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Mathematics
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Natural Computing
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Natural Computing

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Network Science".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 2056

Special Issue Editors

Prof. Dr. Dragos Sburlan

E-Mail Website
Guest Editor
Faculty of Mathematics and Computer Science, Ovidius University, Constanta, Romania
Interests: artificial intelligence; evolutionary computation; time series; data mining
Dr. Elena Bautu

E-Mail Website
Co-Guest Editor
Faculty of Mathematics and Computer Science, Ovidius University, Constanta, Romania
Interests: artificial intelligence; evolutionary computation; time series; data mining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Natural computing is a broad field of research that has gained significant focus not only because many computing models inspired by natural phenomena have proved their usefulness in solving real complex problems, but also because natural media were used successfully as support for computation. The roots of natural computing draw their strength from both theoretical computer science and the natural sciences. The paradigm shift in computer science, widening its classical notion of computation towards embedding, using, or relying on natural phenomena, represents one of the most exciting flows in interdisciplinary research. Topics like cellular automata, neural computation, swarm intelligence, evolutionary computation, membrane and (bio)molecular computing, as well as quantum computing constitute major branches of natural computing. They regard the computational aspects of developmental processes happening at the level of living organisms/cells, how they communicate and collaborate to achieve a certain goal, how the brain processes “information”, or even how interactions between atomic particles can be employed to perform computations.

The Special Issue on “Natural Computing” aims to gather contributions related to nature-inspired models of computation, problem-solving from nature, algorithms, and applications. Authors are welcomed to contribute original high-quality papers from all fields related to natural computing.

Prof. Dr. Dragos Sburlan
Dr. Elena Bautu
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. Mathematics is an international peer-reviewed open access semimonthly 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

  • cellular automata
  • developmental systems
  • DNA Computing
  • evolutionary algorithms and computing
  • membrane and molecular computing
  • neural networks
  • quantum computing
  • soft computing
  • swarm intelligence
  • unconventional computing

Published Papers (1 paper)

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Research

35 pages, 428 KiB  
Article
Predominant Cognitive Learning Particle Swarm Optimization for Global Numerical Optimization
by Qiang Yang, Yufei Jing, Xudong Gao, Dongdong Xu, Zhenyu Lu, Sang-Woon Jeon and Jun Zhang
Mathematics 2022, 10(10), 1620; https://0-doi-org.brum.beds.ac.uk/10.3390/math10101620 - 10 May 2022
Cited by 18 | Viewed by 1486
Abstract
Particle swarm optimization (PSO) has witnessed giant success in problem optimization. Nevertheless, its optimization performance seriously degrades when coping with optimization problems with a lot of local optima. To alleviate this issue, this paper designs a predominant cognitive learning particle swarm optimization (PCLPSO) [...] Read more.
Particle swarm optimization (PSO) has witnessed giant success in problem optimization. Nevertheless, its optimization performance seriously degrades when coping with optimization problems with a lot of local optima. To alleviate this issue, this paper designs a predominant cognitive learning particle swarm optimization (PCLPSO) method to effectively tackle complicated optimization problems. Specifically, for each particle, a new promising exemplar is constructed by letting its personal best position cognitively learn from a better personal experience randomly selected from those of others based on a novel predominant cognitive learning strategy. As a result, different particles preserve different guiding exemplars. In this way, the learning effectiveness and the learning diversity of particles are expectedly improved. To eliminate the dilemma that PCLPSO is sensitive to the involved parameters, we propose dynamic adjustment strategies, so that different particles preserve different parameter settings, which is further beneficial to promote the learning diversity of particles. With the above techniques, the proposed PCLPSO could expectedly compromise the search intensification and diversification in a good way to search the complex solution space properly to achieve satisfactory performance. Comprehensive experiments are conducted on the commonly adopted CEC 2017 benchmark function set to testify the effectiveness of the devised PCLPSO. Experimental results show that PCLPSO obtains considerably competitive or even much more promising performance than several representative and state-of-the-art peer methods. Full article
(This article belongs to the Special Issue Natural Computing)
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