A Sustainable City Planning Algorithm Based on TLBO and Local Search

2017 3rd International Conference on Applied Materials and Manufacturing Technology

Zhang, Ke^1 ; Lin, Li^1 ; Huang, Xuanxuan^1 ; Liu, Yiming^1 ; Zhang, Yonggang^1,2

College of Computer Science and Technology, Jilin University, Changchun

130012, China^1

Key Laboratory of Symbol Computation and Knowledge Engineering, Jilin University, Ministry of Education, Changchun

130012, China^2

关键词: Constraint optimization problems;    Design and implements;    Evaluation modeling;    Planning algorithms;    Social development;    Sustainable cities;    Swarm intelligence algorithms;    Teaching-learning-based optimizations;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/242/1/012120/pdf
DOI  :  10.1088/1757-899X/242/1/012120

来源: IOP

【 摘 要 】

Nowadays, how to design a city with more sustainable features has become a center problem in the field of social development, meanwhile it has provided a broad stage for the application of artificial intelligence theories and methods. Because the design of sustainable city is essentially a constraint optimization problem, the swarm intelligence algorithm of extensive research has become a natural candidate for solving the problem. TLBO (Teaching-Learning-Based Optimization) algorithm is a new swarm intelligence algorithm. Its inspiration comes from the "teaching" and "learning" behavior of teaching class in the life. The evolution of the population is realized by simulating the "teaching" of the teacher and the student "learning" from each other, with features of less parameters, efficient, simple thinking, easy to achieve and so on. It has been successfully applied to scheduling, planning, configuration and other fields, which achieved a good effect and has been paid more and more attention by artificial intelligence researchers. Based on the classical TLBO algorithm, we propose a TLBO-LS algorithm combined with local search. We design and implement the random generation algorithm and evaluation model of urban planning problem. The experiments on the small and medium-sized random generation problem showed that our proposed algorithm has obvious advantages over DE algorithm and classical TLBO algorithm in terms of convergence speed and solution quality.