面向复杂优化调度的角色分工萤火虫算法

赵嘉; 陈文平; 肖人彬; 叶军

doi:10.1631/FITEE.2000691

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面向复杂优化调度的角色分工萤火虫算法

常规文章 | Updated：2022-06-06

- 面向复杂优化调度的角色分工萤火虫算法
- Firefly algorithm with division of roles for complex optimal scheduling
- 信息与电子工程前沿（英文） 2021年22卷第10期页码：1311-1333
- Affiliations：
  
  School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
  Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang 330099, China
  School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
- Author bio：
  
  [ "", "Jia ZHAO, first author of this invited paper, received his ME degree in computer application technology from Nanchang Hangkong University, Nanchang, China, in 2011, and PhD degree in information and communication engineering from Hohai University, Nanjing, China, in 2020. He is currently a full professor with the School of Information Engineering, Nanchang Institute of Technology, Nanchang, China. He is Director of the Nanchang Key Laboratory of Big Data and Computational Intelligence. His research interests include big data analysis and artificial intelligence theory. E-mail: zhaojia925@163.com" ]
  [ "Wenping CHEN, E-mail: chen_9731@163.com" ]
  [ "", "Renbin XIAO, corresponding author of this invited paper, received his PhD degree in systems engineering from Huazhong University of Science and Technology (HUST), Wuhan, China, in 1993. He is currently a full professor with the School of Artificial Intelligence and Automation, HUST. He has coauthored more than 10 books and published over 300 academic papers. He received 10 projects from the National Natural Science Foundation of China and won five science and technology awards from the Ministry of Education and Hubei Province, China. His research interests include swarm intelligence, emergent computation, intelligent manufacturing, and innovation design of complex products" ]
  [ "Jun YE, E-mail: yejun68@sina.com" ]
- Funds：
  
  the National Science and Technology Innovation 2030 Major Project of the Ministry of Science and Technology of China(2018AAA0101200);the National Natural Science Foundation of China(52069014);the National Natural Science Foundation of China(51669014);the Science Foundation for Distinguished Young Scholars of Jiangxi Province, China(2018ACB21029)
- DOI：10.1631/FITEE.2000691
  中图分类号： TP301.6
- 收稿：2020-12-10，
  
  修回：2021-;9-;9，
  
  纸质出版：2021-10
- Accepted：
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赵嘉, 陈文平, 肖人彬, 等. 面向复杂优化调度的角色分工萤火虫算法[J]. 信息与电子工程前沿（英文）, 2021,22(10):1311-1333.

Jia ZHAO, Wenping CHEN, Renbin XIAO, et al. Firefly algorithm with division of roles for complex optimal scheduling[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(10): 1311-1333.
赵嘉, 陈文平, 肖人彬, 等. 面向复杂优化调度的角色分工萤火虫算法[J]. 信息与电子工程前沿（英文）, 2021,22(10):1311-1333. DOI： 10.1631/FITEE.2000691.

Jia ZHAO, Wenping CHEN, Renbin XIAO, et al. Firefly algorithm with division of roles for complex optimal scheduling[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(10): 1311-1333. DOI： 10.1631/FITEE.2000691.

摘要

针对萤火虫算法使用单一学习策略无法有效求解复杂优化调度问题的不足，本文提出一种角色分工萤火虫算法。算法将萤火虫划分为领导者、开发者和跟随者3种角色，并为每种角色分配一种学习策略。领导者使用贪婪柯西突变，开发者随机选择两个领导者使用精英邻域搜索策略局部开发，跟随者随机选择两个优秀粒子进行全局探索。同时，为改善萤火虫算法使用固定步长的不足，提出阶梯变步长策略，以满足算法不同阶段对步长的需求。角色划分可平衡算法的开发与探索能力，多策略的使用能极大提高算法面对复杂优化问题的普适性。通过3组测试函数和一个梯级水库优化调度的仿真实验，验证了该算法的优化性能。

Abstract

A single strategy used in the firefly algorithm (FA) cannot effectively solve the complex optimal scheduling problem. Thus

we propose the FA with division of roles (DRFA). Herein

fireflies are divided into leaders

developers

and followers

while a learning strategy is assigned to each role: the leader chooses the greedy Cauchy mutation; the developer chooses two leaders randomly and uses the elite neighborhood search strategy for local development; the follower randomly selects two excellent particles for global exploration. To improve the efficiency of the fixed step size used in FA

a stepped variable step size strategy is proposed to meet different requirements of the algorithm for the step size at different stages. Role division can balance the development and exploration ability of the algorithm. The use of multiple strategies can greatly improve the versatility of the algorithm for complex optimization problems. The optimal performance of the proposed algorithm has been verified by three sets of test functions and a simulation of optimal scheduling of cascade reservoirs.

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