.png){kind=icon}
返回
Artificial bee colony algorithm with a pure crossover operation for binary optimization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.cie.2020.107011.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Artificial bee colony (ABC) algorithm is one of famous swarm intelligence approaches for continuous optimization. With the help of a solution transformation technique, it can evolve in continuous space but consequences belong to binary space. On the basis of binary ABC, a novel artificial bee colony algorithm (nABC for short) is first proposed for better solving the uncapacitated facility location problem (UFLP). In nABC, a pure crossover operation is proposed to improve information sharing quality and remove random perturbation of original search strategy in employed bees phase. Next, a new frequency of perturbation is presented for enhancing the scale of information sharing between different individuals. Then, a new search strategy without probability mechanism of basic ABC is introduced in the onlooker bees phase. To further balance the enhanced exploitation ability, the original strategy of randomly producing an individual is substituted with an opposition-based learning technique with multiple scout bees and the frequency of perturbation mechanism. To testify the effectiveness and the convergence performance of nABC, it is compared with basic ABC and other famous methods for solving fifteen UFLPs from OR-library. Experimental results demonstrate that the proposed nABC is superior to other state-of-the-art approaches in terms of solution accuracy, convergence speed and robustness.
Keyword:
Artificial bee colony algorithm
Pure crossover operation
Frequency of perturbation
Opposition-based learning
Facility location
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.5
论文数:
1.0W
被引数:
3.8W
机构
引用论文
.png){kind=icon}
Neighborhood search heuristics for the uncapacitated facility location problem无能力设施选址问题的邻域搜索启发式方法
PREAI
分享收藏
查看更多内容