Wenjun Wang

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Wenjun Wang (26)
Hui Wang (14)
Hui Sun (8)
Jia Zhao (7)
Zhihua Cui (6)
Xinyu Zhou (4)
Hu Peng (3)
Huasheng Zhu (3)
Tao Zeng (3)
Tingyu Ye (3)
Changshou Deng (2)
Jianyi Wan (2)
Li Lv (2)
Luqi Zhang (2)
Mingwen Wang (2)
Shahryar Rahnamayan (2)
Songyi Xiao (2)
Xiang Yu (2)
Zhijian Wu (2)
Feng Wang (1)

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Journal/Magazine Names

International Journal of Computing Science and Mathematics (5)
International Journal of Bio-Inspired Computation (3)
International Journal of Wireless and Mobile Computing (3)
International Journal of Innovative Computing and Applications (2)
Applied Mathematics and Computation (1)
Expert Systems with Applications: An International Journal (1)
Information Sciences: an International Journal (1)
Knowledge-Based Systems (1)
Soft Computing - A Fusion of Foundations, Methodologies and Applications (1)

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2019 IEEE Congress on Evolutionary Computation (CEC) (3)
GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion (2)
2016 IEEE Congress on Evolutionary Computation (CEC) (1)
GCIS '09: Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 03 (1)
Neural Information Processing (1)

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Inderscience Publishers (13)
IEEE Press (4)
Association for Computing Machinery (2)
Elsevier Science Inc. (2)
Springer-Verlag (2)
Elsevier Science Publishers B. V. (1)
IEEE Computer Society (1)
Pergamon Press, Inc. (1)

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research-article
Multi-population artificial bee colony algorithm based on the nearest neighbour partition
- Mingze Ma
  School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China
  ,
- Wenjun Wang
  School of Business Administration, Nanchang Institute of Technology, Nanchang, 330099, China
  ,
- Xin Li
  School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China
International Journal of Computing Science and Mathematics, Volume 18, Issue 3•2023, pp 235-244 • https://doi.org/10.1504/ijcsm.2023.134568

The artificial bee colony (ABC) has shown great potential among many swarm intelligence optimisation algorithms (SIOAs). However, ABC still shows deficiencies in some aspects. The weak exploitation ability makes the original ABC hard to achieve promising ...
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research-article
Artificial bee colony based on adaptive search strategy and random grouping mechanism
- Tao Zeng
  School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
  ,
- Wenjun Wang
  School of Business Administration, Nanchang Institute of Technology, Nanchang 330099, China
  ,
- Hui Wang
  School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
  ,
- Zhihua Cui
  School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China
  ,
- Feng Wang
  School of Computer Science, Wuhan University, Wuhan 430072, China
  ,
- Yun Wang
  School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
  ,
- Jia Zhao
  School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
Expert Systems with Applications: An International Journal, Volume 192, Issue C•Apr 2022 • https://doi.org/10.1016/j.eswa.2021.116332
Abstract
As a popular global optimization algorithm, artificial bee colony (ABC) has strong search ability and simple concept. However, ABC has some deficiencies. The exploitation ability of ABC is not as strong as its exploration ability. The ...
Highlights

An adaptive search strategy is designed by monitoring the changes of the success rate.
10Citation
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research-article
Artificial bee colony algorithm with efficient search strategy based on random neighborhood structure
- Tingyu Ye
  School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
  ,
- Wenjun Wang
  School of Business Administration, Nanchang Institute of Technology, Nanchang 330099, China
  ,
- Hui Wang
  School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
  ,
- Zhihua Cui
  School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China
  ,
- Yun Wang
  School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
  ,
- Jia Zhao
  School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
  ,
- Min Hu
  School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
Knowledge-Based Systems, Volume 241, Issue C•Apr 2022 • https://doi.org/10.1016/j.knosys.2022.108306
Abstract
As a popular swarm intelligence algorithm, artificial bee colony (ABC) achieves excellent optimization performance, but it has some shortcomings. In order to strengthen the performance of ABC, a new ABC with efficient search strategy ...
Highlights

A new random K-neighborhood structure (RNS) is designed.
Each solution has an ...
5Citation
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research-article
A new artificial bee colony algorithm based on modified search strategy
- Kai Li
  School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China
  ,
- Minyang Xu
  School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China
  ,
- Tao Zeng
  School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China
  ,
- Tingyu Ye
  School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China
  ,
- Luqi Zhang
  School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China
  ,
- Wenjun Wang
  School of Business Administration, Nanchang Institute of Technology, Nanchang, 330099, China
  ,
- Hui Wang
  School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China
International Journal of Computing Science and Mathematics, Volume 15, Issue 4•2022, pp 387-395 • https://doi.org/10.1504/ijcsm.2022.125917

Artificial bee colony (ABC) is an efficient global optimisation algorithm. It has attracted the attention of many researchers because of its simple concept and strong exploration. However, it exhibits weak exploitation capability. To improve this case, a ...
3Citation
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research-article
Data-driven artificial bee colony algorithm based on radial basis function neural network
- Tao Zeng
  School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
  ,
- Hui Wang
  School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
  ,
- Wenjun Wang
  School of Business Administration, Nanchang Institute of Technology, Nanchang 330099, China
  ,
- Tingyu Ye
  School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
  ,
- Luqi Zhang
  School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
  ,
- Jia Zhao
  School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
International Journal of Bio-Inspired Computation, Volume 20, Issue 1•2022, pp 1-10 • https://doi.org/10.1504/ijbic.2022.126278

Search strategies play an essential role in artificial bee colony (ABC) algorithm. Different optimisation problems and search stages may need different search strategies. However, it is difficult to choose an appropriate search strategy. To tackle this ...
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research-article
A new multi-objective artificial bee colony algorithm based on reference point and opposition
- Songyi Xiao
  School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
  ,
- Wenjun Wang
  School of Business Administration, Nanchang Institute of Technology, Nanchang 330099, China
  ,
- Hui Wang
  School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
  ,
- Zhikai Huang
  School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
International Journal of Bio-Inspired Computation, Volume 19, Issue 1•2022, pp 18-28 • https://doi.org/10.1504/ijbic.2022.120732

A new multi-objective artificial bee colony (ABC) algorithm based on reference point and opposition (called ROMOABC) is proposed in this paper. Firstly, the original framework of ABC is modified to improve the efficiency of population renewal and ...
3Citation
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research-article
Decision preference-based artificial bee colony algorithm for many-objective optimal allocation of water resources
- Wenjun Wang
  School of Business Administration, Nanchang Institute of Technology, Nanchang 330099, China
  ,
- Hui Wang
  School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
  ,
- Changyan Li
  School of Business Administration, Nanchang Institute of Technology, Nanchang 330099, China
International Journal of Computing Science and Mathematics, Volume 12, Issue 4•2020, pp 364-373 • https://doi.org/10.1504/ijcsm.2020.112672

Freshwater becomes a scarce resource because of the rapid growth of population and serious water pollution. Optimal allocation of water resources is an effective method to tackle this issue. In this paper, we consider a many objective water resources ...
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research-article
Brain Storm Optimization Algorithm based on Competition Mechanism
- Fenqiang Li
  School of Science and Technology, Jiujiang University, Jiujiang, China
  ,
- Hu Peng
  School of Science and Technology, Jiujiang University, Jiujiang, China
  ,
- Changshou Deng
  School of Science and Technology, Jiujiang University, Jiujiang, China
  ,
- Yi Zeng
  School of Science and Technology, Jiujiang University, Jiujiang, China
  ,
- Yucheng Tan
  School of Information Science, Jiujiang University, Jiujiang, China
  ,
- Wenjun Wang
  Nanchang Institute of Technology, Nanchang, China
2019 IEEE Congress on Evolutionary Computation (CEC)•June 2019, pp 1140-1147• https://doi.org/10.1109/CEC.2019.8790330
Brain storm optimization algorithm (BSO) is a new but excellent algorithm who based on brainstorming process. However, BSO has some disadvantages like premature convergence and high time complexity. In order to solve these problems, a new BSO based on ...
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research-article
A Novel Artificial Bee Colony Algorithm with Division of Labor for Solving CEC 2019 100-Digit Challenge Benchmark Problems
- Jiaxin Lu
  School of Computer and Information Engineering, Jiangxi Normal University, Nanchang, 330022, China
  ,
- Xinyu Zhou
  School of Computer and Information Engineering, Jiangxi Normal University, Nanchang, 330022, China
  ,
- Yong Ma
  School of Computer and Information Engineering, Jiangxi Normal University, Nanchang, 330022, China
  ,
- Mingwen Wang
  School of Computer and Information Engineering, Jiangxi Normal University, Nanchang, 330022, China
  ,
- Jianyi Wan
  School of Computer and Information Engineering, Jiangxi Normal University, Nanchang, 330022, China
  ,
- Wenjun Wang
  School of Business Administration, Nanchang Institute of Technology, Nanchang, 330099, China
2019 IEEE Congress on Evolutionary Computation (CEC)•June 2019, pp 387-394• https://doi.org/10.1109/CEC.2019.8790252
As a relatively new paradigm of evolutionary algorithms, artificial bee colony (ABC) algorithm has shown attractive performance in solving optimization problems. However, for some complex optimization problems, its performance is still not satisfactory. ...
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research-article
Multi-strategy and Dimension Perturbation Ensemble of Artificial Bee Colony
- Hui Wang
  School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China
  ,
- Wenjun Wang
  School of Business Administration, Nanchang Institute of Technology, Nanchang, 330099, China
  ,
- Songyi Xiao
  School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China
  ,
- Zhihua Cui
  Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan, 030024, China
  ,
- Wei Li
  School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, China
  ,
- Huasheng Zhu
  School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China
  ,
- Shengqing Zhu
  Asia Intelligence Technology PTE LTD, Singapore, 338519, Singapore
2019 IEEE Congress on Evolutionary Computation (CEC)•June 2019, pp 697-704• https://doi.org/10.1109/CEC.2019.8790129
Ensemble learning is a popular machine learning technique, which employs multiple learning methods to obtain better performance than any single constituent method. Recently, ensemble learning was successfully used in several bio-inspired optimization ...
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Differential evolution with multi-information guidance
- Xinyu Zhou
  Jiangxi Normal University, Nanchang, China
  ,
- Yunan Liu
  Jiangxi Normal University, Nanchang, China
  ,
- Mingwen Wang
  Jiangxi Normal University, Nanchang, China
  ,
- Jianyi Wan
  Jiangxi Normal University, Nanchang, China
  ,
- Hui Wang
  Nanchang Institute of Technology, Nanchang, China
  ,
- Wenjun Wang
  Nanchang Institute of Technology, Nanchang, China
  ,
- Hu Peng
  Jiujiang University, Jiujiang, China
GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion•July 2018, pp 260-261• https://doi.org/10.1145/3205651.3205668

In this paper, we proposed a novel differential evolution (DE) variant with multi-information guidance. First, based on a rank-based method, the DE population is divided into three groups by using both of the fitness information and position ...
1Citation
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Published By ACM
Published By ACM
Gaussian bare-bones cuckoo search algorithm
- Hu Peng
  Jiujiang University, Jiujiang, China
  ,
- Changshou Deng
  Jiujiang University, Jiujiang, China
  ,
- Hui Wang
  Nanchang Institute of Technology, Nanchang, China
  ,
- Wenjun Wang
  Nanchang Institute of Technology, Nanchang, China
  ,
- Xinyu Zhou
  Jiangxi Normal University, Nanchang, China
  ,
- Zhijian Wu
  Wuhan University, Wuhan, China
GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion•July 2018, pp 93-94• https://doi.org/10.1145/3205651.3205666

Cuckoo search (CS), as a relatively recent emerged swarm intelligence algorithm, is powerful and popular for the complex real parameter global optimization. However, the premature convergence has greatly affected the performance of original CS. Inspired ...
9Citation
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Article
Firefly Algorithm for Demand Estimation of Water Resources
- Hui Wang
  Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, 330099, Nanchang, China
  School of Information Engineering, Nanchang Institute of Technology, 330099, Nanchang, China
  ,
- Zhihua Cui
  Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, 030024, Taiyuan, China
  ,
- Wenjun Wang
  School of Business Administration, Nanchang Institute of Technology, 330099, Nanchang, China
  ,
- Xinyu Zhou
  College of Computer and Information Engineering, Jiangxi Normal University, 330022, Nanchang, China
  ,
- Jia Zhao
  Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, 330099, Nanchang, China
  School of Information Engineering, Nanchang Institute of Technology, 330099, Nanchang, China
  ,
- Li Lv
  Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, 330099, Nanchang, China
  School of Information Engineering, Nanchang Institute of Technology, 330099, Nanchang, China
  ,
- Hui Sun
  Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, 330099, Nanchang, China
  School of Information Engineering, Nanchang Institute of Technology, 330099, Nanchang, China
Neural Information Processing•November 2017, pp 11-20• https://doi.org/10.1007/978-3-319-70093-9_2
Abstract
Firefly algorithm (FA) is an efficient swarm intelligence optimization technique, which has been used to solve many engineering optimization problems. In this paper, we present a new FA (called NFA) variant for demand estimation of water resources ...
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A new cuckoo search algorithm with hybrid strategies for flow shop scheduling problems
- Hui Wang
  School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China 210044 and School of Information Engineering, Nanchang Institute of Technology, Nanchang, China 330099
  ,
- Wenjun Wang
  School of Business Administration, Nanchang Institute of Technology, Nanchang, China 330099
  ,
- Hui Sun
  School of Information Engineering, Nanchang Institute of Technology, Nanchang, China 330099
  ,
- Zhihua Cui
  School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan, China 030024
  ,
- Shahryar Rahnamayan
  Department of Electrical, Computer, and Software Engineering, University of Ontario Institute of Technology (UOIT), Oshawa, Canada L1H 7K4
  ,
- Sanyou Zeng
  School of Computer Science, China University of Geosciences, Wuhan, China 430074
Soft Computing - A Fusion of Foundations, Methodologies and Applications, Volume 21, Issue 15•August 2017, pp 4297-4307 • https://doi.org/10.1007/s00500-016-2062-9

Cuckoo search (CS) is a recently developed meta-heuristic algorithm, which has shown good performance on many continuous optimization problems. In this paper, we present a new CS algorithm, called NCS, for solving flow shop scheduling problems (FSSP). ...
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research-article
Firefly algorithm with neighborhood attraction
- Hui Wang
  School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
  ,
- Wenjun Wang
  School of Business Administration, Nanchang Institute of Technology, Nanchang 330099, China
  ,
- Xinyu Zhou
  College of Computer and Information Engineering, Jiangxi Normal University, Nanchang 330022, China
  ,
- Hui Sun
  School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
  ,
- Jia Zhao
  School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
  ,
- Xiang Yu
  School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
  ,
- Zhihua Cui
  School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China
Information Sciences: an International Journal, Volume 382, Issue C•March 2017, pp 374-387 • https://doi.org/10.1016/j.ins.2016.12.024

The search of FA is determined by the attractions among fireflies.Too many attractions may result in the oscillations of search and high complexity.A neighborhood attraction model is proposed in this paper.The proposed approach can effectively improve ...
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research-article
Adaptive firefly algorithm with alternative search
- Hui Wang
  School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
  ,
- Wenjun Wang
  School of Business Administration, Nanchang Institute of Technology, Nanchang 330099, China
  ,
- Hui Sun
  School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
  ,
- Jia Zhao
  School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
  ,
- Xiang Yu
  School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
  ,
- Li Lv
  School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
  ,
- Huasheng Zhu
  School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
2016 IEEE Congress on Evolutionary Computation (CEC), pp 1779-1785• https://doi.org/10.1109/CEC.2016.7744004
Firefly algorithm (FA) is a population-based stochastic algorithm, which is inspired by the behavior of the flashing of fireflies. Though some recent studies show that FA is effective on many optimization problems, its performance is greatly influenced by ...
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Firefly algorithm with random attraction
- Hui Wang
  School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
  ,
- Wenjun Wang
  School of Business Administration, Nanchang Institute of Technology, Nanchang 330099, China
  ,
- Hui Sun
  School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
  ,
- Shahryar Rahnamayan
  Department of Electrical, Computer and Software Engineering, University of Ontario Institute of Technology, 2000 Simcoe Street North, Oshawa, ON L1H 7K4, Canada
International Journal of Bio-Inspired Computation, Volume 8, Issue 1•February 2016, pp 33-41 • https://doi.org/10.1504/IJBIC.2016.074630

Firefly algorithm FA is a new meta-heuristic optimisation algorithm, which simulates the social behaviour of fireflies based on the flashing and attraction characteristics of fireflies. The standard FA employs a fully attracted model, in which each ...
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An enhanced search strategy for firefly algorithm
- Wenjun Wang
  School of Business Administration, Nanchang Institute of Technology, Nanchang 330099, China
International Journal of Wireless and Mobile Computing, Volume 11, Issue 1•January 2016, pp 62-67 • https://doi.org/10.1504/IJWMC.2016.079442

The Firefly Algorithm FA is a recently proposed meta-heuristic which is inspired by the social behaviour of fireflies. Though FA has shown good performance on many optimisation problems, it easily falls into local optima in solving complex optimisation ...
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A new artificial bee colony based on GPU for solving large-scale production scheduling problem
- Wenjun Wang
  School of Business Administration, Nanchang Institute of Technology, Nanchang 330099, China
International Journal of Wireless and Mobile Computing, Volume 10, Issue 4•January 2016, pp 393-398 • https://doi.org/10.1504/IJWMC.2016.078203

Production scheduling problem is a difficult and discrete optimisation problem, which is proved to be NP-hard. Its computational time grows exponentially with increasing of problem size. Therefore, solving large-scale production scheduling problem is a ...
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Multi-strategy ensemble artificial bee colony algorithm for large-scale production scheduling problem
- Hui Wang
  School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
  ,
- Wenjun Wang
  School of Business Administration, Nanchang Institute of Technology, Nanchang 330099, China
  ,
- Hui Sun
  School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
International Journal of Innovative Computing and Applications, Volume 6, Issue 3/4•November 2015, pp 128-136

This paper presents a multi-strategy ensemble artificial bee colony MEABC algorithm for solving large-scale production scheduling problem. MEABC is a new variant of artificial bee colony ABC, which has shown good performance on many continuous ...
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It is ACM's intention to make the derivation of any publication statistics it generates clear to the user.

Average citations per article = The total Citation Count divided by the total Publication Count.
Citation Count = cumulative total number of times all authored works by this author were cited by other works within ACM's bibliographic database. Almost all reference lists in articles published by ACM have been captured. References lists from other publishers are less well-represented in the database. Unresolved references are not included in the Citation Count. The Citation Count is citations TO any type of work, but the references counted are only FROM journal and proceedings articles. Reference lists from books, dissertations, and technical reports have not generally been captured in the database. (Citation Counts for individual works are displayed with the individual record listed on the Author Page.)
Publication Count = all works of any genre within the universe of ACM's bibliographic database of computing literature of which this person was an author. Works where the person has role as editor, advisor, chair, etc. are listed on the page but are not part of the Publication Count.
Publication Years = the span from the earliest year of publication on a work by this author to the most recent year of publication of a work by this author captured within the ACM bibliographic database of computing literature (The ACM Guide to Computing Literature, also known as "the Guide".
Available for download = the total number of works by this author whose full texts may be downloaded from an ACM full-text article server. Downloads from external full-text sources linked to from within the ACM bibliographic space are not counted as 'available for download'.
Average downloads per article = The total number of cumulative downloads divided by the number of articles (including multimedia objects) available for download from ACM's servers.
Downloads (cumulative) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server since the downloads were first counted in May 2003. The counts displayed are updated monthly and are therefore 0-31 days behind the current date. Robotic activity is scrubbed from the download statistics.
Downloads (12 months) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server over the last 12-month period for which statistics are available. The counts displayed are usually 1-2 weeks behind the current date. (12-month download counts for individual works are displayed with the individual record.)
Downloads (6 weeks) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server over the last 6-week period for which statistics are available. The counts displayed are usually 1-2 weeks behind the current date. (6-week download counts for individual works are displayed with the individual record.)

ACM Author-Izer Service

Summary Description

ACM Author-Izer is a unique service that enables ACM authors to generate and post links on both their homepage and institutional repository for visitors to download the definitive version of their articles from the ACM Digital Library at no charge.

Downloads from these sites are captured in official ACM statistics, improving the accuracy of usage and impact measurements. Consistently linking to definitive version of ACM articles should reduce user confusion over article versioning.

ACM Author-Izer also extends ACM’s reputation as an innovative “Green Path” publisher, making ACM one of the first publishers of scholarly works to offer this model to its authors.

To access ACM Author-Izer, authors need to establish a free ACM web account. Should authors change institutions or sites, they can utilize the new ACM service to disable old links and re-authorize new links for free downloads from a different site.

How ACM Author-Izer Works

Authors may post ACM Author-Izer links in their own bibliographies maintained on their website and their own institution’s repository. The links take visitors to your page directly to the definitive version of individual articles inside the ACM Digital Library to download these articles for free.

The Service can be applied to all the articles you have ever published with ACM.

Depending on your previous activities within the ACM DL, you may need to take up to three steps to use ACM Author-Izer.

For authors who do not have a free ACM Web Account:

Go to the ACM DL http://dl.acm.org/ and click SIGN UP. Once your account is established, proceed to next step.

For authors who have an ACM web account, but have not edited their ACM Author Profile page:

Sign in to your ACM web account and go to your Author Profile page. Click "Add personal information" and add photograph, homepage address, etc. Click ADD AUTHOR INFORMATION to submit change. Once you receive email notification that your changes were accepted, you may utilize ACM Author-izer.

For authors who have an account and have already edited their Profile Page:

Sign in to your ACM web account, go to your Author Profile page in the Digital Library, look for the ACM Author-izer link below each ACM published article, and begin the authorization process. If you have published many ACM articles, you may find a batch Authorization process useful. It is labeled: "Export as: ACM Author-Izer Service"

ACM Author-Izer also provides code snippets for authors to display download and citation statistics for each “authorized” article on their personal pages. Downloads from these pages are captured in official ACM statistics, improving the accuracy of usage and impact measurements. Consistently linking to the definitive version of ACM articles should reduce user confusion over article versioning.

Note: You still retain the right to post your author-prepared preprint versions on your home pages and in your institutional repositories with DOI pointers to the definitive version permanently maintained in the ACM Digital Library. But any download of your preprint versions will not be counted in ACM usage statistics. If you use these AUTHOR-IZER links instead, usage by visitors to your page will be recorded in the ACM Digital Library and displayed on your page.

FAQ

Q. What is ACM Author-Izer?
A. ACM Author-Izer is a unique, link-based, self-archiving service that enables ACM authors to generate and post links on either their home page or institutional repository for visitors to download the definitive version of their articles for free.
Q. What articles are eligible for ACM Author-Izer?
A. ACM Author-Izer can be applied to all the articles authors have ever published with ACM. It is also available to authors who will have articles published in ACM publications in the future.

Q. Are there any restrictions on authors to use this service?
A. No. An author does not need to subscribe to the ACM Digital Library nor even be a member of ACM.

Q. What are the requirements to use this service?
A. To access ACM Author-Izer, authors need to have a free ACM web account, must have an ACM Author Profile page in the Digital Library, and must take ownership of their Author Profile page.

Q. What is an ACM Author Profile Page?
A. The Author Profile Page initially collects all the professional information known about authors from the publications record as known by the ACM Digital Library. The Author Profile Page supplies a quick snapshot of an author's contribution to the field and some rudimentary measures of influence upon it. Over time, the contents of the Author Profile page may expand at the direction of the community. Please visit the ACM Author Profile documentation page for more background information on these pages.

Q. How do I find my Author Profile page and take ownership?
A. You will need to take the following steps:
- Create a free ACM Web Account
- Sign-In to the ACM Digital Library
- Find your Author Profile Page by searching the ACM Digital Library for your name
- Find the result you authored (where your author name is a clickable link)
- Click on your name to go to the Author Profile Page
- Click the "Add Personal Information" link on the Author Profile Page
- Wait for ACM review and approval; generally less than 24 hours

Q. Why does my photo not appear?
A. Make sure that the image you submit is in .jpg or .gif format and that the file name does not contain special characters

Q. What if I cannot find the Add Personal Information function on my author page?
A. The ACM account linked to your profile page is different than the one you are logged into. Please logout and login to the account associated with your Author Profile Page.

Q. What happens if an author changes the location of his bibliography or moves to a new institution?
A. Should authors change institutions or sites, they can utilize ACM Author-Izer to disable old links and re-authorize new links for free downloads from a new location.

Q. What happens if an author provides a URL that redirects to the author’s personal bibliography page?
A. The service will not provide a free download from the ACM Digital Library. Instead the person who uses that link will simply go to the Citation Page for that article in the ACM Digital Library where the article may be accessed under the usual subscription rules.

However, if the author provides the target page URL, any link that redirects to that target page will enable a free download from the Service.

Q. What happens if the author’s bibliography lives on a page with several aliases?
A. Only one alias will work, whichever one is registered as the page containing the author’s bibliography. ACM has no technical solution to this problem at this time.

Q. Why should authors use ACM Author-Izer?
A. ACM Author-Izer lets visitors to authors’ personal home pages download articles for no charge from the ACM Digital Library. It allows authors to dynamically display real-time download and citation statistics for each “authorized” article on their personal site.

Q. Does ACM Author-Izer provide benefits for authors?
A. Downloads of definitive articles via Author-Izer links on the authors’ personal web page are captured in official ACM statistics to more accurately reflect usage and impact measurements.

Authors who do not use ACM Author-Izer links will not have downloads from their local, personal bibliographies counted. They do, however, retain the existing right to post author-prepared preprint versions on their home pages or institutional repositories with DOI pointers to the definitive version permanently maintained in the ACM Digital Library.

Q. How does ACM Author-Izer benefit the computing community?
A. ACM Author-Izer expands the visibility and dissemination of the definitive version of ACM articles. It is based on ACM’s strong belief that the computing community should have the widest possible access to the definitive versions of scholarly literature. By linking authors’ personal bibliography with the ACM Digital Library, user confusion over article versioning should be reduced over time.

In making ACM Author-Izer a free service to both authors and visitors to their websites, ACM is emphasizing its continuing commitment to the interests of its authors and to the computing community in ways that are consistent with its existing subscription-based access model.

Q. How do authors access ACM Author-Izer?
A. See How to Use

Q. Why can’t I find my most recent publication in my ACM Author Profile Page?
A. There is a time delay between publication and the process which associates that publication with an Author Profile Page. Right now, that process usually takes 4-8 weeks.

Q. How does ACM Author-Izer expand ACM’s “Green Path” Access Policies?
A. ACM Author-Izer extends the rights and permissions that authors retain even after copyright transfer to ACM, which has been among the “greenest” publishers. ACM enables its author community to retain a wide range of rights related to copyright and reuse of materials. They include:
- Posting rights that ensure free access to their work outside the ACM Digital Library and print publications
- Rights to reuse any portion of their work in new works that they may create
- Copyright to artistic images in ACM’s graphics-oriented publications that authors may want to exploit in commercial contexts
- All patent rights, which remain with the original owner

Wenjun Wang

Wenjun Wang

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Results

Multi-population artificial bee colony algorithm based on the nearest neighbour partition

Artificial bee colony based on adaptive search strategy and random grouping mechanism

Artificial bee colony algorithm with efficient search strategy based on random neighborhood structure

A new artificial bee colony algorithm based on modified search strategy

Data-driven artificial bee colony algorithm based on radial basis function neural network

A new multi-objective artificial bee colony algorithm based on reference point and opposition

Decision preference-based artificial bee colony algorithm for many-objective optimal allocation of water resources

Brain Storm Optimization Algorithm based on Competition Mechanism

A Novel Artificial Bee Colony Algorithm with Division of Labor for Solving CEC 2019 100-Digit Challenge Benchmark Problems

Multi-strategy and Dimension Perturbation Ensemble of Artificial Bee Colony

Differential evolution with multi-information guidance

Gaussian bare-bones cuckoo search algorithm

Firefly Algorithm for Demand Estimation of Water Resources

A new cuckoo search algorithm with hybrid strategies for flow shop scheduling problems

Firefly algorithm with neighborhood attraction

Adaptive firefly algorithm with alternative search

Firefly algorithm with random attraction

An enhanced search strategy for firefly algorithm

A new artificial bee colony based on GPU for solving large-scale production scheduling problem

Multi-strategy ensemble artificial bee colony algorithm for large-scale production scheduling problem