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Evolutionary Algorithms and Large-Scale Real-World Applications
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Evolutionary Algorithms and Large-Scale Real-World Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 November 2022) | Viewed by 39657

Special Issue Editors

Dr. Absalom Ezugwu

E-Mail Website
Guest Editor
School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal: Durban, KwaZulu-Natal, South Africa
Interests: machine learning; nature and biologically inspired algorithms; global optimization; evolutionary algorithm; swarm Intelligence; parallel computing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Haruna Chiroma

E-Mail Website
Guest Editor
College of Computer Science and Engineering, University of Hafr Al Batin, Hafar Al Batin 39524, Saudi Arabia
Interests: machine learning; nature-inspired meta-heuristic algorithms; artificial neural networks with an emphasis on deep learning
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
Prof. Dr. Roberto A. Vazquez

E-Mail Website
Guest Editor
Faculty of Engineering, Universidad La Salle, Mexico City, Mexico
Interests: machine learning; evolutionary computation; computer vision; computational neuroscience

Special Issue Information

Dear Colleagues,

In the last decade, computational intelligence and, specifically, evolutionary computing research have witnessed exponential growth in terms of the strong pressure to search for and reinvent new optimization techniques based on nature-inspired phenomena. Similarly, we have also recently witnessed increased research efforts dedicated to addressing several complex real-world problems of either single-objective or multi-objective optimization orientation by using diverse evolutionary search strategies. However, despite the recorded success of these efforts in solving wide-ranging large-scale optimization problems, there is still a wide gap between the variety of application problems that have been addressed in the literature and those encountered in real life, which are significant for solving practical problems in science, medicine, and engineering. Moreover, due to the practical relevance of large-scale optimization problems, computationally efficient and effective evolutionary algorithms for solving such optimization problems are in high demand.

The Special Issue targets novel work that addresses recent advances in the following topics: theoretical analysis of evolutionary algorithms; evolutionary computation theory; evolutionary deep learning; hybrid evolutionary approaches; neuro-evolutionary systems; target-driven visual navigation; evolutionary algorithms for self-driving cars; parallel evolutionary algorithms; GPU implementation of evolutionary algorithms; target-driven visual navigation; evolutionary learning algorithms; neural architecture search; extreme learning machines; and few-shot learning, etc.

Therefore, in this Special Issue, we aim to promote discussions around recent efforts and advances in large-scale real-world applications of evolutionary algorithms to tackle challenging practical optimization problems. We encourage explorations of theory, applied research on the advancement of evolutionary algorithms, surveys, and comprehensive literature reviews.

Dr. Absalom Ezugwu
Dr. Haruna Chiroma
Dr. Laith Abualigah
Prof. Dr. Roberto A. Vazquez
Guest Editors

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Keywords

  • genetic algorithm
  • genetic programming
  • evolutionary programming
  • evolution strategies
  • differential evolution
  • swarm intelligence
  • particle swarm optimization
  • artificial bee colony
  • ant colony optimization
  • artificial immune systems
  • memetic algorithms
  • large-scale optimization problems
  • evolutionary computation
  • cuckoo search algorithms
  • teaching learning-based optimization
  • sybiotic organisms search
  • whale optimization algorithm
  • butterfly optimization algorithm
  • ebola optimization search algorithm

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Published Papers (16 papers)

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19 pages, 3317 KiB  
Article
Selecting Features That Influence Vehicle Collisions in the Internet of Vehicles Based on a Multi-Objective Hybrid Bi-Directional NSGA-III
by Mubarak S. Almutairi, Khalid Almutairi and Haruna Chiroma
Appl. Sci. 2023, 13(4), 2064; https://doi.org/10.3390/app13042064 - 5 Feb 2023
Cited by 1 | Viewed by 1317
Abstract
The smart platform of generating, collecting, managing and processing dynamic data from different sources in the Internet of Vehicles (IoV) pave the way for a large-scale dataset to be accumulated. The dataset can contain records running into hundreds of thousands and even millions [...] Read more.
The smart platform of generating, collecting, managing and processing dynamic data from different sources in the Internet of Vehicles (IoV) pave the way for a large-scale dataset to be accumulated. The dataset can contain records running into hundreds of thousands and even millions of relevant, irrelevant and redundant features. Therefore, feature selection to select only the significant features for developing vehicle collision detection alarm systems for deployment in the IoV edge is critical. However, previous studies on vehicle collision detection in the IoV have not conducted rigorous feature selection. Limited studies have mainly applied Pearson correlation coefficient (PCC) to select subset features influencing vehicle collision in the domain of IoV. However, PCC can cause relevant features to be discarded if the correlation of the non-linear association is too small, thereby providing incorrect feature ranking, which, in turn, increases the chances of developing a model that will give a poor outcome. To close this gap, this paper proposed a multi-objective, filter-based hybrid non-dominated sorted genetic algorithm III with a gain ratio and bi-directional wrapper for the selection of subset features influencing vehicle collision in the IoV. The proposed approach selected the minimal most significant subset features for developing a vehicle collision detection classifier with maximum accuracy for deployment in the IoV. A comparative study proves that the proposed approach performs better than the compared algorithms across hybrid-, wrapper- and filter-based feature selection methods within the family of the NSGA. Further, a comparative analysis with other evolutionary algorithms proves the superiority of the proposal. This study can help researchers in the future by avoiding the use of large-scale computing resources in acquiring data to develop vehicle collision alert systems in the IoV. This can be achieved since only the subset features discovered in this study are collected, as opposed to collecting large features, thus saving time and resources in the subsequent vehicle collision detection data collection in the IoV. Full article
(This article belongs to the Special Issue Evolutionary Algorithms and Large-Scale Real-World Applications)
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20 pages, 4924 KiB  
Article
A System Dynamics Approach to Optimize Milk Production in an Industrial Ranch
by Nasser Shahsavari-Pour, Sajad Rahimi-Ashjerdi, Azim Heydari and Afef Fekih
Appl. Sci. 2023, 13(3), 1662; https://doi.org/10.3390/app13031662 - 28 Jan 2023
Cited by 1 | Viewed by 1909
Abstract
The milk production process on an industrial ranch consists of various and regular activities, with each requiring a proper management approach. Different variables also affect the milk production process, and the maximum milk production is achieved by identifying critical variables. This work was [...] Read more.
The milk production process on an industrial ranch consists of various and regular activities, with each requiring a proper management approach. Different variables also affect the milk production process, and the maximum milk production is achieved by identifying critical variables. This work was motivated by the Fereidan Ahrar ranch management in Isfahan, Iran, which seeks to identify and optimize important variables to increase milk production. This unit also considers livestock omission due to disease and losses as one of the important issues. This kind of omission is followed by the increased medical costs of the ranch. This paper investigated a system dynamics approach and Vensim software to simulate the milk production process considering the combination of demographic livestock and medical costs. System sensitivity was analyzed using the design of experiment (DOE) technique and some scenarios were proposed to maximize milk production by identifying and tuning important variables affecting milk production. The simulation results of the designed model showed five important variables affecting milk production. These variables include the production cycle rate, voluntary omission rate, change rates of female calves per year that are entered into the life cycle of the ranch, pregnant heifers that become dairy herds after calving, and finally, the effect of the medical costs. Full article
(This article belongs to the Special Issue Evolutionary Algorithms and Large-Scale Real-World Applications)
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34 pages, 3230 KiB  
Article
Improved SOSK-Means Automatic Clustering Algorithm with a Three-Part Mutualism Phase and Random Weighted Reflection Coefficient for High-Dimensional Datasets
by Abiodun M. Ikotun and Absalom E. Ezugwu
Appl. Sci. 2022, 12(24), 13019; https://doi.org/10.3390/app122413019 - 19 Dec 2022
Cited by 4 | Viewed by 1886
Abstract
Automatic clustering problems require clustering algorithms to automatically estimate the number of clusters in a dataset. However, the classical K-means requires the specification of the required number of clusters a priori. To address this problem, metaheuristic algorithms are hybridized with K-means to extend [...] Read more.
Automatic clustering problems require clustering algorithms to automatically estimate the number of clusters in a dataset. However, the classical K-means requires the specification of the required number of clusters a priori. To address this problem, metaheuristic algorithms are hybridized with K-means to extend the capacity of K-means in handling automatic clustering problems. In this study, we proposed an improved version of an existing hybridization of the classical symbiotic organisms search algorithm with the classical K-means algorithm to provide robust and optimum data clustering performance in automatic clustering problems. Moreover, the classical K-means algorithm is sensitive to noisy data and outliers; therefore, we proposed the exclusion of outliers from the centroid update’s procedure, using a global threshold of point-to-centroid distance distribution for automatic outlier detection, and subsequent exclusion, in the calculation of new centroids in the K-means phase. Furthermore, a self-adaptive benefit factor with a three-part mutualism phase is incorporated into the symbiotic organism search phase to enhance the performance of the hybrid algorithm. A population size of 40+2g was used for the symbiotic organism search (SOS) algorithm for a well distributed initial solution sample, based on the central limit theorem that the selection of the right sample size produces a sample mean that approximates the true centroid on Gaussian distribution. The effectiveness and robustness of the improved hybrid algorithm were evaluated on 42 datasets. The results were compared with the existing hybrid algorithm, the standard SOS and K-means algorithms, and other hybrid and non-hybrid metaheuristic algorithms. Finally, statistical and convergence analysis tests were conducted to measure the effectiveness of the improved algorithm. The results of the extensive computational experiments showed that the proposed improved hybrid algorithm outperformed the existing SOSK-means algorithm and demonstrated superior performance compared to some of the competing hybrid and non-hybrid metaheuristic algorithms. Full article
(This article belongs to the Special Issue Evolutionary Algorithms and Large-Scale Real-World Applications)
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27 pages, 1392 KiB  
Article
Enhanced Firefly-K-Means Clustering with Adaptive Mutation and Central Limit Theorem for Automatic Clustering of High-Dimensional Datasets
by Abiodun M. Ikotun and Absalom E. Ezugwu
Appl. Sci. 2022, 12(23), 12275; https://doi.org/10.3390/app122312275 - 30 Nov 2022
Cited by 5 | Viewed by 2203
Abstract
Metaheuristic algorithms have been hybridized with the standard K-means to address the latter’s challenges in finding a solution to automatic clustering problems. However, the distance calculations required in the standard K-means phase of the hybrid clustering algorithms increase as the number of clusters [...] Read more.
Metaheuristic algorithms have been hybridized with the standard K-means to address the latter’s challenges in finding a solution to automatic clustering problems. However, the distance calculations required in the standard K-means phase of the hybrid clustering algorithms increase as the number of clusters increases, and the associated computational cost rises in proportion to the dataset dimensionality. The use of the standard K-means algorithm in the metaheuristic-based K-means hybrid algorithm for the automatic clustering of high-dimensional real-world datasets poses a great challenge to the clustering performance of the resultant hybrid algorithms in terms of computational cost. Reducing the computation time required in the K-means phase of the hybrid algorithm for the automatic clustering of high-dimensional datasets will inevitably reduce the algorithm’s complexity. In this paper, a preprocessing phase is introduced into the K-means phase of an improved firefly-based K-means hybrid algorithm using the concept of the central limit theorem to partition the high-dimensional dataset into subgroups of randomly formed subsets on which the K-means algorithm is applied to obtain representative cluster centers for the final clustering procedure. The enhanced firefly algorithm (FA) is hybridized with the CLT-based K-means algorithm to automatically determine the optimum number of cluster centroids and generate corresponding optimum initial cluster centroids for the K-means algorithm to achieve optimal global convergence. Twenty high-dimensional datasets from the UCI machine learning repository are used to investigate the performance of the proposed algorithm. The empirical results indicate that the hybrid FA-K-means clustering method demonstrates statistically significant superiority in the employed performance measures and reducing computation time cost for clustering high-dimensional dataset problems, compared to other advanced hybrid search variants. Full article
(This article belongs to the Special Issue Evolutionary Algorithms and Large-Scale Real-World Applications)
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40 pages, 5361 KiB  
Article
Binary Ebola Optimization Search Algorithm for Feature Selection and Classification Problems
by Olatunji Akinola, Olaide N. Oyelade and Absalom E. Ezugwu
Appl. Sci. 2022, 12(22), 11787; https://doi.org/10.3390/app122211787 - 19 Nov 2022
Cited by 12 | Viewed by 2547
Abstract
In the past decade, the extraction of valuable information from online biomedical datasets has exponentially increased due to the evolution of data processing devices and the utilization of machine learning capabilities to find useful information in these datasets. However, these datasets present a [...] Read more.
In the past decade, the extraction of valuable information from online biomedical datasets has exponentially increased due to the evolution of data processing devices and the utilization of machine learning capabilities to find useful information in these datasets. However, these datasets present a variety of features, dimensionalities, shapes, noise, and heterogeneity. As a result, deriving relevant information remains a problem, since multiple features bottleneck the classification process. Despite their adaptability, current state-of-the-art classifiers have failed to address the problem, giving rise to the exploration of binary optimization algorithms. This study proposes a novel approach to binarizing the Ebola optimization search algorithm. The binary Ebola search optimization algorithm (BEOSA) uses two newly formulated S-shape and V-shape transfer functions to investigate mutations of the infected population in the exploitation and exploration phases, respectively. A model is designed to show a representation of the binary search space and the mapping of the algorithm from the continuous space to the discrete space. Mathematical models are formulated to demonstrate the fitness and cost functions used for evaluating the algorithm. Using 22 benchmark datasets consisting of low, medium and high dimensional data, we exhaustively experimented with the proposed BEOSA method and six other recent similar feature selection methods. The experimental results show that the BEOSA and its variant BIEOSA were highly competitive with different state-of-the-art binary optimization algorithms. A comparative analysis of the classification accuracy obtained for eight binary optimizers showed that BEOSA performed competitively compared to other methods on nine datasets. Evaluation reports on all methods revealed that BEOSA was the top performer, obtaining the best values on eight datasets and eight fitness and cost functions. Computation for the average number of features selected showed that BEOSA outperformed other methods on 11 datasets when population sizes of 75 and 100 were used. Findings from the study revealed that BEOSA is effective in handling the challenge of feature selection in high-dimensional datasets. Full article
(This article belongs to the Special Issue Evolutionary Algorithms and Large-Scale Real-World Applications)
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30 pages, 4641 KiB  
Article
A Modified Gorilla Troops Optimizer for Global Optimization Problem
by Tingyao Wu, Di Wu, Heming Jia, Nuohan Zhang, Khaled H. Almotairi, Qingxin Liu and Laith Abualigah
Appl. Sci. 2022, 12(19), 10144; https://doi.org/10.3390/app121910144 - 9 Oct 2022
Cited by 14 | Viewed by 2687
Abstract
The Gorilla Troops Optimizer (GTO) is a novel Metaheuristic Algorithm that was proposed in 2021. Its design was inspired by the lifestyle characteristics of gorillas, including migration to a known position, migration to an undiscovered position, moving toward the other gorillas, following silverback [...] Read more.
The Gorilla Troops Optimizer (GTO) is a novel Metaheuristic Algorithm that was proposed in 2021. Its design was inspired by the lifestyle characteristics of gorillas, including migration to a known position, migration to an undiscovered position, moving toward the other gorillas, following silverback gorillas and competing with silverback gorillas for females. However, like other Metaheuristic Algorithms, the GTO still suffers from local optimum, low diversity, imbalanced utilization, etc. In order to improve the performance of the GTO, this paper proposes a modified Gorilla Troops Optimizer (MGTO). The improvement strategies include three parts: Beetle-Antennae Search Based on Quadratic Interpolation (QIBAS), Teaching–Learning-Based Optimization (TLBO) and Quasi-Reflection-Based Learning (QRBL). Firstly, QIBAS is utilized to enhance the diversity of the position of the silverback. Secondly, the teacher phase of TLBO is introduced to the update the behavior of following the silverback with 50% probability. Finally, the quasi-reflection position of the silverback is generated by QRBL. The optimal solution can be updated by comparing these fitness values. The performance of the proposed MGTO is comprehensively evaluated by 23 classical benchmark functions, 30 CEC2014 benchmark functions, 10 CEC2020 benchmark functions and 7 engineering problems. The experimental results show that MGTO has competitive performance and promising prospects in real-world optimization tasks. Full article
(This article belongs to the Special Issue Evolutionary Algorithms and Large-Scale Real-World Applications)
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17 pages, 3923 KiB  
Article
An Evolutionary Algorithmic Approach for Improving the Success Rate of Selective Assembly through a Novel EAUB Method
by Siva Kumar Mahalingam, Lenin Nagarajan, Chandran Velu, Vignesh Kumar Dharmaraj, Sachin Salunkhe and Hussein Mohamed Abdelmoneam Hussein
Appl. Sci. 2022, 12(17), 8797; https://doi.org/10.3390/app12178797 - 1 Sep 2022
Cited by 2 | Viewed by 1478
Abstract
This work addresses an evolutionary algorithmic approach to reduce the surplus pieces in selective assembly to increase success rates. A novel equal area amidst unequal bin numbers (EAUB) method is proposed for classifying the parts of the ball bearing assembly by considering the [...] Read more.
This work addresses an evolutionary algorithmic approach to reduce the surplus pieces in selective assembly to increase success rates. A novel equal area amidst unequal bin numbers (EAUB) method is proposed for classifying the parts of the ball bearing assembly by considering the various tolerance ranges of parts. The L16 orthogonal array is used for identifying the effectiveness of the proposed EAUB method through varying the number of bins of the parts of an assembly. Because of qualities such as minimal setting parameters, ease of understanding and implementation, and rapid convergence, the moth–flame optimization (MFO) algorithm is put forward in this work for identifying the optimal combination of bins of the parts of an assembly toward maximizing the percentage of the success rate of making assemblies. Computational results showed a 5.78% improvement in the success rate through the proposed approach compared with the past literature. The usage of the MFO algorithm is justified by comparing the computational results with the harmony search algorithm. Full article
(This article belongs to the Special Issue Evolutionary Algorithms and Large-Scale Real-World Applications)
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26 pages, 5889 KiB  
Article
Hybrid Artificial Intelligence Models with Multi Objective Optimization for Prediction of Tribological Behavior of Polytetrafluoroethylene Matrix Composites
by Musa Alhaji Ibrahim, Hüseyin Çamur, Mahmut A. Savaş, Alhassan Kawu Sabo, Mamunu Mustapha and Sani I. Abba
Appl. Sci. 2022, 12(17), 8671; https://doi.org/10.3390/app12178671 - 30 Aug 2022
Cited by 6 | Viewed by 1884
Abstract
This study presents multi-response optimization and prediction tribological behaviors polytetrafluoroethylene (PTFE) matrix composites. For multi-response optimization, the Taguchi model was hybridized with grey relational analysis to produce grey relational grades (GRG). A support vector regression (SVR) model was combined with novel Harris Hawks’ [...] Read more.
This study presents multi-response optimization and prediction tribological behaviors polytetrafluoroethylene (PTFE) matrix composites. For multi-response optimization, the Taguchi model was hybridized with grey relational analysis to produce grey relational grades (GRG). A support vector regression (SVR) model was combined with novel Harris Hawks’ optimization (HHO) and swarm particle optimization (PSO) models to form hybrid SVR–HHO and SVR–PSO models to predict the GRG. The prediction ability of the models was appraised using the coefficient of determination (R2), correlation coefficient (R), mean square error (MSE), root mean square (RMSE), and mean absolute percentage error (MAPE). The results of the multi-response optimization revealed that the optimal combination of parametric values of GRG for minimum tribological rate was 9 N-1000 mesh-0.14 ms−1-55 m (L3G1SD3SS3). An analysis of variance of the GRG showed that a grit size of 94.56% was the most significant parameter influencing the tribological behavior of PTFE matrix composites. The validation results revealed that an improvement of 52% in GRG was achieved. The prediction results of all models showed that the SVR–PSO and SVR–HHO models were superior to the SVR model. Furthermore, the SVR–HHO model produced superior prediction error and the best goodness of fit over the SVR–PSO model. These findings concluded that hybrids models are promising tools in the multi-response optimization and prediction of tribological behaviors of PTFE matrix composites. They can serve as a guide in the design and development of tribological materials. Full article
(This article belongs to the Special Issue Evolutionary Algorithms and Large-Scale Real-World Applications)
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22 pages, 2541 KiB  
Article
Multi-Objective Hybrid Flower Pollination Resource Consolidation Scheme for Large Cloud Data Centres
by Mohammed Joda Usman, Lubna A. Gabralla, Ahmed Aliyu, Danlami Gabi and Haruna Chiroma
Appl. Sci. 2022, 12(17), 8516; https://doi.org/10.3390/app12178516 - 25 Aug 2022
Cited by 4 | Viewed by 1728
Abstract
Cloud Computing has rapidly emerged as a successful paradigm for providing Information and Communication Technology (ICT) infrastructure. Resource allocation is used to execute user applications in the form of requests for consolidated resources in order to minimize energy consumption and violation of the [...] Read more.
Cloud Computing has rapidly emerged as a successful paradigm for providing Information and Communication Technology (ICT) infrastructure. Resource allocation is used to execute user applications in the form of requests for consolidated resources in order to minimize energy consumption and violation of the Service Level Agreement (SLA) for large-scale data centers resource utilization. The energy consumption is usually caused due to local entrapment and violation of SLA during resource assigning and execution. Several researchers have proposed solutions to reduce local entrapments and violations of SLA, to minimize the energy consumption of the entire data center. However, strategies employed in their solutions face entrapment in either local searches or at the global search level with a certain level of SLA violation. In this light, a Multi-Objective Hybrid Flower Pollination Resource Consolidation (MOH-FPRC) scheme for efficient and optimal resource consolidation of data center resources is put forward. The Local Neighborhood Search (LNS) algorithm has been employed for addressing entrapment at the local search level, while the prominent flower pollination algorithm is used to solve the problem of entrapment at the global search level. This, in turn, reduces the energy consumption of the data centers. In addition, clustering strategies have been introduced with a robust migration mechanism to minimize the violation of SLA while also satisfying minimum energy consumption. The simulation results using the MultiRecCloudSim simulator have shown that our proposed MOH-FPRC demonstrates an improved performance on the data center energy consumption, resource utilization, and SLA violation with a 20.5% decrease, 23.9% increase, and 13.5% reduction, respectively, as compared with the benchmarked algorithms. The proposed scheme has proven its efficiency in minimizing energy consumption while at the same time improving the data center resource allocation with minimum SLA violations. Full article
(This article belongs to the Special Issue Evolutionary Algorithms and Large-Scale Real-World Applications)
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18 pages, 3983 KiB  
Article
A Novel Method for Survival Prediction of Hepatocellular Carcinoma Using Feature-Selection Techniques
by Mona A. S. Ali, Rasha Orban, Rajalaxmi Rajammal Ramasamy, Suresh Muthusamy, Saanthoshkumar Subramani, Kavithra Sekar, Fathimathul Rajeena P. P., Ibrahim Abd Elatif Gomaa, Laith Abulaigh and Diaa Salam Abd Elminaam
Appl. Sci. 2022, 12(13), 6427; https://doi.org/10.3390/app12136427 - 24 Jun 2022
Cited by 9 | Viewed by 2462
Abstract
The World Health Organization (WHO) predicted that 10 million people would have died of cancer by 2020. According to recent studies, liver cancer is the most prevalent cancer worldwide. Hepatocellular carcinoma (HCC) is the leading cause of early-stage liver cancer. However, HCC occurs [...] Read more.
The World Health Organization (WHO) predicted that 10 million people would have died of cancer by 2020. According to recent studies, liver cancer is the most prevalent cancer worldwide. Hepatocellular carcinoma (HCC) is the leading cause of early-stage liver cancer. However, HCC occurs most frequently in patients with chronic liver conditions (such as cirrhosis). Therefore, it is important to predict liver cancer more explicitly by using machine learning. This study examines the survival prediction of a dataset of HCC based on three strategies. Originally, missing values are estimated using mean, mode, and k-Nearest Neighbor (k-NN). We then compare the different select features using the wrapper and embedded methods. The embedded method employs Least Absolute Shrinkage and Selection Operator (LASSO) and ridge regression in conjunction with Logistic Regression (LR). In the wrapper method, gradient boosting and random forests eliminate features recursively. Classification algorithms for predicting results include k-NN, Random Forest (RF), and Logistic Regression. The experimental results indicate that Recursive Feature Elimination with Gradient Boosting (RFE-GB) produces better results, with a 96.66% accuracy rate and a 95.66% F1-score. Full article
(This article belongs to the Special Issue Evolutionary Algorithms and Large-Scale Real-World Applications)
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16 pages, 2031 KiB  
Article
A Few-Shot Learning-Based Reward Estimation for Mapless Navigation of Mobile Robots Using a Siamese Convolutional Neural Network
by Vernon Kok, Micheal Olusanya and Absalom Ezugwu
Appl. Sci. 2022, 12(11), 5323; https://doi.org/10.3390/app12115323 - 25 May 2022
Cited by 2 | Viewed by 2128
Abstract
Deep reinforcement learning-based approaches to mapless navigation have relied on the distance to the goal state being known a priori or that the distance to the goal can be obtained at each timestep. In artificial or simulated environments, obtaining the distance to the [...] Read more.
Deep reinforcement learning-based approaches to mapless navigation have relied on the distance to the goal state being known a priori or that the distance to the goal can be obtained at each timestep. In artificial or simulated environments, obtaining the distance to the goal is considered a trivial task. Still, when applied to a real-world scenario, the distance must be obtained through complex localization techniques, and the use of localization techniques increases the complexity of the agent design. However, for agents navigating in unknown environments, using information about the goal to either form part of the state representation or act as the reward mechanism is usually expensive for both the robot design and for computing costs. This paper proposes using a pre-trained Siamese convolutional neural network (SCNN) to estimate the distance between an agent and its goal, thus enabling agents equipped with onboard cameras to navigate an unknown environment without needing localization sensors. This technique can be applied to environments where a goal location may be unknown, and the only information regarding the goal maybe a description of the goal state. Our experiments show that the Siamese network can learn the distance between the agent and its goal using relatively few training samples. Therefore, it is useful for mapless navigation using only visual state information and reduces the need for complex localization techniques. Full article
(This article belongs to the Special Issue Evolutionary Algorithms and Large-Scale Real-World Applications)
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21 pages, 2483 KiB  
Article
Passenger Flow-Oriented Metro Operation without Timetables
by Li He, Lei Chen, Jin Liu, Clive Roberts, Saijun Yu and Xujie Feng
Appl. Sci. 2022, 12(10), 4999; https://doi.org/10.3390/app12104999 - 15 May 2022
Viewed by 1908
Abstract
Unpredictable fluctuant passenger flow usually exists in urban metro operations. In this situation, traditional predetermined metro timetables cannot always meet the variation of passenger flow, and thus the service quality of the metro system could be affected profoundly. In this paper, by introducing [...] Read more.
Unpredictable fluctuant passenger flow usually exists in urban metro operations. In this situation, traditional predetermined metro timetables cannot always meet the variation of passenger flow, and thus the service quality of the metro system could be affected profoundly. In this paper, by introducing an innovative metro operation method without timetables, we develop a nonlinear integer programming model to continually optimise the train operation to deal with detected real-time passenger flow variations. We aim to minimise the total passenger waiting time in the research time horizon under the vehicle number constraint. A modified genetic algorithm integrated with a macroscopic metro simulator is adopted to solve the proposed model. A case study based on the Beijing Metro Line 19 is implemented to provide a quantitative result for evaluating the proposed passenger flow-oriented metro operation method without timetables. Compared to traditional timetable-based metro operation, the method could significantly improve the metro operation’s flexibility and the quality of services. Full article
(This article belongs to the Special Issue Evolutionary Algorithms and Large-Scale Real-World Applications)
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27 pages, 3568 KiB  
Article
Grid-Based Hybrid Genetic Approach to Relaxed Flexible Flow Shop with Sequence-Dependent Setup Times
by Fredy Juárez-Pérez, Marco Antonio Cruz-Chávez, Rafael Rivera-López, Erika Yesenia Ávila-Melgar, Marta Lilia Eraña-Díaz and Martín H. Cruz-Rosales
Appl. Sci. 2022, 12(2), 607; https://doi.org/10.3390/app12020607 - 9 Jan 2022
Cited by 3 | Viewed by 1996
Abstract
In this paper, a hybrid genetic algorithm implemented in a grid environment to solve hard instances of the flexible flow shop scheduling problem with sequence-dependent setup times is introduced. The genetic algorithm takes advantage of the distributed computing power on the grid to [...] Read more.
In this paper, a hybrid genetic algorithm implemented in a grid environment to solve hard instances of the flexible flow shop scheduling problem with sequence-dependent setup times is introduced. The genetic algorithm takes advantage of the distributed computing power on the grid to apply a hybrid local search to each individual in the population and reach a near optimal solution in a reduced number of generations. Ant colony systems and simulated annealing are used to apply a combination of iterative and cooperative local searches, respectively. This algorithm is implemented using a master–slave scheme, where the master process distributes the population on the slave process and coordinates the communication on the computational grid elements. The experimental results point out that the proposed scheme obtains the upper bound in a broad set of test instances. Also, an efficiency analysis of the proposed algorithm indicates its competitive use of the computational resources of the grid. Full article
(This article belongs to the Special Issue Evolutionary Algorithms and Large-Scale Real-World Applications)
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30 pages, 5495 KiB  
Article
Metaheuristic with Cooperative Processes for the University Course Timetabling Problem
by Martín H. Cruz-Rosales, Marco Antonio Cruz-Chávez, Federico Alonso-Pecina, Jesus del C. Peralta-Abarca, Erika Yesenia Ávila-Melgar, Beatriz Martínez-Bahena and Juana Enríquez-Urbano
Appl. Sci. 2022, 12(2), 542; https://doi.org/10.3390/app12020542 - 6 Jan 2022
Cited by 8 | Viewed by 2147
Abstract
This work presents a metaheuristic with distributed processing that finds solutions for an optimization model of the university course timetabling problem, where collective communication and point-to-point communication are applied, which are used to generate cooperation between processes. The metaheuristic performs the optimization process [...] Read more.
This work presents a metaheuristic with distributed processing that finds solutions for an optimization model of the university course timetabling problem, where collective communication and point-to-point communication are applied, which are used to generate cooperation between processes. The metaheuristic performs the optimization process with simulated annealing within each solution that each process works. The highlight of this work is presented in the algorithmic design for optimizing the problem by applying cooperative processes. In each iteration of the proposed heuristics, collective communication allows the master process to identify the process with the best solution and point-to-point communication allows the best solution to be sent to the master process so that it can be distributed to all the processes in progress in order to direct the search toward a space of solutions which is close to the best solution found at the time. This search is performed by applying simulated annealing. On the other hand, the mathematical representation of an optimization model present in the literature of the university course timing problem is performed. The results obtained in this work show that the proposed metaheuristics improves the results of other metaheuristics for all test instances. Statistical analysis shows that the proposed metaheuristic presents a different behavior from the other metaheuristics with which it is compared. Full article
(This article belongs to the Special Issue Evolutionary Algorithms and Large-Scale Real-World Applications)
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Review

Jump to: Research

49 pages, 3923 KiB  
Review
Expert System and Decision Support System for Electrocardiogram Interpretation and Diagnosis: Review, Challenges and Research Directions
by Kayode S. Adewole, Hammed A. Mojeed, James A. Ogunmodede, Lubna A. Gabralla, Nasir Faruk, Abubakar Abdulkarim, Emmanuel Ifada, Yusuf Y. Folawiyo, Abdukareem A. Oloyede, Lukman A. Olawoyin, Ismaeel A. Sikiru, Musa Nehemiah, Abdulsalam Ya’u Gital and Haruna Chiroma
Appl. Sci. 2022, 12(23), 12342; https://doi.org/10.3390/app122312342 - 2 Dec 2022
Cited by 4 | Viewed by 3663
Abstract
Electrocardiography (ECG) is one of the most widely used recordings in clinical medicine. ECG deals with the recording of electrical activity that is generated by the heart through the surface of the body. The electrical activity generated by the heart is measured using [...] Read more.
Electrocardiography (ECG) is one of the most widely used recordings in clinical medicine. ECG deals with the recording of electrical activity that is generated by the heart through the surface of the body. The electrical activity generated by the heart is measured using electrodes that are attached to the body surface. The use of ECG in the diagnosis and management of cardiovascular disease (CVD) has been in existence for over a decade, and research in this domain has recently attracted large attention. Along this line, an expert system (ES) and decision support system (DSS) have been developed for ECG interpretation and diagnosis. However, despite the availability of a lot of literature, access to recent and more comprehensive review papers on this subject is still a challenge. This paper presents a comprehensive review of the application of ES and DSS for ECG interpretation and diagnosis. Researchers have proposed a number of features and methods for ES and DSS development that can be used to monitor a patient’s health condition through ECG recordings. In this paper, a taxonomy of the features and methods for ECG interpretation and diagnosis were presented. The significance of the features and methods, as well as their limitations, were analyzed. This review further presents interesting theoretical concepts in this domain, as well as identifies challenges and open research issues on ES and DSS development for ECG interpretation and diagnosis that require substantial research effort. In conclusion, this paper identifies important future research areas with the purpose of advancing the development of ES and DSS for ECG interpretation and diagnosis. Full article
(This article belongs to the Special Issue Evolutionary Algorithms and Large-Scale Real-World Applications)
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34 pages, 3830 KiB  
Review
Initialisation Approaches for Population-Based Metaheuristic Algorithms: A Comprehensive Review
by Jeffrey O. Agushaka and Absalom E. Ezugwu
Appl. Sci. 2022, 12(2), 896; https://doi.org/10.3390/app12020896 - 17 Jan 2022
Cited by 36 | Viewed by 4530
Abstract
A situation where the set of initial solutions lies near the position of the true optimality (most favourable or desirable solution) by chance can increase the probability of finding the true optimality and significantly reduce the search efforts. In optimisation problems, the location [...] Read more.
A situation where the set of initial solutions lies near the position of the true optimality (most favourable or desirable solution) by chance can increase the probability of finding the true optimality and significantly reduce the search efforts. In optimisation problems, the location of the global optimum solution is unknown a priori, and initialisation is a stochastic process. In addition, the population size is equally important; if there are problems with high dimensions, a small population size may lie sparsely in unpromising regions, and may return suboptimal solutions with bias. In addition, the different distributions used as position vectors for the initial population may have different sampling emphasis; hence, different degrees of diversity. The initialisation control parameters of population-based metaheuristic algorithms play a significant role in improving the performance of the algorithms. Researchers have identified this significance, and they have put much effort into finding various distribution schemes that will enhance the diversity of the initial populations of the algorithms, and obtain the correct balance of the population size and number of iterations which will guarantee optimal solutions for a given problem set. Despite the affirmation of the role initialisation plays, to our knowledge few studies or surveys have been conducted on this subject area. Therefore, this paper presents a comprehensive survey of different initialisation schemes to improve the quality of solutions obtained by most metaheuristic optimisers for a given problem set. Popular schemes used to improve the diversity of the population can be categorised into random numbers, quasirandom sequences, chaos theory, probability distributions, hybrids of other heuristic or metaheuristic algorithms, Lévy, and others. We discuss the different levels of success of these schemes and identify their limitations. Similarly, we identify gaps and present useful insights for future research directions. Finally, we present a comparison of the effect of population size, the maximum number of iterations, and ten (10) different initialisation methods on the performance of three (3) population-based metaheuristic optimizers: bat algorithm (BA), Grey Wolf Optimizer (GWO), and butterfly optimization algorithm (BOA). Full article
(This article belongs to the Special Issue Evolutionary Algorithms and Large-Scale Real-World Applications)
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