Article
Computer Science, Artificial Intelligence
Wei Zhang, Jianchang Liu, Junhua Liu, Yuanchao Liu, Honghai Wang
Summary: This paper proposes a many-objective evolutionary algorithm to tackle the challenges of many-objective optimization problems. Through novel fitness estimation and grouping layering strategy, the algorithm increases selection pressure and maintains diversity. Experimental studies show that the algorithm is highly competitive compared to existing methods.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Automation & Control Systems
Hui Bai, Ran Cheng, Danial Yazdani, Kay Chen Tan, Yaochu Jin
Summary: This paper proposes a bilevel variable grouping (BLVG)-based framework to address the issue of variable grouping in large-scale dynamic optimization when cooperating with multipopulation strategies. The framework outperforms several state-of-the-art frameworks for large-scale dynamic optimization, as shown by empirical studies.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Information Systems
An Chen, Zhigang Ren, Muyi Wang, Yongsheng Liang, Hanqing Liu, Wenhao Du
Summary: Problem decomposition is important in the application of cooperative coevolution (CC) for large-scale global optimization problems. A new decomposition algorithm called surrogate-assisted variable grouping (SVG) is proposed in this study, which utilizes a general-separability-oriented detection criterion to achieve high accuracy. The SVG algorithm reduces fitness evaluations by using a surrogate model and converts the variable-grouping process into a search process in a binary tree. Experimental results demonstrate that SVG outperforms six state-of-the-art decomposition algorithms in terms of accuracy and efficiency on both additively and nonadditively separable problems, significantly improving the optimization performance of CC.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Software Engineering
Jinglei Guo, Chen Gao, Shouyong Jiang, Wei Xie, Zhijian Wu
Summary: This article presents an improved population-based intelligence algorithm called CIBSO, which uses new grouping and mutation strategies to enhance the efficiency of optimization problems. Experimental results show that CIBSO performs better or at least competitively compared to other algorithms.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2023)
Article
Computer Science, Interdisciplinary Applications
Jaeeun Yoo, In Gwun Jang, Ikjin Lee
Summary: This paper proposes an efficient multi-resolution topology optimization method that significantly reduces computational burden in optimization by using a reduced number of design variables.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2021)
Article
Computer Science, Information Systems
Maoqing Zhang, Wuzhao Li, Liang Zhang, Hao Jin, Yashuang Mu, Lei Wang
Summary: This paper proposes a Pearson correlation-based adaptive variable grouping method to address the computational expense and inflexibility issues of regular grouping methods in tackling large-scale multi-objective problems. The method utilizes the Pearson correlation coefficient to measure the similarities of evolutionary trends of variables, enabling adaptive variable division without additional computational budget. Based on this method, a weighted optimization framework is designed. Experiment and analysis results demonstrate the superiority of the proposed method and the weighted optimization framework over existing methods.
INFORMATION SCIENCES
(2023)
Article
Engineering, Electrical & Electronic
Anna Pietrenko-Dabrowska, Slawomir Koziel
Summary: Numerical optimization is increasingly important in contemporary antenna system design. Due to the lack of ready theoretical models, optimization is mainly based on expensive electromagnetic analysis. Various techniques have been developed to reduce costs, including surrogate-assisted frameworks and sparse sensitivity updates. This study introduces an accelerated gradient-based algorithm with sparse sensitivity updates and variable-resolution EM simulations, achieving significant computational savings.
IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION
(2022)
Article
Mathematics, Applied
Arian Novruzi, Bartosz Protas
Summary: In this paper, an accelerated version of the classical gradient method is proposed for unconstrained optimization problems defined on a Sobolev space H with Hilbert structure. By choosing suitable weights, this method achieves improved convergence compared to the classical gradient method and demonstrates quadratic convergence properties. The effectiveness of the proposed method is verified through numerical experiments.
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
(2023)
Article
Engineering, Electrical & Electronic
Slawomir Koziel, Anna Pietrenko-Dabrowska
Summary: This article proposes a novel approach to fast and improved-reliability gradient-based optimization of antenna structures, which utilizes frequency-based regularization and variable-resolution EM models to enhance design quality and computational efficiency.
IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION
(2022)
Article
Computer Science, Artificial Intelligence
Jie Tian, Mingdong Hou, Hongli Bian, Junqing Li
Summary: Due to the curse of dimensionality, applying surrogate-assisted evaluation algorithms (SAEAs) to high-dimensional expensive problems remains challenging. This paper proposes a variable surrogate model-based particle swarm optimization (VSMPSO) and extends it to solve 200-dimensional problems. By constructing a single surrogate model through simple random sampling, different promising areas are explored in different iterations. The variable model management strategy is used to better utilize the current global model and accelerate the convergence rate of the optimizer. Comparisons with state-of-the-art algorithms demonstrate that VSMPSO achieves high-quality solutions and computational efficiency for high-dimensional problems.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Engineering, Marine
Dong Yin, Yifeng Niu, Jian Yang, Shaobo Yu
Summary: This paper studies the static discrete berth allocation problems for large-scale time-critical marine-loading scenarios, and proposes an iterative variable grouping genetic algorithm to search for near-optimal berth allocation plans. Numerical experiments demonstrate the effectiveness of the algorithm, showing good performance when the number of vessels is kept at a medium scale.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Information Systems
Diego Oliva, Noe Ortega-Sanchez, Mario A. Navarro, Alfonso Ramos-Michel, Mohammed El-Abd, Seyed Jalaleddin Mousavirad, Mohammad H. Nadimi-Shahraki
Summary: This paper proposes a combination of the minimum cross-entropy method and the Global-best brain storm optimization algorithm for image segmentation. The method aims to find the best configuration of thresholds by optimizing the minimum cross entropy, and extract regions of interest.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Ali Mohammadi, Seyed Hamid Zahiri, Seyyed Mohammad Razavi, Ponnuthurai Nagaratnam Suganthan
Summary: This paper introduces an efficient approach based on a variable length particle swarm optimization algorithm for designing optimal IIR filters, aiming to minimize the order intelligently and reduce design complexity. The inclusion of a weighted sum objective function and Optimum Modeling Indicator ensures the optimality of the systems, with application to real-world engineering optimization problems such as sensor coverage.
APPLIED SOFT COMPUTING
(2021)
Article
Mathematical & Computational Biology
Moritz Hanke, Louis Dijkstra, Ronja Foraita, Vanessa Didelez
Summary: This study evaluates variable selection in linear regressions and compares the performance of best subset selection, forward stepwise selection, Lasso, and Elastic net. It finds that best subset selection reliably outperforms other methods only in settings with high signal-to-noise ratio and uncorrelated variables. However, in practical settings, alternatives like Elastic net are faster and appear to perform better, especially for correlated variables.
BIOMETRICAL JOURNAL
(2023)
Article
Operations Research & Management Science
Jian Chen, Gao-Xi Li, Xin-Min Yang
Summary: In this paper, a variable metric method is proposed for solving unconstrained multiobjective optimization problems (MOPs). The method generates a sequence of points using different positive definite matrices and proves that the accumulation points of the sequence are Pareto critical points. The proposed method achieves strong convergence without the assumption of convexity. Additionally, a common matrix is used to approximate the Hessian matrices of all objective functions, and a new nonmonotone line search technique is introduced to achieve local superlinear convergence rate. Numerical results demonstrate the effectiveness of the proposed method.
JOURNAL OF THE OPERATIONS RESEARCH SOCIETY OF CHINA
(2023)
Article
Computer Science, Hardware & Architecture
Issam Damaj, Mohamed Elshafei, Mohammed El-Abd, Mehmet Emin Aydin
MICROPROCESSORS AND MICROSYSTEMS
(2020)
Article
Computer Science, Artificial Intelligence
Iyad Abu Doush, Mohammed El-Abd, Abdelaziz I. Hammouri, Mohammad Qasem Bataineh
Summary: This paper tests and compares six different EMO algorithms using four stopping criteria to analyze the proper stopping criteria for different algorithms.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Engineering, Multidisciplinary
Rehab Ali Ibrahim, Mohamed Abd Elaziz, Ahmed A. Ewees, Mohammed El-Abd, Songfeng Lu
Summary: Feature selection is a crucial step in data mining, with various methods available in literature. Finding the best settings for components to determine relevant features is challenging, but a hyper-heuristic based approach shows promising results in performance improvement.
APPLIED MATHEMATICAL MODELLING
(2021)
Review
Engineering, Civil
Palwasha W. Shaikh, Mohammed El-Abd, Mounib Khanafer, Kaizhou Gao
Summary: The rapid development of urban cities and the increase in population has led to a significant increase in the number of vehicles on the roads, resulting in severe traffic congestion. Short-term, expensive, and short-sighted road expansions are no longer suitable, and alternative solutions are needed. The use of evolutionary and swarm intelligence algorithms to optimize traffic signal control is an effective method. This paper provides a comprehensive literature review on the applications of these algorithms to traffic signal control, categorizing the surveyed work based on decision variables, optimization objectives, problem modeling, and solution encoding. Based on identified gaps, the paper identifies promising future research directions and discusses the future of research in this field.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Multidisciplinary
Bahareh Etaati, Amin Abdollahi Dehkordi, Ali Sadollah, Mohammed El-Abd, Mehdi Neshat
Summary: This paper proposes a comparative truss optimization framework using twelve state-of-the-art bio-inspired algorithms to solve large-scale structural optimization problems, and the results show that the marine predators algorithm outperforms other algorithms in terms of convergence speed and the quality of the proposed designs.
MATHEMATICAL PROBLEMS IN ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Ali Kelkawi, Mohammed El-Abd, Imtiaz Ahmad
Summary: In this paper, a parallel implementation of the cooperative coevolution framework for solving continuous large-scale optimization problems is proposed, utilizing GPU and CUDA platform to optimize problem subcomponents in parallel, leading to significant speedup in the optimization process.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Iyad Abu Doush, Mohammed Azmi Al-Betar, Mohammed A. Awadallah, Zaid Abdi Alkareem Alyasseri, Sharif Naser Makhadmeh, Mohammed El-Abd
Summary: This paper proposes an island neighboring heuristics harmony search algorithm (INHS) to solve blocking flow-shop scheduling problem. The algorithm enhances its performance by diversifying the population using the island model and improving solution quality using neighboring heuristics. Experimental results demonstrate the efficiency and competitiveness of the proposed algorithm in solving instances from different datasets.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Information Systems
Diego Oliva, Noe Ortega-Sanchez, Mario A. Navarro, Alfonso Ramos-Michel, Mohammed El-Abd, Seyed Jalaleddin Mousavirad, Mohammad H. Nadimi-Shahraki
Summary: This paper proposes a combination of the minimum cross-entropy method and the Global-best brain storm optimization algorithm for image segmentation. The method aims to find the best configuration of thresholds by optimizing the minimum cross entropy, and extract regions of interest.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Nada A. Nabeeh, Mai Mohamed, Ahmed Abdel-Monem, Mohamed Abdel-Basset, Karam M. Salim, Mohammed El-Abd, Ali Wagdy
Summary: This research explores the applicability of blockchain technology in supply chain management and proposes an evaluation model based on neutrosophic sets and multi-criteria decision making methods. Through a case study, it is found that the medicine segment is the recommended alternative, while the insurance and jewelry segments are not recommended in certain methods.
2022 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Nada A. Nabeeh, Ahmed Abdel-Monem, Mai Mohamed, Karam M. Sallam, Mohamed Abdel-Basset, Mohammed El-Abd, Ali Wagdy
Summary: The rapid growth of the economy has made the manufacturing process a focus of attention in politics, society, and communities. Manufacturing selection is a complex multi-criteria decision-making issue. Finding suitable multi-criteria decision-making methods for the manufacturing process is crucial for achieving ideal manufacturing. This study proposes a hybrid methodology that combines neutrosophic theory with several MCDM techniques for manufacturing selection. The proposed methodology is evaluated using factors such as computational complexity, adequacy to changes in criteria, and agility. An empirical study is conducted to illustrate the suitability and applicability of the methodology. The results show that the proposed hybrid methodology is convenient and effective for manufacturing selection. A comparative study is also conducted, and the results suggest that the AHP technique performs well in terms of agility, while the MABAC technique performs well in terms of computational complexity. MABAC, MULTIMOORA, and TOPSIS techniques are recommended for their adequacy to changes in criteria. Overall, this comparative study provides valuable insights for decision makers and researchers in selecting the most suitable methodology for the manufacturing selection process.
2022 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Karam M. Sallam, Mohamed Abdel-Basset, Mohammed El-Abd, Ali Wagdy
Summary: This paper introduces an improved Multi-Operator Differential Evolution algorithm (IMODEII), which uses Reinforcement Learning as an adaptive operator selection approach. The performance of IMODEII is tested on benchmark functions from the CEC2022 competition, showing its efficiency.
2022 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Iyad Abu Doush, Mohammed Azmi Al-Betar, Mohammed A. Awadallah, Abdelaziz Hammouri, Mohammed El-Abd
2020 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI)
(2020)
Proceedings Paper
Education, Scientific Disciplines
Mounib Khanafer, Mohammed El-Abd
PROCEEDINGS OF THE 2020 IEEE GLOBAL ENGINEERING EDUCATION CONFERENCE (EDUCON 2020)
(2020)
Proceedings Paper
Education, Scientific Disciplines
Batool Hasan, Yara Al-Quorashy, Shahad Al-Mousa, Yousef Al-Sahhaf, Mohammed El-Abd
PROCEEDINGS OF THE 2020 IEEE GLOBAL ENGINEERING EDUCATION CONFERENCE (EDUCON 2020)
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Mohammed El-Abd, Kunjie Yu, Shilei Ge
2020 12TH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTATIONAL INTELLIGENCE (ICACI)
(2020)
Article
Computer Science, Artificial Intelligence
Guiliang Gong, Jiuqiang Tang, Dan Huang, Qiang Luo, Kaikai Zhu, Ningtao Peng
Summary: This paper proposes a flexible job shop scheduling problem with discrete operation sequence flexibility and designs an improved memetic algorithm to solve it. Experimental results show that the algorithm outperforms other algorithms in terms of performance. The proposed model and algorithm can help production managers obtain optimal scheduling schemes considering operations with or without sequence constraints.
SWARM AND EVOLUTIONARY COMPUTATION
(2024)
Article
Computer Science, Artificial Intelligence
Daniel Molina-Perez, Efren Mezura-Montes, Edgar Alfredo Portilla-Flores, Eduardo Vega-Alvarado, Barbara Calva-Yanez
Summary: This paper presents a new proposal based on two fundamental strategies to improve the performance of the differential evolution algorithm when solving MINLP problems. The proposal considers a set of good fitness-infeasible solutions to explore promising regions and introduces a composite trial vector generation method to enhance combinatorial exploration and convergence capacity.
SWARM AND EVOLUTIONARY COMPUTATION
(2024)
