Biology-Inspired Algorithms and optimization

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 1467

Special Issue Editor

Institute of Informational Technologies, Federal State Budget Educational Institution of Higher Education, MIREA—Russian Technological University, 78, Vernadsky Avenue, 119454 Moscow, Russia
Interests: population-based optimization algorithms; applied mathematics; machine learning; deep learning; data mining; fuzzy set theory; classification; pattern recognition; algorithms
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Special Issue Information

Dear Colleagues,

We invite you to present your latest research in solving various applied optimization problems by using bioinspired algorithms.

We are looking for both new and innovative bioinspired optimization algorithms, as well as new areas of their application.

High-quality papers are solicited to address both theoretical and practical issues of applying bioinspired optimization algorithms.

Submissions are welcome for both traditional optimization problems and new applications.

Potential topics include, but are not limited to, solving single- or multi-objective optimization problems, research into fine-tuning the parameters of various models and systems, as well as a broad spectrum of planning, layout, and placement problems in traditional and new applications.

Prof. Dr. Liliya Demidova
Guest Editor

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. Algorithms 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 1600 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

  • bio-inspired and swarm intelligence
  • optimization techniques
  • decision making support
  • data mining
  • knowledge discovery
  • pattern recognition
  • machine learning
  • artificial neural networks

Published Papers (1 paper)

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Research

15 pages, 318 KiB  
Article
Application of the Parabola Method in Nonconvex Optimization
by Anton Kolosnitsyn, Oleg Khamisov, Eugene Semenkin and Vladimir Nelyub
Algorithms 2024, 17(3), 107; https://doi.org/10.3390/a17030107 - 01 Mar 2024
Viewed by 606
Abstract
We consider the Golden Section and Parabola Methods for solving univariate optimization problems. For multivariate problems, we use these methods as line search procedures in combination with well-known zero-order methods such as the coordinate descent method, the Hooke and Jeeves method, and the [...] Read more.
We consider the Golden Section and Parabola Methods for solving univariate optimization problems. For multivariate problems, we use these methods as line search procedures in combination with well-known zero-order methods such as the coordinate descent method, the Hooke and Jeeves method, and the Rosenbrock method. A comprehensive numerical comparison of the obtained versions of zero-order methods is given in the present work. The set of test problems includes nonconvex functions with a large number of local and global optimum points. Zero-order methods combined with the Parabola method demonstrate high performance and quite frequently find the global optimum even for large problems (up to 100 variables). Full article
(This article belongs to the Special Issue Biology-Inspired Algorithms and optimization)
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