IEEE Computational Intelligence Magazine
The IEEE Computational Intelligence Magazine (CIM) publishes peer-reviewed articles that present emerging novel discoveries, important insights, or tutorial surveys in all areas of computational intelligence design and applications, in keeping with the Field of Interest of the IEEE Computational Intelligence Society (IEEE/CIS). Additionally, CIM serves as a media of communications between the governing body and its membership of IEEE/CIS. Authors are encouraged to submit papers on applications oriented developments, successful industrial implementations, design tools, technology reviews, computational intelligence education, and applied research.
Contributions should contain novel and previously unpublished material. The novelty will usually lie in original concepts, results, techniques, observations, hardware/software implementations, or applications, but may also provide syntheses or new insights into previously reported research. Surveys and expository submissions are also welcome. In general, material which has been previously copyrighted, published or accepted for publication will not be considered for publication; however, prior preliminary or abbreviated publication of the material shall not preclude publication in this journal.
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IEEE CIM Fast-Track Special Issue on "Computational Intelligence for Combating COVID-19"
In order to support the world-wide efforts in fighting COVID-19, the IEEE Computational Intelligence Society (IEEE CIS) has set up a program, the COVID-19 Initiative. Under this initiative, the IEEE CIM will expedite, to the extent possible, the processing of all articles submitted to the special issue with primary focus on COVID-19:
- We have set-up the IEEE CIM Fast-Track Special Issue on Computational Intelligence for Combating COVID-19 to process COVID-19 focused manuscripts. All papers submitted to this Fast-Track Special Issue (due: June 30) will undergo a fast review process, with the targeted first decision within 3 weeks. If the paper can go to the revision stage, the author(s) then have 2 weeks of revision time, followed by another round of review within 3 weeks to reach a final decision. That is to say, we target to reach a final decision for the Fast-Track Special Issue manuscripts by August 7.
- If you decide to submit to this Fast-Track Special Issue, please make sure you select the "Special Issue: Computational Intelligence for Combating COVID-19".
- Your manuscript must be within the scope of IEEE CIM, as well as with a research focus on COVID-19.
- If accepted, CIM will arrange to publish and print such articles immediately. Furthermore, all such articles will be published, free-of-charge to authors and readers, as free access for one year from the date of the publication to enable the research findings to be disseminated widely and freely to other researchers and the community at large.
More information about this Fast-Track Special Issue can be found here. We look forward to your submissions and support to CIM.
Current Special Issues
IEEE CIM Special Issue on "Evolutionary Neural Architecture Search and Applications," Guest Editors: Yanan Sun, Mengjie Zhang, Gary G. Yen. Submission Deadline: August 30, 2020. (Call for Papers)
IEEE CIM Special Issue on "Computational Intelligence for Smart City Services," Guest Editors: Hao Sheng, Hui Xiong, Zhipeng Cai, Xiuzhen Cheng. Submission Deadline: November 1, 2020. (Call for Papers)
Ant Colony Optimization Algorithms for Dynamic Optimization: A Case Study of the Dynamic Travelling Salesperson Problem
Authors: Michalis Mavrovouniotis, Shengxiang Yang, Mien Van, Changhe Li, Marios Polycarpou
Publication: IEEE Computational Intelligence Magazine (CIM)
Issue: Volume 15, Issue 1 – February 2020
Abstract: Ant colony optimization is a swarm intelligence metaheuristic inspired by the foraging behavior of some ant species. Ant colony optimization has been successfully applied to challenging optimization problems. This article investigates existing ant colony optimization algorithms specifically designed for combinatorial optimization problems with a dynamic environment. The investigated algorithms are classified into two frameworks: evaporation-based and population-based. A case study of using these algorithms to solve the dynamic traveling salesperson problem is described. Experiments are systematically conducted using a proposed dynamic benchmark framework to analyze the effect of important ant colony optimization features on numerous test cases. Different performance measures are used to evaluate the adaptation capabilities of the investigated algorithms, indicating which features are the most important when designing ant colony optimization algorithms in dynamic environments.
Index Terms: Heuristic algorithms, Ant colony optimization, Classification algorithms, Particle swarm optimization, Benchmark testing
IEEE Xplore Link: https://ieeexplore.ieee.org/document/8957215