Research of Spatial Layout Optimization based on Genetic Algorithm

This article proposes an evolutionary multiagent framework of the co-operative co-evolutionary multiobjective model (CCMO-EMAS), specifically for equipment layout optimization in engineering. In this framework, each agent is set in a multiobjective cooperative co-evolutionary mode along with the algorithms and corresponding settings. In each iteration, agents are executed in turn, and each agent optimizes a subpopulation from system decomposition. Additionally, the collaboration mechanism is addressed to build complete solutions and evaluate individuals in the co-operative co-evolutionary algorithm. Each subpopulation is optimized once, and the corresponding agent is evaluated based on the improvement of the system memory. Moreover, the agent team is also evolved through an elite genetic algorithm. Finally, the proposed CCMO-EMAS framework is verified in a multimodule satellite equipment layout problem.

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Spatial layout optimization design of multi-type LEDs lighting source based on photoelectrothermal coupling theory

Young Scientists Forum 2017 ◽

10.1117/12.2317713 ◽

2018 ◽

Author(s):

Lingyun Xue ◽

Qingguang Chen ◽

Huanle Rao ◽

Ping Xu ◽

Guang Li

Keyword(s):

Optimization Design ◽

Spatial Layout ◽

Layout Optimization ◽

Coupling Theory

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Wind farm layout optimization using self-informed genetic algorithm with information guided exploitation

Applied Energy ◽

10.1016/j.apenergy.2019.04.084 ◽

2019 ◽

Vol 248 ◽

pp. 429-445 ◽

Cited By ~ 17

Author(s):

Xinglong Ju ◽

Feng Liu

Keyword(s):

Genetic Algorithm ◽

Wind Farm ◽

Layout Optimization ◽

Wind Farm Layout ◽

Wind Farm Layout Optimization

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Wind farm layout optimization based on support vector regression guided genetic algorithm with consideration of participation among landowners

Energy Conversion and Management ◽

10.1016/j.enconman.2019.06.082 ◽

2019 ◽

Vol 196 ◽

pp. 1267-1281 ◽

Cited By ~ 5

Author(s):

Xinglong Ju ◽

Feng Liu ◽

Li Wang ◽

Wei-Jen Lee

Keyword(s):

Genetic Algorithm ◽

Support Vector Regression ◽

Wind Farm ◽

Layout Optimization ◽

Support Vector ◽

Wind Farm Layout ◽

Wind Farm Layout Optimization

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Development of a Composite Model for Simulating Landscape Pattern Optimization Allocation: A Case Study in the Longquanyi District of Chengdu City, Sichuan Province, China

Sustainability ◽

10.3390/su11092678 ◽

2019 ◽

Vol 11 (9) ◽

pp. 2678

Author(s):

Dinghua Ou ◽

Xingzhu Yao ◽

Jianguo Xia ◽

Xuesong Gao ◽

Changquan Wang ◽

...

Keyword(s):

Landscape Pattern ◽

Land Use Planning ◽

Spatial Layout ◽

Layout Optimization ◽

Sichuan Province ◽

Landscape Patterns ◽

Ecological Security ◽

Quantitative Structure ◽

Landscape Suitability ◽

Optimization Allocation

The simulation of landscape pattern optimization allocation (LPOA) to achieve ecological security is an important issue when constructing regional ecological security patterns. In this study, an LPOA model was developed by integrating a binary logistic regression model and a nonlinear programming model with a particle swarm optimization algorithm in order to consider the complexity of landscape pattern optimization in terms of the quantitative structure and spatial layout optimization, integrating the landscape suitability and factors that influence landscape patterns, and under constraints to maximize the economic, ecological, and comprehensive benefits of landscape patterns. The model was employed to simulate the LPOA in the Longquanyi District of Chengdu City, Sichuan Province, China. The model successfully obtained an appropriate combination of the landscape quantitative structure and spatial layout, as well as effectively integrating the landscape suitability and factors that influence the landscape pattern. Thus, the model addressed the problems of previous studies, such as neglecting the coupling between quantitative structure optimization and spatial layout optimization, ignoring the macrofactors that affect landscape patterns during optimization modeling, and initializing particles without considering the suitability of the landscape. Furthermore, we assessed and analyzed the accuracy and feasibility of the landscape pattern spatial layout optimization results, where the results showed that the overall accuracy of the optimization results was 84.98% with a Kappa coefficient of 0.7587, thereby indicating the good performance of the model. Moreover, the simulated optimization allocation scheme for the landscape pattern was consistent with the actual situation. Therefore, this model can provide support and a scientific basis for regional landscape pattern planning, land use planning, urban planning, and other related spatial planning processes.

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