Image Center Layout Optimization Method Based on Improved Genetic Algorithm

doi:10.16182/j.issn1004731x.joss.20-1043

Abstract

Abstract:

Aiming at the layout optimization methods of image center being influenced by the subjective factors and low level of automation, a method of combining systematic layout planning(SLP) with the improved genetic algorithm is proposed. The layout scheme generated by SLP improves the initial population of the genetic algorithm and increases the diversity of the initial population. In order to improve the efficiency of optimization, the improved algorithm updates the crossover probability and mutation probability adaptively according to the evolution stages and the fitness value of the individuals. On the basis of the layout area model and multi-objective optimization mathematical model established for an image center in Xi'an, the improved genetic algorithm is used to in the simulation. The experimental results show that the improved algorithm is faster and more effective than the traditional genetic algorithm or ant colony algorithm. The method can also improve the automation level of image center layout optimization and provide a reasonable reference scheme for the architectural designers.

Key words: image center, layout optimization, systematic layout planning, genetic algorithm, adaptive adjustment

CLC Number:

TP391.9

Zhijie Li, Haoqi Shi, Changhua Li, Jie Zhang. Image Center Layout Optimization Method Based on Improved Genetic Algorithm[J]. Journal of System Simulation, 2022, 34(6): 1173-1184.

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等级	系数	描述
1	1.00	流通强度非常强
2	0.75	流通强度比较强
3	0.50	流通强度一般
4	0.25	流通强度比较弱
5	0	基本无流通需求

等级	系数	描述
1	1.00	邻接强度非常强
2	0.75	邻接强度比较强
3	0.50	邻接强度一般
4	0.25	邻接强度比较弱
5	0	基本无邻接需求

编号	科室	编号	科室
1	肠胃机室	9	远程会诊室
2	MRI室	10	阅片室
3	预留机房室	11	医生值班室
4	DR1室	12	取片处
5	DR2室	13	骨密度仪室
6	医生办公室	14	乳腺钼靶室
7	增强注射室	15	CT室
8	医生更衣室	16	双源CT室

方案	方案序列
Ⅰ	4、14、6、9、5、1、10、11、8、7、15、16、13、12、2、3
Ⅱ	6、10、9、11、8、4、5、1、14、15、16、13、12、7、2、3

算法	平均收敛代数	平均收敛值
遗传算法+优化初始种群	56	111.563 1
遗传算法+自适应机制	42	111.292 2
蚁群算法	104	111.982 1
遗传算法	75	112.321 1
改进后的遗传算法	22	111.142 3