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Processes
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Evolutionary Process for Engineering Optimization
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Evolutionary Process for Engineering Optimization

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: closed (15 June 2022) | Viewed by 28729

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Dr. Amir H. Gandomi

grade E-Mail Website
Guest Editor
1. Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia
2. University Research and Innovation Center (EKIK), Óbuda University, 1034 Budapest, Hungary
Interests: data analytics; machine learning; evolutionary computation; engineering optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Laith Abualigah

E-Mail Website
Guest Editor
Faculty of Information Technology, Al Al-Bayt University, Mafraq, Jordan
Interests: arithmetic optimization algorithm (AOA); bio-inspired computing; nature-inspired computing; swarm intelligence; artificial intelligence; meta-heuristic modeling; optimization algorithms; evolutionary computations; information retrieval; text clustering; feature selection; combinatorial problems; optimization; advanced machine learning; big data; natural language processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Various real-world engineering applications, such as engineering design, industrial manufacturing systems, and water distribution networks, are complex problems. Evolutionary computation is a hot topic of interest amongst researchers in various disciplines of engineering and science. Evolutionary computation is a group of optimization algorithms used for solving global optimization problems, which is inspired by biological evolution. It includes various signal and population-based methods with a meta-heuristic or stochastic optimization part.

In recent years, evolutionary computation methods have been successfully utilized to address complex real-world problems. The literature is abundant with several other approaches that share the same goal: to find a new optimal solution with satisfactory quality by alternating search strategies. Many theoretical and experimental studies have proved significant evolutionary computation properties. The most famous evolutionary computation methods are the genetic algorithm (GA), evolution strategy (ES), differential evolution (DE), particle swarm optimization (PSO), bacterial foraging optimization (BFO), ant colony optimization (ACO), and the memetic algorithm (MA). However, with the fast growth of complex systems, the optimization problems become much larger and complicated. The common issues facing evolutionary algorithms are the dimension of objective functions, decision variables, or constraints.

In light of the expanding interest for new innovative methods of solving real-world and engineering optimization problems, this Special Issue intends to promote high-quality research outputs in the latest progress and improvement of evolutionary algorithms and engineering applications and offers recent advanced researches in the field to serve the researchers and practitioners. The main interest is on interdisciplinary research on te evolutionary algorithm, using modern computational intelligence theories, methods, and practices. We invite the researchers to submit their original contributions addressing particular challenging aspects in evolutionary computation from both theoretical and applied viewpoints. Authors are encouraged to submit their contributions covering the following topics:

Methods (but not be limited to):
Evolutionary computation
Swarm intelligence
Meta-heuristics
Genetic algorithm
Genetic programming
Differential evolution
Particle swarm optimization
Ant colony optimization
Bacterial foraging optimization

Applications (but not be limited to):
Material Optimization
Process Optimization
Engineering design problems
Complex system modelling and optimization
Constraint handling
Parameters tuning
Industrial problems
Benchmarks
Imaging and vision
Knowledge processing
Intelligent fault detection
Control and manufacturing applications
Multi/Many-objective optimization
Real-world applications for complex systems

Prof. Dr. Amir H. Gandomi
Dr. Laith Abualigah
Guest Editors

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 submissions that pass pre-check are 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. Processes 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 2400 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
  • Swarm intelligence
  • Meta-heuristics
  • Genetic algorithm
  • Genetic programming
  • Differential evolution
  • Particle swarm optimization
  • Ant colony optimization
  • Bacterial foraging optimization
  • Material Optimization
  • Process Optimization
  • Engineering design problems
  • Complex system modelling and optimization
  • Constraint handling
  • Parameters tuning
  • Industrial problems
  • Benchmarks
  • Imaging and vision
  • Knowledge processing
  • Intelligent fault detection
  • Control and manufacturing applications
  • Multi/Many-objective optimization
  • Real-world applications for complex systems

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Published Papers (10 papers)

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Research

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23 pages, 2194 KiB  
Article
Data Driven Model Estimation for Aerial Vehicles: A Perspective Analysis
by Syeda Kounpal Fatima, Manzar Abbas, Imran Mir, Faiza Gul, Suleman Mir, Nasir Saeed, Abdullah Alhumaidi Alotaibi, Turke Althobaiti and Laith Abualigah
Processes 2022, 10(7), 1236; https://doi.org/10.3390/pr10071236 - 21 Jun 2022
Cited by 19 | Viewed by 3264
Abstract
Unmanned Aerial Vehicles (UAVs) are important tool for various applications, including enhancing target detection accuracy in various surface-to-air and air-to-air missions. To ensure mission success of these UAVs, a robust control system is needed, which further requires well-characterized dynamic system model. This paper [...] Read more.
Unmanned Aerial Vehicles (UAVs) are important tool for various applications, including enhancing target detection accuracy in various surface-to-air and air-to-air missions. To ensure mission success of these UAVs, a robust control system is needed, which further requires well-characterized dynamic system model. This paper aims to present a consolidated framework for the estimation of an experimental UAV utilizing flight data. An elaborate estimation mechanism is proposed utilizing various model structures, such as Autoregressive Exogenous (ARX), Autoregressive Moving Average exogenous (ARMAX), Box Jenkin’s (BJ), Output Error (OE), and state-space and non-linear Autoregressive Exogenous. A perspective analysis and comparison are made to identify the salient aspects of each model structure. Model configuration with best characteristics is then identified based upon model quality parameters such as residual analysis, final prediction error, and fit percentages. Extensive validation to evaluate the performance of the developed model is then performed utilizing the flight dynamics data collected. Results indicate the model’s viability as the model can accurately predict the system performance at a wide range of operating conditions. Through this, to the best of our knowledge, we present for the first time a model prediction analysis, which utilizes comprehensive flight dynamics data instead of simulation work. Full article
(This article belongs to the Special Issue Evolutionary Process for Engineering Optimization)
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24 pages, 4479 KiB  
Article
Multilayer Reversible Data Hiding Based on the Difference Expansion Method Using Multilevel Thresholding of Host Images Based on the Slime Mould Algorithm
by Abolfazl Mehbodniya, Behnaz karimi Douraki, Julian L. Webber, Hamzah Ali Alkhazaleh, Ersin Elbasi, Mohammad Dameshghi, Raed Abu Zitar and Laith Abualigah
Processes 2022, 10(5), 858; https://doi.org/10.3390/pr10050858 - 26 Apr 2022
Cited by 18 | Viewed by 2339
Abstract
Researchers have scrutinized data hiding schemes in recent years. Data hiding in standard images works well, but does not provide satisfactory results in distortion-sensitive medical, military, or forensic images. This is because placing data in an image can cause permanent distortion after data [...] Read more.
Researchers have scrutinized data hiding schemes in recent years. Data hiding in standard images works well, but does not provide satisfactory results in distortion-sensitive medical, military, or forensic images. This is because placing data in an image can cause permanent distortion after data mining. Therefore, a reversible data hiding (RDH) technique is required. One of the well-known designs of RDH is the difference expansion (DE) method. In the DE-based RDH method, finding spaces that create less distortion in the marked image is a significant challenge, and has a high insertion capacity. Therefore, the smaller the difference between the selected pixels and the more correlation between two consecutive pixels, the less distortion can be achieved in the image after embedding the secret data. This paper proposes a multilayer RDH method using the multilevel thresholding technique to reduce the difference value in pixels and increase the visual quality and the embedding capacity. Optimization algorithms are one of the most popular methods for solving NP-hard problems. The slime mould algorithm (SMA) gives good results in finding the best solutions to optimization problems. In the proposed method, the SMA is applied to the host image for optimal multilevel thresholding of the image pixels. Moreover, the image pixels in different and more similar areas of the image are located next to one another in a group and classified using the specified thresholds. As a result, the embedding capacity in each class can increase by reducing the value of the difference between two consecutive pixels, and the distortion of the marked image can decrease after inserting the personal data using the DE method. Experimental results show that the proposed method is better than comparable methods regarding the degree of distortion, quality of the marked image, and insertion capacity. Full article
(This article belongs to the Special Issue Evolutionary Process for Engineering Optimization)
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14 pages, 564 KiB  
Article
Enhance Teaching-Learning-Based Optimization for Tsallis-Entropy-Based Feature Selection Classification Approach
by Di Wu, Heming Jia, Laith Abualigah, Zhikai Xing, Rong Zheng, Hongyu Wang and Maryam Altalhi
Processes 2022, 10(2), 360; https://doi.org/10.3390/pr10020360 - 14 Feb 2022
Cited by 24 | Viewed by 2773
Abstract
Feature selection is an effective method to reduce the number of data features, which boosts classification performance in machine learning. This paper uses the Tsallis-entropy-based feature selection to detect the significant feature. Support Vector Machine (SVM) is adopted as the classifier for classification [...] Read more.
Feature selection is an effective method to reduce the number of data features, which boosts classification performance in machine learning. This paper uses the Tsallis-entropy-based feature selection to detect the significant feature. Support Vector Machine (SVM) is adopted as the classifier for classification purposes in this paper. We proposed an enhanced Teaching-Learning-Based Optimization (ETLBO) to optimize the SVM and Tsallis entropy parameters to improve classification accuracy. The adaptive weight strategy and Kent chaotic map are used to enhance the optimal ability of the traditional TLBO. The proposed method aims to avoid the main weaknesses of the original TLBO, which is trapped in local optimal and unbalance between the search mechanisms. Experiments based on 16 classical datasets are selected to test the performance of the ETLBO, and the results are compared with other well-established optimization algorithms. The obtained results illustrate that the proposed method has better performance in classification accuracy. Full article
(This article belongs to the Special Issue Evolutionary Process for Engineering Optimization)
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28 pages, 3401 KiB  
Article
Migration-Based Moth-Flame Optimization Algorithm
by Mohammad H. Nadimi-Shahraki, Ali Fatahi, Hoda Zamani, Seyedali Mirjalili, Laith Abualigah and Mohamed Abd Elaziz
Processes 2021, 9(12), 2276; https://doi.org/10.3390/pr9122276 - 18 Dec 2021
Cited by 58 | Viewed by 5784
Abstract
Moth–flame optimization (MFO) is a prominent swarm intelligence algorithm that demonstrates sufficient efficiency in tackling various optimization tasks. However, MFO cannot provide competitive results for complex optimization problems. The algorithm sinks into the local optimum due to the rapid dropping of population diversity [...] Read more.
Moth–flame optimization (MFO) is a prominent swarm intelligence algorithm that demonstrates sufficient efficiency in tackling various optimization tasks. However, MFO cannot provide competitive results for complex optimization problems. The algorithm sinks into the local optimum due to the rapid dropping of population diversity and poor exploration. Hence, in this article, a migration-based moth–flame optimization (M-MFO) algorithm is proposed to address the mentioned issues. In M-MFO, the main focus is on improving the position of unlucky moths by migrating them stochastically in the early iterations using a random migration (RM) operator, maintaining the solution diversification by storing new qualified solutions separately in a guiding archive, and, finally, exploiting around the positions saved in the guiding archive using a guided migration (GM) operator. The dimensionally aware switch between these two operators guarantees the convergence of the population toward the promising zones. The proposed M-MFO was evaluated on the CEC 2018 benchmark suite on dimension 30 and compared against seven well-known variants of MFO, including LMFO, WCMFO, CMFO, CLSGMFO, LGCMFO, SMFO, and ODSFMFO. Then, the top four latest high-performing variants were considered for the main experiments with different dimensions, 30, 50, and 100. The experimental evaluations proved that the M-MFO provides sufficient exploration ability and population diversity maintenance by employing migration strategy and guiding archive. In addition, the statistical results analyzed by the Friedman test proved that the M-MFO demonstrates competitive performance compared to the contender algorithms used in the experiments. Full article
(This article belongs to the Special Issue Evolutionary Process for Engineering Optimization)
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25 pages, 3049 KiB  
Article
Deep Ensemble of Slime Mold Algorithm and Arithmetic Optimization Algorithm for Global Optimization
by Rong Zheng, Heming Jia, Laith Abualigah, Qingxin Liu and Shuang Wang
Processes 2021, 9(10), 1774; https://doi.org/10.3390/pr9101774 - 4 Oct 2021
Cited by 57 | Viewed by 3537
Abstract
In this paper, a new hybrid algorithm based on two meta-heuristic algorithms is presented to improve the optimization capability of original algorithms. This hybrid algorithm is realized by the deep ensemble of two new proposed meta-heuristic methods, i.e., slime mold algorithm (SMA) and [...] Read more.
In this paper, a new hybrid algorithm based on two meta-heuristic algorithms is presented to improve the optimization capability of original algorithms. This hybrid algorithm is realized by the deep ensemble of two new proposed meta-heuristic methods, i.e., slime mold algorithm (SMA) and arithmetic optimization algorithm (AOA), called DESMAOA. To be specific, a preliminary hybrid method was applied to obtain the improved SMA, called SMAOA. Then, two strategies that were extracted from the SMA and AOA, respectively, were embedded into SMAOA to boost the optimizing speed and accuracy of the solution. The optimization performance of the proposed DESMAOA was analyzed by using 23 classical benchmark functions. Firstly, the impacts of different components are discussed. Then, the exploitation and exploration capabilities, convergence behaviors, and performances are evaluated in detail. Cases at different dimensions also were investigated. Compared with the SMA, AOA, and another five well-known optimization algorithms, the results showed that the proposed method can outperform other optimization algorithms with high superiority. Finally, three classical engineering design problems were employed to illustrate the capability of the proposed algorithm for solving the practical problems. The results also indicate that the DESMAOA has very promising performance when solving these problems. Full article
(This article belongs to the Special Issue Evolutionary Process for Engineering Optimization)
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28 pages, 8428 KiB  
Article
An Improved Hybrid Aquila Optimizer and Harris Hawks Algorithm for Solving Industrial Engineering Optimization Problems
by Shuang Wang, Heming Jia, Laith Abualigah, Qingxin Liu and Rong Zheng
Processes 2021, 9(9), 1551; https://doi.org/10.3390/pr9091551 - 30 Aug 2021
Cited by 108 | Viewed by 6176
Abstract
Aquila Optimizer (AO) and Harris Hawks Optimizer (HHO) are recently proposed meta-heuristic optimization algorithms. AO possesses strong global exploration capability but insufficient local exploitation ability. However, the exploitation phase of HHO is pretty good, while the exploration capability is far from satisfactory. Considering [...] Read more.
Aquila Optimizer (AO) and Harris Hawks Optimizer (HHO) are recently proposed meta-heuristic optimization algorithms. AO possesses strong global exploration capability but insufficient local exploitation ability. However, the exploitation phase of HHO is pretty good, while the exploration capability is far from satisfactory. Considering the characteristics of these two algorithms, an improved hybrid AO and HHO combined with a nonlinear escaping energy parameter and random opposition-based learning strategy is proposed, namely IHAOHHO, to improve the searching performance in this paper. Firstly, combining the salient features of AO and HHO retains valuable exploration and exploitation capabilities. In the second place, random opposition-based learning (ROBL) is added in the exploitation phase to improve local optima avoidance. Finally, the nonlinear escaping energy parameter is utilized better to balance the exploration and exploitation phases of IHAOHHO. These two strategies effectively enhance the exploration and exploitation of the proposed algorithm. To verify the optimization performance, IHAOHHO is comprehensively analyzed on 23 standard benchmark functions. Moreover, the practicability of IHAOHHO is also highlighted by four industrial engineering design problems. Compared with the original AO and HHO and five state-of-the-art algorithms, the results show that IHAOHHO has strong superior performance and promising prospects. Full article
(This article belongs to the Special Issue Evolutionary Process for Engineering Optimization)
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37 pages, 2649 KiB  
Article
A Novel Evolutionary Arithmetic Optimization Algorithm for Multilevel Thresholding Segmentation of COVID-19 CT Images
by Laith Abualigah, Ali Diabat, Putra Sumari and Amir H. Gandomi
Processes 2021, 9(7), 1155; https://doi.org/10.3390/pr9071155 - 2 Jul 2021
Cited by 119 | Viewed by 5088
Abstract
One of the most crucial aspects of image segmentation is multilevel thresholding. However, multilevel thresholding becomes increasingly more computationally complex as the number of thresholds grows. In order to address this defect, this paper proposes a new multilevel thresholding approach based on the [...] Read more.
One of the most crucial aspects of image segmentation is multilevel thresholding. However, multilevel thresholding becomes increasingly more computationally complex as the number of thresholds grows. In order to address this defect, this paper proposes a new multilevel thresholding approach based on the Evolutionary Arithmetic Optimization Algorithm (AOA). The arithmetic operators in science were the inspiration for AOA. DAOA is the proposed approach, which employs the Differential Evolution technique to enhance the AOA local research. The proposed algorithm is applied to the multilevel thresholding problem, using Kapur’s measure between class variance functions. The suggested DAOA is used to evaluate images, using eight standard test images from two different groups: nature and CT COVID-19 images. Peak signal-to-noise ratio (PSNR) and structural similarity index test (SSIM) are standard evaluation measures used to determine the accuracy of segmented images. The proposed DAOA method’s efficiency is evaluated and compared to other multilevel thresholding methods. The findings are presented with a number of different threshold values (i.e., 2, 3, 4, 5, and 6). According to the experimental results, the proposed DAOA process is better and produces higher-quality solutions than other comparative approaches. Moreover, it achieved better-segmented images, PSNR, and SSIM values. In addition, the proposed DAOA is ranked the first method in all test cases. Full article
(This article belongs to the Special Issue Evolutionary Process for Engineering Optimization)
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23 pages, 2301 KiB  
Article
Improved NSGA-III with Second-Order Difference Random Strategy for Dynamic Multi-Objective Optimization
by Haijuan Zhang, Gai-Ge Wang, Junyu Dong and Amir H. Gandomi
Processes 2021, 9(6), 911; https://doi.org/10.3390/pr9060911 - 21 May 2021
Cited by 12 | Viewed by 2808
Abstract
Most real-world problems that have two or three objectives are dynamic, and the environment of the problems may change as time goes on. For the purpose of solving dynamic multi-objective problems better, two proposed strategies (second-order difference strategy and random strategy) were incorporated [...] Read more.
Most real-world problems that have two or three objectives are dynamic, and the environment of the problems may change as time goes on. For the purpose of solving dynamic multi-objective problems better, two proposed strategies (second-order difference strategy and random strategy) were incorporated with NSGA-III, namely SDNSGA-III. When the environment changes in SDNSGA-III, the second-order difference strategy and random strategy are first used to improve the individuals in the next generation population, then NSGA-III is employed to optimize the individuals to obtain optimal solutions. Our experiments were conducted with two primary objectives. The first was to test the values of the metrics mean inverted generational distance (MIGD), mean generational distance (MGD), and mean hyper volume (MHV) on the test functions (Fun1 to Fun6) via the proposed algorithm and the four state-of-the-art algorithms. The second aim was to compare the metrics’ value of NSGA-III with single strategy and SDNSGA-III, proving the efficiency of the two strategies in SDNSGA-III. The comparative data obtained from the experiments demonstrate that SDNSGA-III has good convergence and diversity compared with four other evolutionary algorithms. What is more, the efficiency of second-order difference strategy and random strategy was also analyzed in this paper. Full article
(This article belongs to the Special Issue Evolutionary Process for Engineering Optimization)
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35 pages, 5825 KiB  
Article
Material Generation Algorithm: A Novel Metaheuristic Algorithm for Optimization of Engineering Problems
by Siamak Talatahari, Mahdi Azizi and Amir H. Gandomi
Processes 2021, 9(5), 859; https://doi.org/10.3390/pr9050859 - 13 May 2021
Cited by 75 | Viewed by 4851
Abstract
A new algorithm, Material Generation Algorithm (MGA), was developed and applied for the optimum design of engineering problems. Some advanced and basic aspects of material chemistry, specifically the configuration of chemical compounds and chemical reactions in producing new materials, are determined as inspirational [...] Read more.
A new algorithm, Material Generation Algorithm (MGA), was developed and applied for the optimum design of engineering problems. Some advanced and basic aspects of material chemistry, specifically the configuration of chemical compounds and chemical reactions in producing new materials, are determined as inspirational concepts of the MGA. For numerical investigations purposes, 10 constrained optimization problems in different dimensions of 10, 30, 50, and 100, which have been benchmarked by the Competitions on Evolutionary Computation (CEC), are selected as test examples while 15 of the well-known engineering design problems are also determined to evaluate the overall performance of the proposed method. The best results of different classical and new metaheuristic optimization algorithms in dealing with the selected problems were taken from the recent literature for comparison with MGA. Additionally, the statistical values of the MGA algorithm, consisting of the mean, worst, and standard deviation, were calculated and compared to the results of other metaheuristic algorithms. Overall, this work demonstrates that the proposed MGA is able provide very competitive, and even outstanding, results and mostly outperforms other metaheuristics. Full article
(This article belongs to the Special Issue Evolutionary Process for Engineering Optimization)
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Review

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31 pages, 8636 KiB  
Review
Scheduling by NSGA-II: Review and Bibliometric Analysis
by Iman Rahimi, Amir H. Gandomi, Kalyanmoy Deb, Fang Chen and Mohammad Reza Nikoo
Processes 2022, 10(1), 98; https://doi.org/10.3390/pr10010098 - 4 Jan 2022
Cited by 45 | Viewed by 6130
Abstract
NSGA-II is an evolutionary multi-objective optimization algorithm that has been applied to a wide variety of search and optimization problems since its publication in 2000. This study presents a review and bibliometric analysis of numerous NSGA-II adaptations in addressing scheduling problems. This paper [...] Read more.
NSGA-II is an evolutionary multi-objective optimization algorithm that has been applied to a wide variety of search and optimization problems since its publication in 2000. This study presents a review and bibliometric analysis of numerous NSGA-II adaptations in addressing scheduling problems. This paper is divided into two parts. The first part discusses the main ideas of scheduling and different evolutionary computation methods for scheduling and provides a review of different scheduling problems, such as production and personnel scheduling. Moreover, a brief comparison of different evolutionary multi-objective optimization algorithms is provided, followed by a summary of state-of-the-art works on the application of NSGA-II in scheduling. The next part presents a detailed bibliometric analysis focusing on NSGA-II for scheduling applications obtained from the Scopus and Web of Science (WoS) databases based on keyword and network analyses that were conducted to identify the most interesting subject fields. Additionally, several criteria are recognized which may advise scholars to find key gaps in the field and develop new approaches in future works. The final sections present a summary and aims for future studies, along with conclusions and a discussion. Full article
(This article belongs to the Special Issue Evolutionary Process for Engineering Optimization)
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