Special Issue "Metaheuristic Algorithms in Engineering Optimization Problems"

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 (20 October 2020).

Special Issue Editors

Dr. Daniel Gutiérrez Reina
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Guest Editor
Electronic Engineering Department, University of Seville, Calle San Fernando, 4, 41004 Sevilla, Spain
Interests: multi-hop networks; sensor networks; VANETs; FANETs; evolutionary computation; machine learning; deep learning
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Dr. Vishal Sharma
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Guest Editor
Information Security Engineering Department, Soonchunhyang University, South Korea,
Interests: UAV communications; 5G networks; drone security; estimation and prediction theory; blockchain; statistics and data analytics
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Dr. Kathiravan Srinivasan
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Guest Editor
School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
Interests: machine learning; communication systems and networks; multimedia and computer vision; artificial intelligence; data science; wireless networks
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

At present, many engineering optimization problems cannot be solved by traditional methods based on gradient. Several reasons make traditional methods unsuitable for complex engineering problems, such as usage of derivatives that are not available in simulation-based systems, where a mathematical formulation is difficult, and also poor performance in nonconvex landscapes, where local minimum/maximum can stop the optimization algorithm. In the last decade, new meta-heuristic algorithms have arisen, such as Firefly Algorithm, Harmonic Search, and Bat Optimization, among other approaches, that present significant performances in many engineering areas, such as telecommunications, robotics, mechanical design, and power systems, among others. Furthermore, multiobjective approaches like the ones based on Pareto dominance are appropriate for engineering applications, where normally, efficiency and cost performance metrics are counterbalanced.

This Special Issue pursues both novel metaheuristic algorithms and the application of existing metaheuristic approaches in engineering problems. Since abundant liteturate can be found in some engineering areas, both surveys and literature reviews are welcome.

The possible topics of interest include but are not limited to the following areas:

  • Genetic Algorithm (GA) for engineering optimization problems;
  • Swarm Optimization Algorithms (PSO, Firefly, Ant Colony, etc.) for engineering optimization problems;
  • Bio-inspired optimization algorithms for engineering optimization problems;
  • Genetic programing for engineering optimization problems;
  • Evolutionary strategies for engineering optimization problems;
  • Multiobjective optimization for engineering optimization problems;
  • Evolutionary algorithms based on subrogate models for engineering optimization problems;
  • Hybrid metaheuristic algorithms for engineering optimization problems;
  • Parallel metaheuristic algorithms for engineering optimization problems;
  • Combination of machine learning approaches and metaheuristic algorithms for engineering optimization problems;
  • Application of metaheuristic algorithms for adjusting the hyperparameter of Deep Learning models applied to engineering problems.

Dr. Daniel Gutiérrez Reina
Dr. Kathiravan Srinivasan
Dr. vishal sharma
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 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. 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 1000 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

  • Evolutionary computation
  • Bio-inspired optimization
  • Swarm optimization
  • Machine learning
  • Genetic programming

Published Papers (1 paper)

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Research

Open AccessArticle
Study on Multi-Objective Optimization-Based Climate Responsive Design of Residential Building
Algorithms 2020, 13(9), 238; https://doi.org/10.3390/a13090238 - 21 Sep 2020
Abstract
This paper proposes an optimization process based on a parametric platform for building climate responsive design. Taking residential buildings in six typical American cities as examples, it proposes thermal environment comfort (Discomfort Hour, DH), building energy demand (BED) and building global cost (GC) [...] Read more.
This paper proposes an optimization process based on a parametric platform for building climate responsive design. Taking residential buildings in six typical American cities as examples, it proposes thermal environment comfort (Discomfort Hour, DH), building energy demand (BED) and building global cost (GC) as the objective functions for optimization. The design variables concern building orientation, envelope components, and window types, etc. The optimal solution is provided from two different perspectives of the public sector (energy saving optimal) and private households (cost-optimal) respectively. By comparing the optimization results with the performance indicators of the reference buildings in various cities, the outcome can give the precious indications to rebuild the U.S. residential buildings with a view to energy-efficiency and cost optimality depending on the location. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms in Engineering Optimization Problems)
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