Constrained Optimization Solution Based on an Improved Genetic Algorithm

摘要：

　　A hybrid adaptive genetic algorithm is proposed for solving constrained optimization problems.The algorithm combines adaptive penalty method and smoothing technique in order to get no parameter tuning and easily escaping from the local optimal solutions.Meanwhile, local line search technique is introduced and a new crossover operator is designed for getting much faster convergence.The performance of the algorithm is tested on thirteen benchmark functions and the results indicate that the proposed algorithm is robust and effective.

关键词： constrained optimization genetic algorithm penalty function local line search

作者: Chunyan Li Guanghui Zeng

作者单位: Logistics Department,Hunan Universtiy of Science and Engineering,Yongzhou 425100,China Department of Electronic and Information Engineering,Guangzhou City Construction College,Conghua 510

会议类型: 国际会议

会议名称: the 3nd International Conference on Digital Manufacturing & Automation (第三届数字制造与自动化国际会议(ICDMA 2012))

会议地点: 桂林

会议语种:英文

页码: 334-337

在线出版日期: 2012-08-01（万方平台首次上网日期，不代表论文的发表时间）