Special Issue "Evolutionary Algorithms in Engineering Design Optimization"

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

Deadline for manuscript submissions: 30 June 2021.

Special Issue Editors

Dr. David Greiner
E-Mail Website
Guest Editor
Institute of Intelligent Systems and Numerical Applications in Engineering, Universidad de Las Palmas de Gran Canaria, 35017, Spain
Interests: structural optimization; design optimization; multi-objective optimization; evolutionary algorithms; computational mechanics
Prof. Dr. António Gaspar‐Cunha
E-Mail Website
Guest Editor
Institute of Polymers and Composites (IPC), University of Minho, Guimarães, Portugal
Interests: multi-objective optimization; multidisciplinary optimization; decision making; robustness of the solutions; engineering optimization
Assoc. Prof. Dr. Daniel Hernández-Sosa
E-Mail Website
Guest Editor
Institute of Intelligent Systems and Numerical Applications in Engineering,Universidad de Las Palmas de Gran Canaria, 35017, Spain
Interests: autonomous robotics; autonomous underwater vehicles; multi-objective optimization; evolutionary algorithms for path-planning and obstacle avoidance
Assoc. Prof. Dr. Edmondo Minisci
E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, University of Strathclyde, Glasgow, United Kingdom
Interests: multidisciplinary design optimization; optimization under uncertainty; nature inspired algorithms; engineering optimization; aerospace engineering; surrogate based optimization
Assoc. Prof. Dr. Aleš Zamuda
E-Mail Website
Guest Editor
Faculty of Electrical Engineering and Computer Science, University of Maribor, Slovenia
Interests: differential evolution; multiobjective optimization; evolutionary robotics; artificial life; cloud computing

Special Issue Information

Dear Colleagues,

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
Guest Editors

Manuscript Submission Information

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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

Published Papers (7 papers)

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Research

Open AccessArticle
A Comparison of Archiving Strategies for Characterization of Nearly Optimal Solutions under Multi-Objective Optimization
Mathematics 2021, 9(9), 999; https://doi.org/10.3390/math9090999 - 28 Apr 2021
Viewed by 190
Abstract
In a multi-objective optimization problem, in addition to optimal solutions, multimodal and/or nearly optimal alternatives can also provide additional useful information for the decision maker. However, obtaining all nearly optimal solutions entails an excessive number of alternatives. Therefore, to consider the nearly optimal [...] Read more.
In a multi-objective optimization problem, in addition to optimal solutions, multimodal and/or nearly optimal alternatives can also provide additional useful information for the decision maker. However, obtaining all nearly optimal solutions entails an excessive number of alternatives. Therefore, to consider the nearly optimal solutions, it is convenient to obtain a reduced set, putting the focus on the potentially useful alternatives. These solutions are the alternatives that are close to the optimal solutions in objective space, but which differ significantly in the decision space. To characterize this set, it is essential to simultaneously analyze the decision and objective spaces. One of the crucial points in an evolutionary multi-objective optimization algorithm is the archiving strategy. This is in charge of keeping the solution set, called the archive, updated during the optimization process. The motivation of this work is to analyze the three existing archiving strategies proposed in the literature (ArchiveUpdatePQ,ϵDxy, Archive_nevMOGA, and targetSelect) that aim to characterize the potentially useful solutions. The archivers are evaluated on two benchmarks and in a real engineering example. The contribution clearly shows the main differences between the three archivers. This analysis is useful for the design of evolutionary algorithms that consider nearly optimal solutions. Full article
(This article belongs to the Special Issue Evolutionary Algorithms in Engineering Design Optimization)
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Open AccessArticle
Assessment of Optimization Methods for Aeroacoustic Prediction of Trailing-Edge Interaction Noise in Axisymmetric Jets
Mathematics 2021, 9(9), 998; https://doi.org/10.3390/math9090998 - 28 Apr 2021
Viewed by 188
Abstract
Our concern in this paper is in the fine-tuning of the arbitrary parameters within the upstream turbulence structure for the acoustic spectrum of a rapid-distortion theory (RDT)-based model of trailing-edge noise. RDT models are based on an appropriate asymptotic limit of the Linearized [...] Read more.
Our concern in this paper is in the fine-tuning of the arbitrary parameters within the upstream turbulence structure for the acoustic spectrum of a rapid-distortion theory (RDT)-based model of trailing-edge noise. RDT models are based on an appropriate asymptotic limit of the Linearized Euler Equations and apply when the interaction time of the turbulence with the surface edge discontinuity is small compared to the eddy turnover time. When an arbitrary transversely sheared jet mean flow convects a finite region of nonhomogeneous turbulence, the acoustic spectrum of the pressure field scattered by the trailing-edge depends on (among other things) the upstream turbulence via the Fourier transform of the correlation function, R22 (where subscript 2 refers to a co-ordinate surface normal to the plate). We show that the length and time scale parameters that govern the spatial and temporal de-correlation of R22 can be found using formal optimization methods to avoid any uncertainty in their selection by hand-tuning. We assess various optimization methods that are broadly categorized into an ‘evolutionary’ and ‘non-evolutionary’ paradigm. That is, we optimize the acoustic spectrum using the Multi-Start algorithm, Particle Swarm Optimization and the Multi-Population Adaptive Inflationary Differential Evolution Algorithm. The optimization is based upon different objective functions for the acoustic spectrum and/or turbulence structure. We show that this approach, while resulting in the total modest increase in computation time (on average 2 h), gives excellent prediction over most frequencies (within 2–4 dB) where the trailing-edge noise associated amplification in sound exists. Full article
(This article belongs to the Special Issue Evolutionary Algorithms in Engineering Design Optimization)
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Open AccessArticle
A Simple Proposal for Including Designer Preferences in Multi-Objective Optimization Problems
Mathematics 2021, 9(9), 991; https://doi.org/10.3390/math9090991 - 28 Apr 2021
Viewed by 209
Abstract
Including designer preferences in every phase of the resolution of a multi-objective optimization problem is a fundamental issue to achieve a good quality in the final solution. To consider preferences, the proposal of this paper is based on the definition of what we [...] Read more.
Including designer preferences in every phase of the resolution of a multi-objective optimization problem is a fundamental issue to achieve a good quality in the final solution. To consider preferences, the proposal of this paper is based on the definition of what we call a preference basis that shows the preferred optimization directions in the objective space. Associated to this preference basis a new basis in the objective space—dominance basis—is computed. With this new basis the meaning of dominance is reinterpreted to include the designer’s preferences. In this paper, we show the effect of changing the geometric properties of the underlying structure of the Euclidean objective space by including preferences. This way of incorporating preferences is very simple and can be used in two ways: by redefining the optimization problem and/or in the decision-making phase. The approach can be used with any multi-objective optimization algorithm. An advantage of including preferences in the optimization process is that the solutions obtained are focused on the region of interest to the designer and the number of solutions is reduced, which facilitates the interpretation and analysis of the results. The article shows an example of the use of the preference basis and its associated dominance basis in the reformulation of the optimization problem, as well as in the decision-making phase. Full article
(This article belongs to the Special Issue Evolutionary Algorithms in Engineering Design Optimization)
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Open AccessArticle
Detailed Study on the Behavior of Improved Beam T-Junctions Modeling for the Characterization of Tubular Structures, Based on Artificial Neural Networks Trained with Finite Element Models
Mathematics 2021, 9(9), 943; https://doi.org/10.3390/math9090943 - 23 Apr 2021
Viewed by 175
Abstract
The actual behavior of welded T-junctions in tubular structures depends strongly on the topology of the junction at the joint level. In finite element analysis, beam-type elements are usually employed due to their simplicity and low computational cost, even though they cannot reproduce [...] Read more.
The actual behavior of welded T-junctions in tubular structures depends strongly on the topology of the junction at the joint level. In finite element analysis, beam-type elements are usually employed due to their simplicity and low computational cost, even though they cannot reproduce the joints topologies and characteristics. To adjust their behavior to a more realistic situation, elastic elements can be introduced at the joint level, whose characteristics must be determined through costly validations. This paper studies the optimization and implementation of the validation data, through the creation of an optimal surrogate model based on neural networks, leading to a model that predicts the stiffness of elastic elements, introduced at the joint level based on available data. The paper focuses on how the neural network should be chosen, when training data is very limited and, more importantly, which of the available data should be used for training and which for verification. The methodology used is based on a Monte Carlo analysis that allows an exhaustive study of both the network parameters and the distribution and choice of the limited data in the training set to optimize its performance. The results obtained indicate that the use of neural networks without a careful methodology in this type of problems could lead to inaccurate results. It is also shown that a conscientious choice of training data, among the data available in the problem of choice of elastic parameters for T-junctions in finite elements, is fundamental to achieve functional surrogate models. Full article
(This article belongs to the Special Issue Evolutionary Algorithms in Engineering Design Optimization)
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Open AccessArticle
Population Diversity Control of Genetic Algorithm Using a Novel Injection Method for Bankruptcy Prediction Problem
Mathematics 2021, 9(8), 823; https://doi.org/10.3390/math9080823 - 10 Apr 2021
Viewed by 297
Abstract
Exploration and exploitation are the two main concepts of success for searching algorithms. Controlling exploration and exploitation while executing the search algorithm will enhance the overall performance of the searching algorithm. Exploration and exploitation are usually controlled offline by proper settings of parameters [...] Read more.
Exploration and exploitation are the two main concepts of success for searching algorithms. Controlling exploration and exploitation while executing the search algorithm will enhance the overall performance of the searching algorithm. Exploration and exploitation are usually controlled offline by proper settings of parameters that affect the population-based algorithm performance. In this paper, we proposed a dynamic controller for one of the most well-known search algorithms, which is the Genetic Algorithm (GA). Population Diversity Controller-GA (PDC-GA) is proposed as a novel feature-selection algorithm to reduce the search space while building a machine-learning classifier. The PDC-GA is proposed by combining GA with k-mean clustering to control population diversity through the exploration process. An injection method is proposed to redistribute the population once 90% of the solutions are located in one cluster. A real case study of a bankruptcy problem obtained from UCI Machine Learning Repository is used in this paper as a binary classification problem. The obtained results show the ability of the proposed approach to enhance the performance of the machine learning classifiers in the range of 1% to 4%. Full article
(This article belongs to the Special Issue Evolutionary Algorithms in Engineering Design Optimization)
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Open AccessArticle
A Two-Stage Mono- and Multi-Objective Method for the Optimization of General UPS Parallel Manipulators
Mathematics 2021, 9(5), 543; https://doi.org/10.3390/math9050543 - 04 Mar 2021
Viewed by 327
Abstract
This paper introduces a two-stage method based on bio-inspired algorithms for the design optimization of a class of general Stewart platforms. The first stage performs a mono-objective optimization in order to reach, with sufficient dexterity, a regular target workspace while minimizing the elements’ [...] Read more.
This paper introduces a two-stage method based on bio-inspired algorithms for the design optimization of a class of general Stewart platforms. The first stage performs a mono-objective optimization in order to reach, with sufficient dexterity, a regular target workspace while minimizing the elements’ lengths. For this optimization problem, we compare three bio-inspired algorithms: the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO), and the Boltzman Univariate Marginal Distribution Algorithm (BUMDA). The second stage looks for the most suitable gains of a Proportional Integral Derivative (PID) control via the minimization of two conflicting objectives: one based on energy consumption and the tracking error of a target trajectory. To this effect, we compare two multi-objective algorithms: the Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) and Non-dominated Sorting Genetic Algorithm-III (NSGA-III). The main contributions lie in the optimization model, the proposal of a two-stage optimization method, and the findings of the performance of different bio-inspired algorithms for each stage. Furthermore, we show optimized designs delivered by the proposed method and provide directions for the best-performing algorithms through performance metrics and statistical hypothesis tests. Full article
(This article belongs to the Special Issue Evolutionary Algorithms in Engineering Design Optimization)
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Open AccessArticle
Fractional Order PID Controller Design for an AVR System Using Chaotic Yellow Saddle Goatfish Algorithm
Mathematics 2020, 8(7), 1182; https://doi.org/10.3390/math8071182 - 18 Jul 2020
Cited by 4 | Viewed by 942
Abstract
This paper presents a novel method for optimal tunning of a Fractional Order Proportional-Integral-Derivative (FOPID) controller for an Automatic Voltage Regulator (AVR) system. The presented method is based on the Yellow Saddle Goatfish Algorithm (YSGA), which is improved with Chaotic Logistic Maps. Additionally, [...] Read more.
This paper presents a novel method for optimal tunning of a Fractional Order Proportional-Integral-Derivative (FOPID) controller for an Automatic Voltage Regulator (AVR) system. The presented method is based on the Yellow Saddle Goatfish Algorithm (YSGA), which is improved with Chaotic Logistic Maps. Additionally, a novel objective function for the optimization of the FOPID parameters is proposed. The performance of the obtained FOPID controller is verified by comparison with various FOPID controllers tuned by other metaheuristic algorithms. A comparative analysis is performed in terms of step response, frequency response, root locus, robustness test, and disturbance rejection ability. Results of the simulations undoubtedly show that the FOPID controller tuned with the proposed Chaotic Yellow Saddle Goatfish Algorithm (C-YSGA) outperforms FOPID controllers tuned by other algorithms, in all of the previously mentioned performance tests. Full article
(This article belongs to the Special Issue Evolutionary Algorithms in Engineering Design Optimization)
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