摘要
交互式蚁群优化(interactive ant colony optimization,i ACO)是一种利用人来评价解的优劣而进行系统优化的技术,可以求解性能指标不能或者难以数量化的优化问题。分析了交互式蚁群优化模型面临的研究困难。针对Tanabe等人提出的交互式蚂蚁算法性能不足的问题,提出利用全局历史最优解进行信息素的更新,并将信息素限定在一定区间内的改进交互式蚁群优化算法,从人机交互角度讨论了解的构造方法和人的评价策略。最后,利用函数优化和汽车造型设计进行了实验,运行结果表明算法具有较高优化性能。
Interactive ant colony optimization (iACO) is a technique that optimizes target systems based on human evaluation. It can be used to solve the systems whose optimization indices are unable or difficult to be quantificated.Firstly, this paper analyzes the difficulties faced by iACO model. Nextly, aiming at the low performance of interactive ant system that is put forward by Tanabe et al., this paper proposes an improved iACO algorithm. The pheromone in the proposed model is updated with the best ant and limited to a certain range. The method of constructing solutions and evaluating strategies from the perspective of human-computer interaction are discussed. Finally, the results of the experiments of function optimization and car styling draft design show that the proposed algorithm has higher optimization performance.
作者
黄永青
杨善林
梁昌勇
HUANG Yongqing;YANG Shanlin;LIANG Changyong(School of Management, Hefei University of Technology, Hefei 230009, China;Institute of Information Technology and Engineering Management, Tongling University, Tongling,Anhui 244000, China)
出处
《计算机科学与探索》
CSCD
北大核心
2016年第12期1720-1728,共9页
Journal of Frontiers of Computer Science and Technology
基金
国家自然科学基金Nos.71271072
71331002
中国博士后科学基金No.2014M560508
中央高校基本科研业务费专项资金No.2013HGBH0029
高等学校博士学科点专项科研基金No.20110111110006
安徽省自然科学基金No.1208085MG121
安徽省教育厅重点项目Nos.KJ2012A269
SK2015A537
铜陵学院科研项目No.2014tlxyxs31~~
关键词
交互式蚁群优化
蚁群优化
人机交互
汽车造型
interactive ant colony optimization
ant colony optimization
human-computer interaction
car styling