Parameter Optimization Methods Based on Computational Intelligence Techniques in Context of Sustainable Computing

2017 ◽  
pp. 101-113
Author(s):  
Pankaj Upadhyay ◽  
Jitender Kumar Chhabra
Keyword(s):  
Parameter Optimization ◽  
Computational Intelligence ◽  
Optimization Methods

scholarly journals Exploration of Semantic Geo-Object Recognition Based on the Scale Parameter Optimization Method for Remote Sensing Images

2021 ◽  
Vol 10 (6) ◽  
pp. 420
Author(s):  
Jun Wang ◽  
Lili Jiang ◽  
Qingwen Qi ◽  
Yongji Wang
Keyword(s):  
Parameter Optimization ◽  
Scale Parameter ◽  
Human Perception ◽  
Optimization Methods ◽  
Optimization Method ◽  
Initiation Point ◽  
Scale Parameters ◽  
Object Based ◽  
Geographic Object ◽  
Multiresolution Segmentation

Image segmentation is of significance because it can provide objects that are the minimum analysis units for geographic object-based image analysis (GEOBIA). Most segmentation methods usually set parameters to identify geo-objects, and different parameter settings lead to different segmentation results; thus, parameter optimization is critical to obtain satisfactory segmentation results. Currently, many parameter optimization methods have been developed and successfully applied to the identification of single geo-objects. However, few studies have focused on the recognition of the union of different types of geo-objects (semantic geo-objects), such as a park. The recognition of semantic geo-objects is likely more crucial than that of single geo-objects because the former type of recognition is more correlated with the human perception. This paper proposes an approach to recognize semantic geo-objects. The key concept is that a single geo-object is the smallest component unit of a semantic geo-object, and semantic geo-objects are recognized by iteratively merging single geo-objects. Thus, the optimal scale of the semantic geo-objects is determined by iteratively recognizing the optimal scales of single geo-objects and using them as the initiation point of the reset scale parameter optimization interval. In this paper, we adopt the multiresolution segmentation (MRS) method to segment Gaofen-1 images and tested three scale parameter optimization methods to validate the proposed approach. The results show that the proposed approach can determine the scale parameters, which can produce semantic geo-objects.

Parameter optimization methods for estimating dynamic origin-destination trip-tables

1997 ◽  
Vol 31 (2) ◽  
pp. 141-157 ◽  
Author(s):  
Hanif D. Sherali ◽  
Namita Arora ◽  
Antoine G. Hobeika
Keyword(s):  
Parameter Optimization ◽  
Optimization Methods

scholarly journals Comparison of parameter optimization methods for quantitative susceptibility mapping

2020 ◽  
Vol 85 (1) ◽  
pp. 480-494 ◽  
Author(s):  
Carlos Milovic ◽  
Claudia Prieto ◽  
Berkin Bilgic ◽  
Sergio Uribe ◽  
Julio Acosta‐Cabronero ◽  
...  
Keyword(s):  
Parameter Optimization ◽  
Optimization Methods ◽  
Susceptibility Mapping ◽  
Quantitative Susceptibility Mapping

scholarly journals Computational Intelligence-Based Optimization Methods for Power Quality and Dynamic Response Enhancement of ac Microgrids

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4063 ◽  
Author(s):  
Touqeer Ahmed Jumani ◽  
Mohd Wazir Mustafa ◽  
Nawaf N. Hamadneh ◽  
Samer H. Atawneh ◽  
Madihah Md. Rasid ◽  
...  
Keyword(s):  
Dynamic Response ◽  
Power Quality ◽  
Computational Intelligence ◽  
Research Work ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Operating Conditions ◽  
Inference System ◽  
Study Objective ◽  
Comprehensive Review

The penetration of distributed generators (DGs) in the existing power system has brought some real challenges regarding the power quality and dynamic response of the power systems. To overcome the above-mentioned issues, the researchers around the world have tried and tested different control methods among which the computational intelligence (CI) based methods have been found as most effective in mitigating the power quality and transient response problems intuitively. The significance of the mentioned optimization approaches in contemporary ac Microgrid (MG) controls can be observed from the increasing number of published articles and book chapters in the recent past. However, literature related to this important subject is scattered with no comprehensive review that provides detailed insight information on this substantial development. As such, this research work provides a detailed overview of four of the most extensively used CI-based optimization techniques, namely, artificial neural network (ANN), fuzzy logic (FL), adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA) as applied in ac MG controls from 42 research articles along with their basic working mechanism, merits, and limitations. Due to space and scope constraints, this study excludes the applications of swarm intelligence-based optimization methods in the studied field of research. Each of the mentioned CI algorithms is explored for three major MG control applications i.e., reactive power compensation and power quality, MPPT and MG’s voltage, frequency, and power regulation. In addition, this work provides a classification of the mentioned CI-based optimization studies based on various categories such as key study objective, optimization method applied, DGs utilized, studied MG operating mode, and considered operating conditions in order to ease the searchability and selectivity of the articles for the readers. Hence, it is envisaged that this comprehensive review will provide a valuable one-stop source of knowledge to the researchers working in the field of CI-based ac MG control architectures.

Parameter optimization in modelling unsteady compound channel flows

1992 ◽  
Vol 19 (3) ◽  
pp. 441-446 ◽  
Author(s):  
Habib Abida ◽  
Ronald D. Townsend
Keyword(s):  
Parameter Optimization ◽  
Optimization Methods ◽  
Flood Plain ◽  
Flood Routing ◽  
Compound Channel ◽  
Flood Events ◽  
Channel Section ◽  
Simplex Methods ◽  
Boundary Roughness ◽  
Good Agreement

Optimization methods are used to estimate data for routing floods through open compound channels (main channels with flood plain zones). These data include the irregular channel section geometry and the varying boundary roughness. Differences between simulated and observed stages and discharges are minimized using three optimization algorithms: Powell's method, Rosenbrock's algorithm, and the Nelder and Meade simplex method. Powells' method performed poorly; however, both the Rosenbrock and simplex methods yielded good results. The estimated data using the Rosenbrock and simplex methods were used to route different flood events observed in a laboratory channel. Simulated peak stages and discharges were in good agreement with those estimated using actual routing data. Key words: compound channel, flood routing, lateral momentum transfer, optimization, unsteady flow.

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