Full Text:   <3191>

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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: 4752

Citations:  Bibtex RefMan EndNote GB/T7714


Ren-bin Xiao


Jia Zhao


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


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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%T Firefly algorithm with division of roles for complex optimal scheduling
%A Jia Zhao
%A Wenping Chen
%A Renbin Xiao
%A Jun Ye
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%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000691

T1 - Firefly algorithm with division of roles for complex optimal scheduling
A1 - Jia Zhao
A1 - Wenping Chen
A1 - Renbin Xiao
A1 - Jun Ye
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 22
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SP - 1311
EP - 1333
%@ 2095-9184
Y1 - 2021
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2000691

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.




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


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