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Submit excellent papers to Swarm and Evolutionary Computation.

  • Associate Editor: A. Zamuda

Special Issue: “Evolutionary Algorithms in Engineering Design Optimization”

“Mathematics – MDPI” Journal (Web of Science indexed JCR Q1)

https://www.mdpi.com/journal/mathematics/special_issues/Evolutionary_Algorithms_Engineering_Design_Optimization

 

Submissions already open, until 31 March, 2021

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Editors:
David Greiner, Universidad de Las Palmas de Gran Canaria, Spain
Antonio Gaspar-Cunha, University of Minho, Portugal
Daniel Hernández-Sosa, Universidad de Las Palmas de Gran Canaria, Spain
Edmondo Minisci, University of Strathclyde, United Kingdom
Ales Zamuda, University of Maribor, Slovenia

Summary:
Evolutionary algorithms (EAs) are population-based global optimizers, which, due to their characteristics, allowed to solve, in a straightforward way, many real world optimization problems in the last three decades, particularly, in engineering field.
Their main advantages comprise: not requiring any requisite to the objective / fitness evaluation function (e.g., continuity, derivability, convexity, etc.); they are not limited by the appearance of discrete and/or mixed variables, neither by the requirement of uncertainty quantification in the search. Moreover, they can deal with more than one objective function simultaneously through the use of evolutionary multi-objective optimization algorithms. This set of advantages, and the continuously increased computing capability of modern computers, has enhanced their application in research and industry.
From the application point of view, in this special issue proposal, all engineering fields are welcomed, such as, e.g., aerospace and aeronautical, biomedical, chemical and materials science, civil, electronic and telecommunications, energy and electrical, manufacturing, logistics and transportation, mechanical, naval architecture, reliability, robotics, structural, etc.
From the EAs point of view, the integration of innovative and improvement aspects in the algorithms (e.g., genetic algorithms, differential evolution, evolution strategies, etc.) for solving real world engineering design problems, in the abovementioned application fields, are welcomed and encouraged, such as: parallel EAs, surrogate modeling, hybridization with other optimization techniques, multi-objective and many-objective optimization, etc.

Keywords:
decision making, design optimization, engineering design, engineering optimization, evolutionary algorithms, multidisciplinary optimization, multi-objective optimization, optimum design, optimization in aerospace, optimization under uncertainty, robustness of the solutions, surrogate based optimization

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