Constraint genetic algorithm and its application in sintering proportioning

2017 2nd International Seminar on Advances in Materials Science and Engineering

Wu, Tiebin^1 ; Liu, Yunlian^1 ; Tang, Wenyan^2 ; Li, Xinjun^1 ; Yu, Yi^1

Hunan University of Humanities, Science and Technology, Loudi Hunan

41700, China^1

Hunan University of Technology, Zhuzhou Hunan

412007, China^2

关键词: Constrained optimi-zation problems;    Cross-over probability;    Initial population;    ITS applications;    Learning strategy;    Pre-mature convergences;    Solution space;    Speed of convergence;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/231/1/012022/pdf
DOI  :  10.1088/1757-899X/231/1/012022

来源: IOP

【 摘 要 】

This paper puts forward a method for constrained optimization problems based on self-adaptive penalty function and improved genetic algorithm. In order to improve the speed of convergence and avoid premature convergence, a method based on good-point set theory has been proposed. By using good point set method for generating initial population, the initial population is uniformly distributed in the solution space. This paper Designs an elite reverse learning strategy, and proposes a mechanism to automatically adjust the crossover probability according to the individual advantages and disadvantages. The tests indicate that the proposed constrained genetic algorithm is efficient and feasible.