数据驱动的发展式头脑风暴优化算法综述-《郑州大学学报(工学版)》

文章信息/Info

Title:: Dwvelopmental Brain Storm Optimization Algorithms: From a Data-driven Perspective

作者:: 程适; 陈俊风; 孙奕菲; 史玉回; 1.陕西师范大学计算机科学学院,陕西西安,710119；2.河海大学物联网工程学院,江苏常州,213022；3.陕西师范大学物理学与信息技术学院,陕西西安,710119；4.南方科技大学计算机科学与工程系,广东深圳,518055

Author(s):: Cheng Shi1; Chen Junfeng2; Sun Yifei3; Shi Yuhui4; 1. School of Computer Science, Shaanxi Normal University, Xi’an, Shaanxi, 710119; 2. School of Internet of Things Engineering, Hohai University, Changzhou, Jiangsu, 213022; 3. School of Physics and Information Technology, Shaanxi Normal University, Xi’an, Shaanxi 710119; 4. Computer Science, Southern University of Science and Technology Department of Engineering and Engineering, Shenzhen, Guangdong, 518055

Keywords:: brain storm optimization; developmental swarm intelligence; convergent operation; divergent operation

摘要:: 头脑风暴优化（Brain Storm Optimization，BSO）算法是一种新兴的群体智能优化方法，以众人集思广益解决问题为原型，抽取其中解决问题的模式，将其抽象为智能优化算法。介绍了头脑风暴优化算法的优化算子和基本原理，在对基本头脑风暴优化算法和目标空间中的头脑风暴优化算法比较的基础上，对头脑风暴优化算法的研究现状，包括群体多样性、求解不同类型问题和实际应用的研究现状进行了全面的综述。最后对头脑风暴优化算法有待进一步研究的问题进行了展望.

Abstract:: For swarm intelligence algorithms, each individual in the swarm represented a solution in the search space, and it also could be seen as a data sample from the search space. Brain storm optimization (BSO) algorithm was a new and promising swarm intelligence algorithm, which simulated the human brainstorming process. Through the convergent operation and divergent operation, individuals in BSO were grouped and diverged in the search space/objective space. In this paper, the development history, and the state-of-the-art of the BSO algorithm were reviewed. Every individual in the BSO algorithm was not only a solution to the problem to be optimized, but also a data point to reveal the landscape of the problem. Based on the survey of brain storm optimization algorithms, more analyses could be conducted to understand the function of BSO algorithm and more variants of BSO algorithms could be proposed t22o solve different problems.