Special Issue "Entropy in Metaheuristics and Bioinspired Algorithms"

A special issue of Entropy (ISSN 1099-4300).

Deadline for manuscript submissions: 30 September 2020.

Special Issue Editors

Dr. Diego Oliva
Website
Guest Editor
Depto. de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara, Jalisco, México
Interests: metaheuristic algorithms; bioinspired computation; image processing; machine learning; optimization
Special Issues and Collections in MDPI journals
Dr. Salvador Miguel Hinojosa Cervantes

Guest Editor
Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Av. Revolución, 1500, Guadalajara, Jal, Mexico
Interests: image processing; bioinspired computation; multi-objective optimization; machine learning
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Metaheuristics and bioinspired algorithms are used in different fields of science and technology. They are important optimization tools that commonly are based on populations to explore the solutions to complex problems. Different methods are also used for the classification of approximation using different learning rules extracted from nature.

Entropy is a powerful tool that has changed the analysis of information. The use of entropy has been extended in metaheuristics and bioinspired algorithms from measuring uncertainty to helping to explore and exploit search spaces in optimization. Different kinds of entropy are used depending on what is required. Moreover, in the information era, it is necessary to use metaheuristics and bioinspired methods to provide accurate solutions to complex problems. Hybrid algorithms are also important; they merge skills from different approaches and make decisions based on different rules to accurately explore the possible solutions.

Since the fields of metaheuristics and bioinspired algorithms are constantly growing, it is complicated to follow all the branches where they are combined with entropy. Considering the above, this Special Issue aims to present the latest advances in metaheuristics and bioinspired algorithms that employ or solve problems where entropy is included. It also seeks to include literature reviews and surveys on related topics.

Dr. Diego Oliva
Dr. Salvador Miguel Hinojosa Cervantes
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 papers will be 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 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

  • metaheuristics
  • bioinspired algorithms
  • cross entropy
  • Shannon entropy
  • fuzzy entropy
  • machine learning
  • neural networks
  • swarm optimization
  • evolutionary computation

Published Papers (1 paper)

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Research

Open AccessArticle
Multi-Level Image Thresholding Based on Modified Spherical Search Optimizer and Fuzzy Entropy
Entropy 2020, 22(3), 328; https://doi.org/10.3390/e22030328 - 12 Mar 2020
Abstract
Multi-level thresholding is one of the effective segmentation methods that have been applied in many applications. Traditional methods face challenges in determining the suitable threshold values; therefore, metaheuristic (MH) methods have been adopted to solve these challenges. In general, MH methods had been [...] Read more.
Multi-level thresholding is one of the effective segmentation methods that have been applied in many applications. Traditional methods face challenges in determining the suitable threshold values; therefore, metaheuristic (MH) methods have been adopted to solve these challenges. In general, MH methods had been proposed by simulating natural behaviors of swarm ecosystems, such as birds, animals, and others. The current study proposes an alternative multi-level thresholding method based on a new MH method, a modified spherical search optimizer (SSO). This was performed by using the operators of the sine cosine algorithm (SCA) to enhance the exploitation ability of the SSO. Moreover, Fuzzy entropy is applied as the main fitness function to evaluate the quality of each solution inside the population of the proposed SSOSCA since Fuzzy entropy has established its performance in literature. Several images from the well-known Berkeley dataset were used to test and evaluate the proposed method. The evaluation outcomes approved that SSOSCA showed better performance than several existing methods according to different image segmentation measures. Full article
(This article belongs to the Special Issue Entropy in Metaheuristics and Bioinspired Algorithms)
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