Special Issue "Metaheuristics and Machine Learning: Theory and Applications"

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 May 2022) | Viewed by 1667

Special Issue Editors

Prof. Dr. Essam H. Houssein
E-Mail Website
Guest Editor
Faculty of Computers and Information, Minia University, 61519 Minia, Egypt
Interests: metaheuristics; artificial intelligence; optimization, IoT and WSNs; data mining; image processing
Prof. Dr. Hegazy Rezk
E-Mail Website
Guest Editor
College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, 11911 Al-Kharj, Saudi Arabia
Interests: renewable energy; smart grid; hybrid systems; power electronics; optimization and artificial intelligence
Prof. Dr. Diego Oliva
E-Mail Website
Guest Editor
Depto. De Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, 44430, Guadalajara, Jal., Mexico
Interests: metaheuristics; bioinspired computation; image processing; machine learning; optimization
Prof. Dr. Salah Kamel
E-Mail Website
Guest Editor
Department of Electrical Engineering, Faculty of Engineering, Aswan University, 81542 Aswan, Egypt
Interests: power systems; metaheuristics; renewable energy; microgrids

Special Issue Information

Dear Colleagues,

In recent years, metaheuristics (MHs) have become essential tools for solving challenging optimization problems encountered in industry, engineering, biomedical, image processing, and the theoretical field. Several different metaheuristics exist, and new ones are under constant development. One of the most fundamental principles in our world is the search for an optimal state. Therefore, there exist a diverse range of MHs have been used for many years in the formulation and solution of computational problems. This special issue brings together outstanding research and recent developments in metaheuristics (MHs), Machine Learning (ML), and their applications in the industrial world. Therefore, recently, MHs have been combined with several ML techniques to deal with different global and engineering optimization problems, also real-world applications. Papers published in this special issue describe original works in different topics in science and engineering, such as Metaheuristics, Ariticial Intillegence, Machine learning, Soft Computing, Neural Networks, Multi-criteria decision-making, Energy efficiency, Sustainable development, etc.

Recommended Topics

The topics covered by this book will present a collection of high-quality research works written by renowned leaders in the field. We invite all researchers and practitioners to develop algorithms, systems, and applications, to share their results, ideas, and experiences. Topics of interest include, but are not limited to, the following:

▪ Hybrid and Parallelization Metaheuristics
▪ Multi-objective optimization
▪ Multilevel segmentation and Image processing
▪ Feature selection
▪ Reinforcement learning and Supervised learning
▪ Pattern recognition
▪ Ariticial Intillegence
▪ Fuzzy systems
▪ Data mining
▪ Computer vision
▪ Quantum Optimization
▪ Bioinformatics and Biomedical applications
▪ Engineering applications

Prof. Dr. Essam H. Houssein
Prof. Dr. Hegazy Rezk
Prof. Dr. Diego Oliva
Prof. Dr. Salah Kamel
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. 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 1400 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
  • optimization
  • convex optimization problem
  • constrained optimization problem
  • artificial Intelligence
  • machine learning
  • real-world applications

Published Papers (1 paper)

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Research

Article
A Reward Population-Based Differential Genetic Harmony Search Algorithm
Algorithms 2022, 15(1), 23; https://doi.org/10.3390/a15010023 - 14 Jan 2022
Cited by 2 | Viewed by 895
Abstract
To overcome the shortcomings of the harmony search algorithm, such as its slow convergence rate and poor global search ability, a reward population-based differential genetic harmony search algorithm is proposed. In this algorithm, a population is divided into four ordinary sub-populations and one [...] Read more.
To overcome the shortcomings of the harmony search algorithm, such as its slow convergence rate and poor global search ability, a reward population-based differential genetic harmony search algorithm is proposed. In this algorithm, a population is divided into four ordinary sub-populations and one reward sub-population, for each of which the evolution strategy of the differential genetic harmony search is used. After the evolution, the population with the optimal average fitness is combined with the reward population to produce a new reward population. During an experiment, tests were conducted first on determining the value of the harmony memory size (HMS) and the harmony memory consideration rate (HMCR), followed by an analysis of the effect of their values on the performance of the proposed algorithm. Then, six benchmark functions were selected for the experiment, and a comparison was made on the calculation results of the standard harmony memory search algorithm, reward population harmony search algorithm, differential genetic harmony algorithm, and reward population-based differential genetic harmony search algorithm. The result suggests that the reward population-based differential genetic harmony search algorithm has the merits of a strong global search ability, high solving accuracy, and satisfactory stability. Full article
(This article belongs to the Special Issue Metaheuristics and Machine Learning: Theory and Applications)
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