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: closed (30 June 2021) | Viewed by 11242

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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 Las Palmas de Gran Canaria, 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
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 Las Palmas de Gran Canaria, Spain
Interests: autonomous robotics; autonomous underwater vehicles; multi-objective optimization; evolutionary algorithms for path-planning and obstacle avoidance
Dr. Edmondo Minisci
E-Mail Website
Guest Editor
Intelligent Computational Engineering Laboratory (ICE-Lab), University of Strathclyde, Glasgow G11XJ, UK
Interests: multidisciplinary design optimization; optimization under uncertainty; nature inspired algorithms; engineering optimization; aerospace engineering; surrogate based optimization
Dr. Aleš Zamuda
E-Mail Website
Guest Editor
Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000 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

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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 (14 papers)

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Research

Article
Multi-Objective Optimisation under Uncertainty with Unscented Temporal Finite Elements
Mathematics 2021, 9(23), 3010; https://doi.org/10.3390/math9233010 - 24 Nov 2021
Viewed by 366
Abstract
This paper presents a novel method for multi-objective optimisation under uncertainty developed to study a range of mission trade-offs, and the impact of uncertainties on the evaluation of launch system mission designs. A memetic multi-objective optimisation algorithm, named MODHOC, which combines the Direct [...] Read more.
This paper presents a novel method for multi-objective optimisation under uncertainty developed to study a range of mission trade-offs, and the impact of uncertainties on the evaluation of launch system mission designs. A memetic multi-objective optimisation algorithm, named MODHOC, which combines the Direct Finite Elements in Time transcription method with Multi Agent Collaborative Search, is extended to account for model uncertainties. An Unscented Transformation is used to capture the first two statistical moments of the quantities of interest. A quantification model of the uncertainty was developed for the atmospheric model parameters. An optimisation under uncertainty was run for the design of descent trajectories for a spaceplane-based two-stage launch system. Full article
(This article belongs to the Special Issue Evolutionary Algorithms in Engineering Design Optimization)
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Article
Differential Evolution with Estimation of Distribution for Worst-Case Scenario Optimization
Mathematics 2021, 9(17), 2137; https://doi.org/10.3390/math9172137 - 02 Sep 2021
Viewed by 525
Abstract
Worst-case scenario optimization deals with the minimization of the maximum output in all scenarios of a problem, and it is usually formulated as a min-max problem. Employing nested evolutionary algorithms to solve the problem requires numerous function evaluations. This work proposes a differential [...] Read more.
Worst-case scenario optimization deals with the minimization of the maximum output in all scenarios of a problem, and it is usually formulated as a min-max problem. Employing nested evolutionary algorithms to solve the problem requires numerous function evaluations. This work proposes a differential evolution with an estimation of distribution algorithm. The algorithm has a nested form, where a differential evolution is applied for both the design and scenario space optimization. To reduce the computational cost, we estimate the distribution of the best worst solution for the best solutions found so far. The probabilistic model is used to sample part of the initial population of the scenario space differential evolution, using a priori knowledge of the previous generations. The method is compared with a state-of-the-art algorithm on both benchmark problems and an engineering application, and the related results are reported. Full article
(This article belongs to the Special Issue Evolutionary Algorithms in Engineering Design Optimization)
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Article
The Real-Life Application of Differential Evolution with a Distance-Based Mutation-Selection
Mathematics 2021, 9(16), 1909; https://doi.org/10.3390/math9161909 - 10 Aug 2021
Viewed by 541
Abstract
This paper proposes the real-world application of the Differential Evolution (DE) algorithm using, distance-based mutation-selection, population size adaptation, and an archive for solutions (DEDMNA). This simple framework uses three widely-used mutation types with the application of binomial crossover. For each solution, the most [...] Read more.
This paper proposes the real-world application of the Differential Evolution (DE) algorithm using, distance-based mutation-selection, population size adaptation, and an archive for solutions (DEDMNA). This simple framework uses three widely-used mutation types with the application of binomial crossover. For each solution, the most proper position prior to evaluation is selected using the Euclidean distances of three newly generated positions. Moreover, an efficient linear population-size reduction mechanism is employed. Furthermore, an archive of older efficient solutions is used. The DEDMNA algorithm is applied to three real-life engineering problems and 13 constrained problems. Seven well-known state-of-the-art DE algorithms are used to compare the efficiency of DEDMNA. The performance of DEDMNA and other algorithms are comparatively assessed using statistical methods. The results obtained show that DEDMNA is a very comparable optimiser compared to the best performing DE variants. The simple idea of measuring the distance of the mutant solutions increases the performance of DE significantly. Full article
(This article belongs to the Special Issue Evolutionary Algorithms in Engineering Design Optimization)
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Article
Evolutionary Design of a System for Online Surface Roughness Measurements
Mathematics 2021, 9(16), 1904; https://doi.org/10.3390/math9161904 - 10 Aug 2021
Cited by 1 | Viewed by 597
Abstract
Surface roughness is one of the key characteristics of machined components as it affects the surface quality and, consequently, the lifetime of the components themselves. The most common method of measuring the surface roughness is contact profilometry. Although this method is still widely [...] Read more.
Surface roughness is one of the key characteristics of machined components as it affects the surface quality and, consequently, the lifetime of the components themselves. The most common method of measuring the surface roughness is contact profilometry. Although this method is still widely applied, it has several drawbacks, such as limited measurement speed, sensitivity to vibrations, and requirement for precise positioning of the measured samples. In this paper, machine vision, machine learning and evolutionary optimization algorithms are used to induce a model for predicting the surface roughness of automotive components. Based on the attributes extracted by a machine vision algorithm, a machine learning algorithm generates the roughness predictive model. In addition, an evolutionary algorithm is used to tune the machine vision and machine learning algorithm parameters in order to find the most accurate predictive model. The developed methodology is comparable to the existing contact measurement method with respect to accuracy, but advantageous in that it is capable of predicting the surface roughness online and in real time. Full article
(This article belongs to the Special Issue Evolutionary Algorithms in Engineering Design Optimization)
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Article
Genetic Programming Guidance Control System for a Reentry Vehicle under Uncertainties
Mathematics 2021, 9(16), 1868; https://doi.org/10.3390/math9161868 - 06 Aug 2021
Cited by 1 | Viewed by 505
Abstract
As technology improves, the complexity of controlled systems increases as well. Alongside it, these systems need to face new challenges, which are made available by this technology advancement. To overcome these challenges, the incorporation of AI into control systems is changing its status, [...] Read more.
As technology improves, the complexity of controlled systems increases as well. Alongside it, these systems need to face new challenges, which are made available by this technology advancement. To overcome these challenges, the incorporation of AI into control systems is changing its status, from being just an experiment made in academia, towards a necessity. Several methods to perform this integration of AI into control systems have been considered in the past. In this work, an approach involving GP to produce, offline, a control law for a reentry vehicle in the presence of uncertainties on the environment and plant models is studied, implemented and tested. The results show the robustness of the proposed approach, which is capable of producing a control law of a complex nonlinear system in the presence of big uncertainties. This research aims to describe and analyze the effectiveness of a control approach to generate a nonlinear control law for a highly nonlinear system in an automated way. Such an approach would benefit the control practitioners by providing an alternative to classical control approaches, without having to rely on linearization techniques. Full article
(This article belongs to the Special Issue Evolutionary Algorithms in Engineering Design Optimization)
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Article
Multi-Objective Optimization of Plastics Thermoforming
Mathematics 2021, 9(15), 1760; https://doi.org/10.3390/math9151760 - 26 Jul 2021
Cited by 1 | Viewed by 663
Abstract
The practical application of a multi-objective optimization strategy based on evolutionary algorithms was proposed to optimize the plastics thermoforming process. For that purpose, in this work, differently from the other works proposed in the literature, the shaping step was considered individually with the [...] Read more.
The practical application of a multi-objective optimization strategy based on evolutionary algorithms was proposed to optimize the plastics thermoforming process. For that purpose, in this work, differently from the other works proposed in the literature, the shaping step was considered individually with the aim of optimizing the thickness distribution of the final part originated from sheets characterized by different thickness profiles, such as constant thickness, spline thickness variation in one direction and concentric thickness variation in two directions, while maintaining the temperature constant. As far we know, this is the first work where such a type of approach is proposed. A multi-objective optimization strategy based on Evolutionary Algorithms was applied to the determination of the final part thickness distribution with the aim of demonstrating the validity of the methodology proposed. The results obtained considering three different theoretical initial sheet shapes indicate clearly that the methodology proposed is valid, as it provides solutions with physical meaning and with great potential to be applied in real practice. The different thickness profiles obtained for the optimal Pareto solutions show, in all cases, that that the different profiles along the front are related to the objectives considered. Also, there is a clear improvement in the successive generations of the evolutionary algorithm. Full article
(This article belongs to the Special Issue Evolutionary Algorithms in Engineering Design Optimization)
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Article
Multi-Objective Optimum Design and Maintenance of Safety Systems: An In-Depth Comparison Study Including Encoding and Scheduling Aspects with NSGA-II
Mathematics 2021, 9(15), 1751; https://doi.org/10.3390/math9151751 - 25 Jul 2021
Cited by 1 | Viewed by 627
Abstract
Maximising profit is an important target for industries in a competitive world and it is possible to achieve this by improving the system availability. Engineers have employed many techniques to improve systems availability, such as adding redundant devices or scheduling maintenance strategies. However, [...] Read more.
Maximising profit is an important target for industries in a competitive world and it is possible to achieve this by improving the system availability. Engineers have employed many techniques to improve systems availability, such as adding redundant devices or scheduling maintenance strategies. However, the idea of using such techniques simultaneously has not received enough attention. The authors of the present paper recently studied the simultaneous optimisation of system design and maintenance strategy in order to achieve both maximum availability and minimum cost: the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was coupled with Discrete Event Simulation in a real encoding environment in order to achieve a set of non-dominated solutions. In this work, that study is extended and a thorough exploration using the above-mentioned Multi-objective Evolutionary Algorithm is developed using an industrial case study, paying attention to the possible impact on solutions as a result of different encodings, parameter configurations and chromosome lengths, which affect the accuracy levels when scheduling preventive maintenance. Non-significant differences were observed in the experimental results, which raises interesting conclusions regarding flexibility in the preventive maintenance strategy. Full article
(This article belongs to the Special Issue Evolutionary Algorithms in Engineering Design Optimization)
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Article
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 621
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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Article
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 488
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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Article
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
Cited by 1 | Viewed by 611
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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Article
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 449
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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Article
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 804
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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Article
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
Cited by 1 | Viewed by 906
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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Article
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 23 | Viewed by 1690
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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