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Waste generation is a continuous process that needs to be managed effectively to ensure environmental safety and public health. The recent circular economy (CE) practices have brought a new shape for the waste management industry, creating value from the generated waste. The shift to a CE represents one of the most significant challenges, particularly in sorting and classifying generated waste. Addressing these challenges would facilitate the recycling industry and helps in promoting remanufacturing. But in the COVID times, most of the generated waste is getting mixed with conventional waste types, especially in the global south. The pandemic has resulted in colossal infectious waste generation. Its handling became the most significant challenge raising fears and concerns over sorting and classifying. Hence, this study proposes an Artificial Intelligence (AI) based automated solution for sorting COVID related medical waste streams from other waste types and, at the same time, ensures data-driven decisions for recycling in the context of CE. Metal, paper, glass waste categories, including the polyethylene terephthalate (PET) waste from the pandemic, are considered. The waste type classification is done based on the image-texture-dependent features, which provided an accurate sorting and classification before the recycling process starts. The features are fused using the proposed decision-level feature fusion scheme. The classification model based on the support vector machine (SVM) classifier performs best (with 96.5 % accuracy, 95.3 % sensitivity, and 95.9 % specificity) in classifying waste types in the context of circular manufacturing and exhibiting the abilities to manage the COVID related medical waste mixed.
Nallapaneni Manoj Kumar; Mazin Abed Mohammed; Karrar Hameed Abdulkareem; Robertas Damasevicius; Salama A. Mostafa; Mashael S. Maashi; Shauhrat S. Chopra. Artificial Intelligence-based Solution for Sorting COVID Related Medical Waste Streams and Supporting Data-driven Decisions for Smart Circular Economy Practice. Process Safety and Environmental Protection 2021, 152, 482 -494.
AMA StyleNallapaneni Manoj Kumar, Mazin Abed Mohammed, Karrar Hameed Abdulkareem, Robertas Damasevicius, Salama A. Mostafa, Mashael S. Maashi, Shauhrat S. Chopra. Artificial Intelligence-based Solution for Sorting COVID Related Medical Waste Streams and Supporting Data-driven Decisions for Smart Circular Economy Practice. Process Safety and Environmental Protection. 2021; 152 ():482-494.
Chicago/Turabian StyleNallapaneni Manoj Kumar; Mazin Abed Mohammed; Karrar Hameed Abdulkareem; Robertas Damasevicius; Salama A. Mostafa; Mashael S. Maashi; Shauhrat S. Chopra. 2021. "Artificial Intelligence-based Solution for Sorting COVID Related Medical Waste Streams and Supporting Data-driven Decisions for Smart Circular Economy Practice." Process Safety and Environmental Protection 152, no. : 482-494.
Physicians depend on their insight and experience and on a fundamentally indicative or symptomatic approach to decide on the possible ailment of a patient. However, numerous phases of problem identification and longer strategies can prompt a longer time for consulting and can subsequently cause other patients that require attention to wait for longer. This can bring about pressure and tension concerning those patients. In this study, we focus on developing a decision-support system for diagnosing the symptoms as a result of hearing loss. The model is implemented by utilizing machine learning techniques. The Frequent Pattern Growth (FP-Growth) algorithm is used as a feature transformation method and the multivariate Bernoulli naïve Bayes classification model as the classifier. To find the correlation that exists between the hearing thresholds and symptoms of hearing loss, the FP-Growth and association rule algorithms were first used to experiment with small sample and large sample datasets. The result of these two experiments showed the existence of this relationship, and that the performance of the hybrid of the FP-Growth and naïve Bayes algorithms in identifying hearing-loss symptoms was found to be efficient, with a very small error rate. The average accuracy rate and average error rate for the multivariate Bernoulli model with FP-Growth feature transformation, using five training sets, are 98.25% and 1.73%, respectively.
Mohd Abd Ghani; Nasir Noma; Mazin Mohammed; Karrar Abdulkareem; Begonya Garcia-Zapirain; Mashael Maashi; Salama Mostafa. Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning Techniques. Sustainability 2021, 13, 5406 .
AMA StyleMohd Abd Ghani, Nasir Noma, Mazin Mohammed, Karrar Abdulkareem, Begonya Garcia-Zapirain, Mashael Maashi, Salama Mostafa. Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning Techniques. Sustainability. 2021; 13 (10):5406.
Chicago/Turabian StyleMohd Abd Ghani; Nasir Noma; Mazin Mohammed; Karrar Abdulkareem; Begonya Garcia-Zapirain; Mashael Maashi; Salama Mostafa. 2021. "Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning Techniques." Sustainability 13, no. 10: 5406.
Recently, there has been an advancement in the development of innovative computer-aided techniques for the segmentation and classification of retinal vessels, the application of which is predominant in clinical applications. Consequently, this study aims to provide a detailed overview of the techniques available for segmentation and classification of retinal vessels. Initially, retinal fundus photography and retinal image patterns are briefly introduced. Then, an introduction to the pre-processing operations and advanced methods of identifying retinal vessels is deliberated. In addition, a discussion on the validation stage and assessment of the outcomes of retinal vessels segmentation is presented. In this paper, the proposed methods of classifying arteries and veins in fundus images are extensively reviewed, which are categorized into automatic and semi-automatic categories. There are some challenges associated with the classification of vessels in images of the retinal fundus, which include the low contrast accompanying the fundus image and the inhomogeneity of the background lighting. The inhomogeneity occurs as a result of the process of imaging, whereas the low contrast which accompanies the image is caused by the variation between the background and the contrast of the various blood vessels. This means that the contrast of thicker vessels is higher than those that are thinner. Another challenge is related to the color changes that occur in the retina from different subjects, which are rooted in biological features. Most of the techniques used for the classification of the retinal vessels are based on geometric and visual characteristics that set the veins apart from the arteries. In this study, different major contributions are summarized as review studies that adopted deep learning approaches and machine learning techniques to address each of the limitations and problems in retinal blood vessel segmentation and classification techniques. We also review the current challenges, knowledge gaps and open issues, limitations and problems in retinal blood vessel segmentation and classification techniques.
Aws A. Abdulsahib; Moamin A. Mahmoud; Mazin Abed Mohammed; Hind Hameed Rasheed; Salama A. Mostafa; Mashael S. Maashi. Comprehensive review of retinal blood vessel segmentation and classification techniques: intelligent solutions for green computing in medical images, current challenges, open issues, and knowledge gaps in fundus medical images. Network Modeling Analysis in Health Informatics and Bioinformatics 2021, 10, 1 -32.
AMA StyleAws A. Abdulsahib, Moamin A. Mahmoud, Mazin Abed Mohammed, Hind Hameed Rasheed, Salama A. Mostafa, Mashael S. Maashi. Comprehensive review of retinal blood vessel segmentation and classification techniques: intelligent solutions for green computing in medical images, current challenges, open issues, and knowledge gaps in fundus medical images. Network Modeling Analysis in Health Informatics and Bioinformatics. 2021; 10 (1):1-32.
Chicago/Turabian StyleAws A. Abdulsahib; Moamin A. Mahmoud; Mazin Abed Mohammed; Hind Hameed Rasheed; Salama A. Mostafa; Mashael S. Maashi. 2021. "Comprehensive review of retinal blood vessel segmentation and classification techniques: intelligent solutions for green computing in medical images, current challenges, open issues, and knowledge gaps in fundus medical images." Network Modeling Analysis in Health Informatics and Bioinformatics 10, no. 1: 1-32.
Fatimah O. Albalawi; Mashael S. Maashi. Selection and Optimization of Software Development Life Cycles Using a Genetic Algorithm. Intelligent Automation & Soft Computing 2021, 28, 39 -52.
AMA StyleFatimah O. Albalawi, Mashael S. Maashi. Selection and Optimization of Software Development Life Cycles Using a Genetic Algorithm. Intelligent Automation & Soft Computing. 2021; 28 (1):39-52.
Chicago/Turabian StyleFatimah O. Albalawi; Mashael S. Maashi. 2021. "Selection and Optimization of Software Development Life Cycles Using a Genetic Algorithm." Intelligent Automation & Soft Computing 28, no. 1: 39-52.
Hissah A. Ben Zayed; Mashael S. Maashi. Optimizing the Software Testing Problem Using Search-Based Software Engineering Techniques. Intelligent Automation & Soft Computing 2021, 29, 307 -318.
AMA StyleHissah A. Ben Zayed, Mashael S. Maashi. Optimizing the Software Testing Problem Using Search-Based Software Engineering Techniques. Intelligent Automation & Soft Computing. 2021; 29 (1):307-318.
Chicago/Turabian StyleHissah A. Ben Zayed; Mashael S. Maashi. 2021. "Optimizing the Software Testing Problem Using Search-Based Software Engineering Techniques." Intelligent Automation & Soft Computing 29, no. 1: 307-318.
The outbreaks of Coronavirus (COVID-19) epidemic have increased the pressure on healthcare and medical systems worldwide. The timely diagnosis of infected patients is a critical step to limit the spread of the COVID-19 epidemic. The chest radiography imaging has shown to be an effective screening technique in diagnosing the COVID-19 epidemic. To reduce the pressure on radiologists and control of the epidemic, fast and accurate a hybrid deep learning framework for diagnosing COVID-19 virus in chest X-ray images is developed and termed as the COVID-CheXNet system. First, the contrast of the X-ray image was enhanced and the noise level was reduced using the contrast-limited adaptive histogram equalization and Butterworth bandpass filter, respectively. This was followed by fusing the results obtained from two different pre-trained deep learning models based on the incorporation of a ResNet34 and high-resolution network model trained using a large-scale dataset. Herein, the parallel architecture was considered, which provides radiologists with a high degree of confidence to discriminate between the healthy and COVID-19 infected people. The proposed COVID-CheXNet system has managed to correctly and accurately diagnose the COVID-19 patients with a detection accuracy rate of 99.99%, sensitivity of 99.98%, specificity of 100%, precision of 100%, F1-score of 99.99%, MSE of 0.011%, and RMSE of 0.012% using the weighted sum rule at the score-level. The efficiency and usefulness of the proposed COVID-CheXNet system are established along with the possibility of using it in real clinical centers for fast diagnosis and treatment supplement, with less than 2 s per image to get the prediction result.
Alaa S. Al-Waisy; Shumoos Al-Fahdawi; Mazin Abed Mohammed; Karrar Hameed Abdulkareem; Salama A. Mostafa; Mashael S. Maashi; Muhammad Arif; Begonya Garcia-Zapirain. COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images. Soft Computing 2020, 1 -16.
AMA StyleAlaa S. Al-Waisy, Shumoos Al-Fahdawi, Mazin Abed Mohammed, Karrar Hameed Abdulkareem, Salama A. Mostafa, Mashael S. Maashi, Muhammad Arif, Begonya Garcia-Zapirain. COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images. Soft Computing. 2020; ():1-16.
Chicago/Turabian StyleAlaa S. Al-Waisy; Shumoos Al-Fahdawi; Mazin Abed Mohammed; Karrar Hameed Abdulkareem; Salama A. Mostafa; Mashael S. Maashi; Muhammad Arif; Begonya Garcia-Zapirain. 2020. "COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images." Soft Computing , no. : 1-16.
The discrimination of non-focal class (NFC) and focal class (FC), is vital in localizing the epileptogenic zone (EZ) during neurosurgery. In the conventional diagnosis method, the neurologist has to visually examine the long hour electroencephalogram (EEG) signals, which consumes time and is prone to error. Hence, in this present work, automated diagnosis of FC EEG signals from NFC EEG signals is developed using the Fast Walsh–Hadamard Transform (FWHT) method, entropies, and artificial neural network (ANN). The FWHT analyzes the EEG signals in the frequency domain and decomposes it into the Hadamard coefficients. Five different nonlinear features, namely approximate entropy (ApEn), log-energy entropy (LogEn), fuzzy entropy (FuzzyEn), sample entropy (SampEn), and permutation entropy (PermEn) are extracted from the decomposed Hadamard coefficients. The extracted features detail the nonlinearity in the NFC and the FC EEG signals. The judicious entropy features are supplied to the ANN classifier, with a 10-fold cross-validation method to classify the NFC and FC classes. Two publicly available datasets such as the University of Bonn and Bern-Barcelona dataset are used to evaluate the proposed approach. A maximum sensitivity of 99.70%, the accuracy of 99.50%, and specificity of 99.30% with the 3750 pairs of NFC and FC signal are achieved using the Bern-Barcelona dataset, while the accuracy of 92.80%, the sensitivity of 91%, and specificity of 94.60% is achieved using University of Bonn dataset. Compared to the existing technique, the proposed approach attained a maximum classification performance in both the dataset.
Prasanna J.; M. S. P. Subathra; Mazin Abed Mohammed; Mashael S. Maashi; Begonya Garcia-Zapirain; N. J. Sairamya; S. Thomas George. Detection of Focal and Non-Focal Electroencephalogram Signals Using Fast Walsh-Hadamard Transform and Artificial Neural Network. Sensors 2020, 20, 4952 .
AMA StylePrasanna J., M. S. P. Subathra, Mazin Abed Mohammed, Mashael S. Maashi, Begonya Garcia-Zapirain, N. J. Sairamya, S. Thomas George. Detection of Focal and Non-Focal Electroencephalogram Signals Using Fast Walsh-Hadamard Transform and Artificial Neural Network. Sensors. 2020; 20 (17):4952.
Chicago/Turabian StylePrasanna J.; M. S. P. Subathra; Mazin Abed Mohammed; Mashael S. Maashi; Begonya Garcia-Zapirain; N. J. Sairamya; S. Thomas George. 2020. "Detection of Focal and Non-Focal Electroencephalogram Signals Using Fast Walsh-Hadamard Transform and Artificial Neural Network." Sensors 20, no. 17: 4952.
Voice pathology disorders can be effectively detected using computer-aided voice pathology classification tools. These tools can diagnose voice pathologies at an early stage and offering appropriate treatment. This study aims to develop a powerful feature extraction voice pathology detection tool based on Deep Learning. In this paper, a pre-trained Convolutional Neural Network (CNN) was applied to a dataset of voice pathology to maximize the classification accuracy. This study also proposes a distinguished training method combined with various training strategies in order to generalize the application of the proposed system on a wide range of problems related to voice disorders. The proposed system has tested using a voice database, namely the Saarbrücken voice database (SVD). The experimental results show the proposed CNN method for speech pathology detection achieves accuracy up to 95.41%. It also obtains 94.22% and 96.13% for F1-Score and Recall. The proposed system shows a high capability of the real-clinical application that offering a fast-automatic diagnosis and treatment solutions within 3 s to achieve the classification accuracy.
Mazin Abed Mohammed; Karrar Hameed Abdulkareem; Salama A. Mostafa; Mohd Khanapi Abd Ghani; Mashael S. Maashi; Begonya Garcia-Zapirain; Ibon Oleagordia; Hosam AlHakami; Fahad Taha Al-Dhief. Voice Pathology Detection and Classification Using Convolutional Neural Network Model. Applied Sciences 2020, 10, 3723 .
AMA StyleMazin Abed Mohammed, Karrar Hameed Abdulkareem, Salama A. Mostafa, Mohd Khanapi Abd Ghani, Mashael S. Maashi, Begonya Garcia-Zapirain, Ibon Oleagordia, Hosam AlHakami, Fahad Taha Al-Dhief. Voice Pathology Detection and Classification Using Convolutional Neural Network Model. Applied Sciences. 2020; 10 (11):3723.
Chicago/Turabian StyleMazin Abed Mohammed; Karrar Hameed Abdulkareem; Salama A. Mostafa; Mohd Khanapi Abd Ghani; Mashael S. Maashi; Begonya Garcia-Zapirain; Ibon Oleagordia; Hosam AlHakami; Fahad Taha Al-Dhief. 2020. "Voice Pathology Detection and Classification Using Convolutional Neural Network Model." Applied Sciences 10, no. 11: 3723.
ABSTRACT Nowadays, coronavirus (COVID-19) is getting international attention due it considered as a life-threatened epidemic disease that hard to control the spread of infection around the world. Machine learning (ML) is one of intelligent technique that able to automatically predict the event with reasonable accuracy based on the experience and learning process. In the meantime, a rapid number of ML models have been proposed for predicate the cases of COVID-19. Thus, there is need for an evaluation and benchmarking of COVID-19 ML models which considered the main challenge of this study. Furthermore, there is no single study have addressed the problem of evaluation and benchmarking of COVID diagnosis models. However, this study proposed an intelligent methodology is to help the health organisations in the selection COVID-19 diagnosis system. The benchmarking and evaluation of diagnostic models for COVID-19 is not a trivial process. There are multiple criteria requires to evaluate and some of the criteria are conflicting with each other. Our study is formulated as a decision matrix (DM) that embedded mix of ten evaluation criteria and twelve diagnostic models for COVID-19. The multi-criteria decision-making (MCDM) method is employed to evaluate and benchmarking the different diagnostic models for COVID19 with respect to the evaluation criteria. An integrated MCDM method are proposed where TOPSIS applied for the benchmarking and ranking purpose while Entropy used to calculate the weights of criteria. The study results revealed that the benchmarking and selection problems associated with COVID19 diagnosis models can be effectively solved using the integration of Entropy and TOPSIS. The SVM (linear) classifier is selected as the best diagnosis model for COVID19 with the closeness coefficient value of 0.9899 for our case study data. Furthermore, the proposed methodology has solved the significant variance for each criterion in terms of ideal best and worst best value, beside issue when specific diagnosis models have same ideal best value.
Mazin Abed Mohammed; Karrar Hameed Abdulkareem; Alaa S. Al-Waisy; Salama A. Mostafa; Shumoos Al-Fahdawi; Ahmed Musa Dinar; Wajdi Alhakami; Abdullah Baz; Mohammed Nasser Al-Mhiqani; Hosam Alhakami; Nureize Arbaiy; Mashael S. Maashi; Ammar Awad Mutlag; Begonya Garcia-Zapirain; Isabel De La Torre Diez. Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods. IEEE Access 2020, 8, 99115 -99131.
AMA StyleMazin Abed Mohammed, Karrar Hameed Abdulkareem, Alaa S. Al-Waisy, Salama A. Mostafa, Shumoos Al-Fahdawi, Ahmed Musa Dinar, Wajdi Alhakami, Abdullah Baz, Mohammed Nasser Al-Mhiqani, Hosam Alhakami, Nureize Arbaiy, Mashael S. Maashi, Ammar Awad Mutlag, Begonya Garcia-Zapirain, Isabel De La Torre Diez. Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods. IEEE Access. 2020; 8 (99):99115-99131.
Chicago/Turabian StyleMazin Abed Mohammed; Karrar Hameed Abdulkareem; Alaa S. Al-Waisy; Salama A. Mostafa; Shumoos Al-Fahdawi; Ahmed Musa Dinar; Wajdi Alhakami; Abdullah Baz; Mohammed Nasser Al-Mhiqani; Hosam Alhakami; Nureize Arbaiy; Mashael S. Maashi; Ammar Awad Mutlag; Begonya Garcia-Zapirain; Isabel De La Torre Diez. 2020. "Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods." IEEE Access 8, no. 99: 99115-99131.
In healthcare applications, numerous sensors and devices produce massive amounts of data which are the focus of critical tasks. Their management at the edge of the network can be done by Fog computing implementation. However, Fog Nodes suffer from lake of resources That could limit the time needed for final outcome/analytics. Fog Nodes could perform just a small number of tasks. A difficult decision concerns which tasks will perform locally by Fog Nodes. Each node should select such tasks carefully based on the current contextual information, for example, tasks’ priority, resource load, and resource availability. We suggest in this paper a Multi-Agent Fog Computing model for healthcare critical tasks management. The main role of the multi-agent system is mapping between three decision tables to optimize scheduling the critical tasks by assigning tasks with their priority, load in the network, and network resource availability. The first step is to decide whether a critical task can be processed locally; otherwise, the second step involves the sophisticated selection of the most suitable neighbor Fog Node to allocate it. If no Fog Node is capable of processing the task throughout the network, it is then sent to the Cloud facing the highest latency. We test the proposed scheme thoroughly, demonstrating its applicability and optimality at the edge of the network using iFogSim simulator and UTeM clinic data.
Ammar Awad Mutlag; Mohd Khanapi Abd Ghani; Mazin Abed Mohammed; Mashael S. Maashi; Othman Mohd; Salama A. Mostafa; Karrar Hameed Abdulkareem; Gonçalo Marques; Isabel De La Torre Díez. MAFC: Multi-Agent Fog Computing Model for Healthcare Critical Tasks Management. Sensors 2020, 20, 1853 .
AMA StyleAmmar Awad Mutlag, Mohd Khanapi Abd Ghani, Mazin Abed Mohammed, Mashael S. Maashi, Othman Mohd, Salama A. Mostafa, Karrar Hameed Abdulkareem, Gonçalo Marques, Isabel De La Torre Díez. MAFC: Multi-Agent Fog Computing Model for Healthcare Critical Tasks Management. Sensors. 2020; 20 (7):1853.
Chicago/Turabian StyleAmmar Awad Mutlag; Mohd Khanapi Abd Ghani; Mazin Abed Mohammed; Mashael S. Maashi; Othman Mohd; Salama A. Mostafa; Karrar Hameed Abdulkareem; Gonçalo Marques; Isabel De La Torre Díez. 2020. "MAFC: Multi-Agent Fog Computing Model for Healthcare Critical Tasks Management." Sensors 20, no. 7: 1853.
Multi‐objective hyper‐heuristics is a search method or learning mechanism that operates over a fixed set of low‐level heuristics to solve multi‐objective optimization problems by controlling and combining the strengths of those heuristics. Although numerous papers on hyper‐heuristics have been published and several studies are still underway, most research has focused on single‐objective optimization. Work on hyper‐heuristics for multi‐objective optimization remains limited. This chapter draws attention to this area of research to help researchers and PhD students understand and reuse these methods. It also provides the basic concepts of multi‐objective optimization and hyper‐heuristics to facilitate a better understanding of the related research areas, in addition to exploring hyper‐heuristic methodologies that address multi‐objective optimization. Some design issues related to the development of hyper‐heuristic framework for multi‐objective optimization are discussed. The chapter concludes with a case study of multi‐objective selection hyper‐heuristics and its application on a real‐world problem.
Mashael Maashi. Multi‐Objective Hyper‐Heuristics. Heuristics and Hyper-Heuristics - Principles and Applications 2017, 1 .
AMA StyleMashael Maashi. Multi‐Objective Hyper‐Heuristics. Heuristics and Hyper-Heuristics - Principles and Applications. 2017; ():1.
Chicago/Turabian StyleMashael Maashi. 2017. "Multi‐Objective Hyper‐Heuristics." Heuristics and Hyper-Heuristics - Principles and Applications , no. : 1.
A selection hyper-heuristic is a high level search methodology which operates over a fixed set of low level heuristics. During the iterative search process, a heuristic is selected and applied to a candidate solution in hand, producing a new solution which is then accepted or rejected at each step. Selection hyper-heuristics have been increasingly, and successfully, applied to single-objective optimization problems, while work on multi-objective selection hyper-heuristics is limited. This work presents one of the initial studies on selection hyper-heuristics combining a choice function heuristic selection methodology with great deluge and late acceptance as non-deterministic move acceptance methods for multi-objective optimization. A well-known hypervolume metric is integrated into the move acceptance methods to enable the approaches to deal with multi-objective problems. The performance of the proposed hyper-heuristics is investigated on the Walking Fish Group test suite which is a common benchmark for multi-objective optimization. Additionally, they are applied to the vehicle crashworthiness design problem as a real-world multi-objective problem. The experimental results demonstrate the effectiveness of the non-deterministic move acceptance, particularly great deluge when used as a component of a choice function based selection hyper-heuristic
Mashael Maashi; Graham Kendall; Ender Özcan. Choice function based hyper-heuristics for multi-objective optimization. Applied Soft Computing 2015, 28, 312 -326.
AMA StyleMashael Maashi, Graham Kendall, Ender Özcan. Choice function based hyper-heuristics for multi-objective optimization. Applied Soft Computing. 2015; 28 ():312-326.
Chicago/Turabian StyleMashael Maashi; Graham Kendall; Ender Özcan. 2015. "Choice function based hyper-heuristics for multi-objective optimization." Applied Soft Computing 28, no. : 312-326.
Hyper-heuristics are emerging methodologies that perform a search over the space of heuristics in an attempt to solve difficult computational optimization problems. We present a learning selection choice function based hyper-heuristic to solve multi-objective optimization problems. This high level approach controls and combines the strengths of three well-known multi-objective evolutionary algorithms (i.e. NSGAII, SPEA2 and MOGA), utilizing them as the low level heuristics. The performance of the proposed learning hyper-heuristic is investigated on the Walking Fish Group test suite which is a common benchmark for multi-objective optimization. Additionally, the proposed hyper-heuristic is applied to the vehicle crashworthiness design problem as a real-world multi-objective problem. The experimental results demonstrate the effectiveness of the hyper-heuristic approach when compared to the performance of each low level heuristic run on its own, as well as being compared to other approaches including an adaptive multi-method search, namely AMALGAM.
Mashael Maashi; Ender Özcan; Graham Kendall. A multi-objective hyper-heuristic based on choice function. Expert Systems with Applications 2014, 41, 4475 -4493.
AMA StyleMashael Maashi, Ender Özcan, Graham Kendall. A multi-objective hyper-heuristic based on choice function. Expert Systems with Applications. 2014; 41 (9):4475-4493.
Chicago/Turabian StyleMashael Maashi; Ender Özcan; Graham Kendall. 2014. "A multi-objective hyper-heuristic based on choice function." Expert Systems with Applications 41, no. 9: 4475-4493.