• 人工智能 •

### 基于差分演化策略的混沌乌鸦算法求解折扣{0-1}背包问题

1. 河北地质大学 信息工程学院, 石家庄 050031
• 收稿日期:2017-06-13 修回日期:2017-08-31 出版日期:2018-01-10 发布日期:2018-01-22
• 通讯作者: 刘雪静
• 作者简介:刘雪静(1980-),女,河北定州人,讲师,硕士,CCF会员,主要研究方向:演化计算、机器学习;贺毅朝(1969-),男,河北晋州人,教授,硕士,CCF高级会员,主要研究方向:智能计算、计算复杂性理论;路凤佳(1980-),女,河北沧州人,讲师,硕士,主要研究方向:大数据、机器学习;吴聪聪(1975-),女,河北唐山人,讲师,硕士,主要研究方向:智能计算、信息检索、机器学习;才秀凤(1978-),女,河北丰润人,讲师,硕士,主要研究方向:智能计算、机器学习。
• 基金资助:
河北省高等学校科学研究计划项目（ZD2016005）；河北省自然科学基金资助项目（F2016403055）。

### Chaotic crow search algorithm based on differential evolution strategy for solving discount {0-1} knapsack problem

1. College of Information Engineering, Hebei GEO University, Shijiazhuang Hebei 050031, China
• Received:2017-06-13 Revised:2017-08-31 Online:2018-01-10 Published:2018-01-22
• Supported by:
This work is partially supported by Scientific Research Project Program of Colleges and Universities in Hebei Province (ZD2016005), the Natural Science Foundation of Hebei Province (F2016403055).

Abstract: In Discount {0-1} Knapsack Problem (D{0-1}KP), the weight coefficients and the value coefficients in a large range, are difficult to solve by deterministic algorithms. To solve this problem, a Chaotic Crow Search Algorithm based on Differential Evolution strategy (DECCSA) was proposed. Firstly, the initial crow population was generated by chaotic mapping. Secondly, mixed coding and Greedy Repair and Optimization Strategy (GROS) were used to solve the coding problem of D{0-1}KP. Finally, Difference Evolution (DE) strategy was introduced to improve the convergence rate of the algorithm. The experimental results on four large-scale D{0-1}KP instances show that DECCSA is better than Genetic Algorithm (GA), bacterial foraging optimization algorithm, and mutated bat algorithm, and it can get the optimal solution or approximate optimal solution. It's very suitable for solving D{0-1}KP.