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CLC number: TP301.6

On-line Access: 2021-10-08

Received: 2020-12-10

Revision Accepted: 2021-03-09

Crosschecked: 2021-09-09

Cited: 0

Clicked: 3027

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Ren-bin Xiao

https://orcid.org/0000-0003-0951-2734

Jia Zhao

https://orcid.org/0000-0002-3652-1903

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Frontiers of Information Technology & Electronic Engineering  2021 Vol.22 No.10 P.1311-1333

http://doi.org/10.1631/FITEE.2000691


Firefly algorithm with division of roles for complex optimal scheduling


Author(s):  Jia Zhao, Wenping Chen, Renbin Xiao, Jun Ye

Affiliation(s):  School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China; more

Corresponding email(s):   zhaojia925@163.com, chen_9731@163.com, rbxiao@hust.edu.cn, yejun68@sina.com

Key Words:  Firefly algorithm (FA), Division of roles, Cauchy mutation, Elite neighborhood search, Optimal scheduling


Jia Zhao, Wenping Chen, Renbin Xiao, Jun Ye. Firefly algorithm with division of roles for complex optimal scheduling[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(10): 1311-1333.

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author="Jia Zhao, Wenping Chen, Renbin Xiao, Jun Ye",
journal="Frontiers of Information Technology & Electronic Engineering",
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pages="1311-1333",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000691"
}

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%A Wenping Chen
%A Renbin Xiao
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T1 - Firefly algorithm with division of roles for complex optimal scheduling
A1 - Jia Zhao
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DOI - 10.1631/FITEE.2000691


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.

面向复杂优化调度的角色分工萤火虫算法

赵嘉1,2,陈文平1,肖人彬3,叶军1
1南昌工程学院信息工程学院,中国南昌市,330099
2江西省水信息协同传感与智能处理重点实验室,中国南昌市,330099
3华中科技大学人工智能与自动化学院,中国武汉市,430074
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。

关键词:萤火虫算法;角色分工;柯西突变;精英邻域搜索;优化调度

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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