scholarly journals Sustainable Spatial Land Use Optimization through Non-Dominated Sorting Genetic Algorithm-II (NSGA-II): (Case Study: Baboldasht District of Isfahan)

2015 ◽  
Vol 8 (S3) ◽  
pp. 118 ◽  
Author(s):  
Mahmoud Mohammadi ◽  
Mahin Nastaran ◽  
Alireza Sahebgharani
Keyword(s):  
Genetic Algorithm ◽  
Land Use ◽  
Nsga Ii ◽  
Land Use Optimization

scholarly journals Spatial Multi-Objective Land Use Optimization toward Livability Based on Boundary-Based Genetic Algorithm: A Case Study in Singapore

2020 ◽  
Vol 9 (1) ◽  
pp. 40 ◽  
Author(s):  
Kai Cao ◽  
Muyang Liu ◽  
Shu Wang ◽  
Mengqi Liu ◽  
Wenting Zhang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Land Use ◽  
Central Region ◽  
Optimization Model ◽  
Optimization Problem ◽  
Weighted Sum ◽  
Multi Objective ◽  
Land Use Optimization ◽  
Future Direction

In this research, the concept of livability has been quantitatively and comprehensively reviewed and interpreted to contribute to spatial multi-objective land use optimization modelling. In addition, a multi-objective land use optimization model was constructed using goal programming and a weighted-sum approach, followed by a boundary-based genetic algorithm adapted to help address the spatial multi-objective land use optimization problem. Furthermore, the model is successfully and effectively applied to the case study in the Central Region of Queenstown Planning Area of Singapore towards livability. In the case study, the experiments based on equal weights and experiments based on different weights combination have been successfully conducted, which can demonstrate the effectiveness of the spatial multi-objective land use optimization model developed in this research as well as the robustness and reliability of computer-generated solutions. In addition, the comparison between the computer-generated solutions and the two real planned scenarios has also clearly demonstrated that our generated solutions are much better in terms of fitness values. Lastly, the limitation and future direction of this research have been discussed.

Quantifying water provision service supply, demand and spatial flow for land use optimization: A case study in the YanHe watershed

2020 ◽  
Vol 43 ◽  
pp. 101117 ◽  
Author(s):  
Dengshuai Chen ◽  
Jing Li ◽  
Xiaonan Yang ◽  
Zixiang Zhou ◽  
Yuqi Pan ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Optimization ◽  
Water Provision

scholarly journals The Application of Genetic Algorithm in Land Use Optimization Research: A Review

Land ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 526
Author(s):  
Xiaoe Ding ◽  
Minrui Zheng ◽  
Xinqi Zheng
Keyword(s):  
Genetic Algorithm ◽  
Land Use ◽  
Search Algorithm ◽  
Sustainable Use ◽  
The United States ◽  
Future Research ◽  
Research Institution ◽  
Land Use Optimization ◽  
Optimization Stage ◽  
Optimization Search

Land use optimization (LUO) first considers which types of land use should exist in a certain area, and secondly, how to allocate these land use types to specific land grid units. As an intelligent global optimization search algorithm, the Genetic Algorithm (GA) has been widely used in this field. However, there are no comprehensive reviews concerning the development process for the application of the Genetic Algorithm in land use optimization (GA-LUO). This article used a bibliometric analysis method to explore current state and development trends for GA-LUO from 1154 relevant documents published over the past 25 years from Web of Science. We also displayed a visualization network from the aspects of core authors, research institutions, and highly cited literature. The results show the following: (1) The countries that published the most articles are the United States and China, and the Chinese Academy of Sciences is the research institution that publishes the most articles. (2) The top 10 cited articles focused on describing how to build GA models for multi-objective LUO. (3) According to the number of keywords that appear for the first time in each time period, we divided the process of GA-LUO into four stages: the presentation and improvement of methods stage (1995–2004), the optimization stage (2005–2008), the hybrid application of multiple models stage (2009–2016), and the introduction of the latest method stage (after 2017). Furthermore, future research trends are mainly manifested in integrating together algorithms with GA and deepening existing research results. This review could help researchers know this research domain well and provide effective solutions for land use problems to ensure the sustainable use of land resources.

Parameter Based Multi-Objective Optimization of Video CODECs

2011 ◽  
pp. 288-308
Author(s):  
F. Al-Abri ◽  
E.A. Edirisinghe ◽  
C. Grecos
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Formulation ◽  
Video Codec ◽  
Video Content ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Video Codecs ◽  
Multi Objective ◽  
On Demand

This chapter presents a generalised framework for multi-objective optimisation of video CODECs for use in off-line, on-demand applications. In particular, an optimization scheme is proposed to determine the optimum coding parameters for a H.264 AVC video codec in a memory and bandwidth constrained environment, which minimises codec complexity and video distortion. The encoding/decoding parameters that have a significant impact on the performance of the codec are initially obtained through experimental analysis. A mathematical formulation by means of regression is subsequently used to associate these parameters with the relevant objectives and define a Multi-Objective Optimization (MOO) problem. Solutions to the optimization problem are reached through a Non-dominated Sorting Genetic Algorithm (NSGA-II). It is shown that the proposed framework is flexible on the number of objectives that can jointly be optimized. Furthermore, any of the objectives can be included as constraints depending on the requirements of the services to be supported. Practical use of the proposed framework is described using a case study that involves video content transmission to a mobile hand.

Multi-Objective Optimization of Manufacturing Processes Using Evolutionary Algorithms

2012 ◽  
pp. 44-66
Author(s):  
M. Kanthababu
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Multiple Objectives ◽  
Manufacturing Processes ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Local Optima ◽  
Multi Objective ◽  
Research Areas

Recently evolutionary algorithms have created more interest among researchers and manufacturing engineers for solving multiple-objective problems. The objective of this chapter is to give readers a comprehensive understanding and also to give a better insight into the applications of solving multi-objective problems using evolutionary algorithms for manufacturing processes. The most important feature of evolutionary algorithms is that it can successfully find globally optimal solutions without getting restricted to local optima. This chapter introduces the reader with the basic concepts of single-objective optimization, multi-objective optimization, as well as evolutionary algorithms, and also gives an overview of its salient features. Some of the evolutionary algorithms widely used by researchers for solving multiple objectives have been presented and compared. Among the evolutionary algorithms, the Non-dominated Sorting Genetic Algorithm (NSGA) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) have emerged as most efficient algorithms for solving multi-objective problems in manufacturing processes. The NSGA method applied to a complex manufacturing process, namely plateau honing process, considering multiple objectives, has been detailed with a case study. The chapter concludes by suggesting implementation of evolutionary algorithms in different research areas which hold promise for future applications.

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