Special Issue "Advances of Metaheuristic Computation"

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

Deadline for manuscript submissions: 31 August 2020.

Special Issue Editors

Prof. Dr. Erik Valdemar Cuevas
Guest Editor
Department of Electronics, Universidad de Guadalajara, Av.Revolucion 1500, Guadalajara, Mexico
Interests: computer vision; evolutionary computation; artificial intelligence; bio-inspired computation
Special Issues and Collections in MDPI journals
Prof. Dr. Francisco G. Montoya
Guest Editor
Dr. Alfredo Alcayde
Guest Editor
Department of Engineering, Universidad de Almería; La Cañada de San Urbano s/n; 04120 Almería, Spain
Interests: electrical engineering; power systems; renewable energy; optimization
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Metaheuristic computation is one of the most important emerging technologies of recent times. Over the last few years, there has been an exponential growth of research activity in this field. Despite the fact that the concept itself has not been precisely defined, metaheuristic methods have become the standard term that encompasses several stochastic, population-based, and system-inspired approaches.

Metaheuristic schemes use as inspiration our scientific understanding of biological, natural, or social systems, which at some level of abstraction can be represented as optimization processes. They are intended to serve as general-purpose easy-to-use optimization techniques capable of reaching globally optimal or at least nearly optimal solutions. Some common features clearly appear in most of the metaheuristic approaches, such as the use of diversification to force the exploration of regions of the search space, rarely visited until now, and the use of intensification or exploitation, to investigate thoroughly some promising regions. Another common feature is the use of memory to archive the best solutions encountered. Due to their robustness, metaheuristic techniques are well-suited options for industrial and real-world tasks. They do not need gradient information and they can operate on each kind of parameter space (continuous, discrete, combinatorial, or even mixed variants). Essentially, the credibility of metaheuristic algorithms relies on their ability to solve difficult, real-world problems reaching a better performance in terms of accuracy and robustness.

This Special Issue aims to provide a collection of high-quality research articles that address broad challenges in both theoretical and application aspects of metaheuristic algorithms. We invite colleagues to contribute original research articles as well as review articles that will stimulate the continuing effort on metaheuristic approaches to solving problems in different domains. In the Special Issue, the contributions are mainly divided into two groups: (A) foundations, improvements, or hybrid approaches and (B) applications. Potential topics for this Special Issue include, but are not limited to:

(A) Foundations, improvements or hybrid approaches:

- Analysis or comparison of metaheuristic methods (single or multi-objective)

- New stochastic search strategies

- Enhanced versions of existent metaheuristic schemes (single or multi-objective)

- New metaheuristic techniques generated through the combination of different paradigms

(B) Applications:

- In communications

- In control processes

- In decision making

- In signal and image processing

- In power systems

Dr. Erik Cuevas
Prof. Dr. Francisco G. Montoya
Dr. Alfredo Alcayde
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. Mathematics 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 1200 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.

Published Papers (1 paper)

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Open AccessArticle
Multi-Objective Optimization Benchmarking Using DSCTool
Mathematics 2020, 8(5), 839; https://doi.org/10.3390/math8050839 - 22 May 2020
By performing data analysis, statistical approaches are highly welcome to explore the data. Nowadays with the increases in computational power and the availability of big data in different domains, it is not enough to perform exploratory data analysis (descriptive statistics) to obtain some [...] Read more.
By performing data analysis, statistical approaches are highly welcome to explore the data. Nowadays with the increases in computational power and the availability of big data in different domains, it is not enough to perform exploratory data analysis (descriptive statistics) to obtain some prior insights from the data, but it is a requirement to apply higher-level statistics that also require much greater knowledge from the user to properly apply them. One research area where proper usage of statistics is important is multi-objective optimization, where the performance of a newly developed algorithm should be compared with the performances of state-of-the-art algorithms. In multi-objective optimization, we are dealing with two or more usually conflicting objectives, which result in high dimensional data that needs to be analyzed. In this paper, we present a web-service-based e-Learning tool called DSCTool that can be used for performing a proper statistical analysis for multi-objective optimization. The tool does not require any special statistics knowledge from the user. Its usage and the influence of a proper statistical analysis is shown using data taken from a benchmarking study performed at the 2018 IEEE CEC (The IEEE Congress on Evolutionary Computation) is appropriate. Competition on Evolutionary Many-Objective Optimization. Full article
(This article belongs to the Special Issue Advances of Metaheuristic Computation)
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