Green Landscape Design Based on Niche Genetic Algorithm for E-Business Solutions

Green Landscape Design Based on Niche Genetic Algorithm for E-Business Solutions

Guoxing Chen, Ashutosh Sharma
Copyright: © 2022 |Pages: 11
DOI: 10.4018/IJeC.304446
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Abstract

In order to solve the unreasonable problems in green environmental protection design, this paper proposes a green building landscape space environment optimization design scheme based on niche genetic algorithm, which optimizes the building landscape size by multi-objective. The target of optimization includes the cost of building and public facilities such as green belt. The purpose of this article is to present an optimization design scheme using genetic algorithm that can optimize the size of landscape and cost reduction and providing facilities by processing the information. The results show that: People's satisfaction value of square and Pedestrian Street is 0.61 and 0.38, respectively. In conclusion it has been analyzed that the scientific planning and design of urban architectural landscape is of great significance to improve the urban appearance. It has been further concluded that the proposed approach enhances the quality of urban environment, development of urban economy and open to the outside world.
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1. Introduction

The urban landscape contains the cityscape and background integral to a region and has numerous characteristics. Local governments endorse urban landscape conventions and must reflect resident’s estimations during voluntary happenings in the improvement of urban landscape or planning of city. When the design of urban landscape assumed to be a problem of optimization, the optimal resolution may differ conditionally on the province and is subjective by the estimations and characteristics of the citizens.

The environmental protection of architectural landscape and the beauty of architectural space are the ideal goals for designers. In recent years, with the development of society, people's awareness of environmental protection is gradually enhanced. At the same time, the aesthetic of architectural art also has different ideas from the past. The direction of architectural landscape space optimization is gradually developing to three-dimensional greening. Three dimensional greening is to make the roof and wall greening of buildings coordinate with the landscape (Zhu, 2019). The traditional architectural space optimization design often has the problems of poor direct viewing effect and low residents' satisfaction, which is caused by the unreasonable distribution of building space. With the development of the city, architecture and landscape not only meet the basic needs of people's life, but also get more and more attention from the perspective of functionality and artistry.

In order to extend the performance of the optimization of landscape design, genetic algorithm is proposed recently. The aim of introducing concepts and algorithms into genetic algorithm is hypothetically to exploit the local distinctive information for evaluating the ways and means of discovering the optimal solution when dealing with difficult complications. From another aspect, it improves the quality of urban life and affects the basic factors of urban development, all of which constitute the main body of urban environment. It is a new subject for planning, architecture, landscape, technology and other fields to better serve people from macro and micro perspectives. Green building landscape design with certain urban characteristics has become a new trend and trend. Urban architectural landscape design, in a sense, represents the culture of the city and is the interpretation of the comprehensive development of the city. Scientific and reasonable planning and design of urban architectural landscape is of great significance to improve the urban appearance, enhance the quality of urban environment, and promote the development of urban economy and opening up to the outside world (Hong & Xi-Jing, 2019; Lee et al., 2019; Sun & Wang, 2018).

The rest of paper is organized as: Section 2 describes the recent work done by various researchers and their contributions to the literature. The methodology of the proposed genetic algorithm for the construction and optimization is discussed is Section 3. The experimentation part includes results and analysis of the genetic algorithm which is described in Section 4. At last the conclusion from the adaptive algorithm is presented in Section 5.

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