm for the optimal tuning of model parameters of organic transistors on thin film (OTFT). In particular, parameters are first roughly estimated to assure the best fit with the experimental transfer characteristics; the estimates are successively refined through the multi-objective strategy by also matching the values of the experimental mobility. The performance of the method has been assessed by estimating the parameters value of both P-type and N-type OTFTs characterized by different values of channel lengths; the obtained results evidence that the proposed method can obtain suitable parameters values for all the considered channel lengths.

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