Special Issue "Evolutionary Algorithms in Engineering Design Optimization"
Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 11242
A printed edition of this Special Issue is available here.
Interests: structural optimization; design optimization; multi-objective optimization; evolutionary algorithms; computational mechanics
Interests: multi-objective optimization; multidisciplinary optimization; decision making; robustness of the solutions; engineering optimization
Interests: autonomous robotics; autonomous underwater vehicles; multi-objective optimization; evolutionary algorithms for path-planning and obstacle avoidance
Interests: multidisciplinary design optimization; optimization under uncertainty; nature inspired algorithms; engineering optimization; aerospace engineering; surrogate based optimization
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 fields. Their main advantages are comprised of the following: they do not require 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 or 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 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.
Within the EA field, 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 the following: parallel EAs, surrogate modeling, hybridization with other optimization techniques, multi-objective and many-objective optimization, etc.
Assoc. Prof. Dr. David Greiner
Prof. Dr. António Gaspar‐Cunha
Assoc. Prof. Dr. Daniel Hernández-Sosa
Assoc. Prof. Dr. Edmondo Minisci
Assoc. Prof. Dr. Aleš Zamuda
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- 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