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Selected Feature Papers from 13th International Conference on Swarm Intelligence (ICSI)

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: closed (15 December 2022) | Viewed by 4010

Special Issue Editors


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Guest Editor
School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China
Interests: swarm intelligence; swarm intelligence optimization algorithm; fireworks algorithm; swarm robotics; machine learning and data mining
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Automation Science and Engineering, Xi’an Jiaotong University, No. 28 Xianning West Road, Xi’an 710049, China
Interests: multi-agent learning; intelligence computation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Swarm intelligence (SI) is an important concept in artificial intelligence and computer science with emergent properties. SI is one of the most effective methods to confront modern complex problems in the real world. A lot of novel algorithms are emerging. ICSI continues to provide the best communication opportunities for SI researchers and practitioners.

The Thirteenth International Conference on Swarm Intelligence (ICSI'2022) will be held in Xi'an, China, aiming to serve as a forum for researchers and practitioners to exchange the latest advances in theories, technologies, and applications of swarm intelligence and related areas. We encourage authors who present an article at the 2022 International Conference on Swarm Intelligence (ICSI) and who feel that their contribution is within the scope of interest of the journal Entropy to submit an original and essential extension of the ICSI paper to be considered for publication.

Prof. Dr. Ying Tan
Prof. Dr. Liangjun Ke
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Swarm intelligence
  • Swarm intelligence optimization algorithm
  • Multi-objective optimization
  • Swarm robotics
  • Evolutionary computing
  • Differential evolution
  • Swarm computing
  • Artificial life
  • Social evolution
  • Cooperative theories
  • Multi-agent system and theory
  • Natural computing
  • Collective/social intelligence
  • Evolving intelligence
  • Machine learning
  • Data mining
  • Reinforcement learning

Published Papers (1 paper)

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Research

44 pages, 2153 KiB  
Article
AutoMH: Automatically Create Evolutionary Metaheuristic Algorithms Using Reinforcement Learning
by Boris Almonacid
Entropy 2022, 24(7), 957; https://doi.org/10.3390/e24070957 - 10 Jul 2022
Cited by 3 | Viewed by 2919
Abstract
Machine learning research has been able to solve problems in multiple domains. Machine learning represents an open area of research for solving optimisation problems. The optimisation problems can be solved using a metaheuristic algorithm, which can find a solution in a reasonable amount [...] Read more.
Machine learning research has been able to solve problems in multiple domains. Machine learning represents an open area of research for solving optimisation problems. The optimisation problems can be solved using a metaheuristic algorithm, which can find a solution in a reasonable amount of time. However, the time required to find an appropriate metaheuristic algorithm, that would have the convenient configurations to solve a set of optimisation problems properly presents a problem. The proposal described in this article contemplates an approach that automatically creates metaheuristic algorithms given a set of optimisation problems. These metaheuristic algorithms are created by modifying their logical structure via the execution of an evolutionary process. This process employs an extension of the reinforcement learning approach that considers multi-agents in their environment, and a learning agent composed of an analysis process and a process of modification of the algorithms. The approach succeeded in creating a metaheuristic algorithm that managed to solve different continuous domain optimisation problems from the experiments performed. The implications of this work are immediate because they describe a basis for the generation of metaheuristic algorithms in an online-evolution. Full article
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