Article
Biology
Mingjing Wang, Xiaoping Li, Long Chen
Summary: This paper proposes an enhanced multimodal multiobjective genetic algorithm (ESNSGA-II) to solve multimodal multiobjective optimization issues. The ESNSGA-II algorithm utilizes a special crowding distance calculation and a unique crossover mechanism to ensure a balance between convergence and diversity in both decision space and object space. Experimental results show that ESNSGA-II outperforms other algorithms in terms of efficiently searching multiple Pareto Sets.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Computer Science, Artificial Intelligence
Qinghua Gu, Yifan Peng, Qian Wang, Song Jiang
Summary: This paper proposes a multimodal multi-objective optimization method based on a local optimal neighborhood crowding distance differential evolution algorithm. The method combines an adaptive partitioning strategy, opposition-based learning, and a distance calculation method to improve the convergence and realism of the optimization process. Experimental results demonstrate that the proposed method achieves high levels of performance across multiple objectives.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Ting Zhou, Zhongbo Hu, Qinghua Su, Wentao Xiong
Summary: This paper proposes a novel multimodal multiobjective differential evolution algorithm (MMOcDE) that can locate multiple high quality equivalent Pareto optimal sets and obtain a uniformly distributed Pareto front simultaneously.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Suchitra Agrawal, Aruna Tiwari, Bhaskar Yaduvanshi, Prashant Rajak
Summary: The main aim of feature subset selection is to find the minimum number of required features to perform classification without affecting the accuracy. It is one of the useful real-world applications for different types of classification datasets. However, most of the existing studies do not consider multiple feature subsets of the same size.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Shijie Xiong, Wenyin Gong, Kai Wang
Summary: This paper proposes an enhanced adaptive neighborhood-based speciation differential evolution (EANSDE) algorithm to solve multimodal optimization problems (MMOPs). The algorithm adaptively controls parameters to alleviate the fine-tuning process by users. It introduces an external archive to store inferior solutions and merges them with the current population in the following search. Additionally, a crowding relieving mechanism is proposed to remove extremely similar individuals from the population. Experimental results demonstrate the superiority of EANSDE on the 20 benchmark MMOPs in CEC-2013.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Suchitra Agrawal, Aruna Tiwari, Bhaskar Yaduvanshi, Prashant Rajak
Summary: The main aim of multimodal multiobjective optimization algorithms is to find multiple optimal solutions for multiple objectives while maintaining diversity and convergence balance. This is achieved through the use of niching technique, balancing strategy, and archive strategy.
APPLIED SOFT COMPUTING
(2023)
Article
Automation & Control Systems
Junzhong Ji, Tongxuan Wu, Cuicui Yang
Summary: The article proposes a multimodal multiobjective differential evolution algorithm with species conservation to locate different Pareto-optimal solution sets (PSs) in known areas and explore new areas for diverse solutions. Comparative experiments have shown that the proposed algorithm performs competitively on multimodal multiobjective optimization problems (MMOPs).
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Ting Zhou, Zhongbo Hu, Quan Zhou, Shixiong Yuan
Summary: In this paper, a novel grey prediction evolution algorithm MMGPE is proposed for solving multimodal multiobjective optimization problems. The algorithm incorporates improvements in initialization, parameter setting, accelerating convergence, and environmental selection, showing effectiveness and superiority in achieving multiple Pareto optimal sets while obtaining a well-distributed Pareto front.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Kai Wang, Wenyin Gong, Libao Deng, Ling Wang
Summary: This paper proposes a dynamically hybrid niching-based differential evolution algorithm (DHNDE) for solving multimodal optimization problems (MMOPs). The DHNDE algorithm achieves a good tradeoff between diversity and convergence by dynamically using two niching techniques, introducing a secondary archive, and improving the neighborhood speciation-based DE. Experimental results demonstrate that DHNDE provides highly competitive results compared to other methods, especially for MMOPs with a large number of global optima.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Hong Zhao, Zhi-Hui Zhan, Jing Liu
Summary: This paper proposes an outlier aware differential evolution (OADE) algorithm to address the challenges of locating multiple global optima and enhancing the accuracy of solutions in multimodal optimization problems (MMOPs). The algorithm includes three novel mechanisms: a dimension and guidance-balanced mutation (DGM) strategy, an outlier based selection (OBS) strategy, and an inactive outlier-based re-initialization (IOR) strategy. Experimental results on 20 widely used multimodal benchmarks show that the proposed OADE algorithm generally outperforms some well-performing and state-of-the-art algorithms in multimodal optimization.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Junna Zhang, Degang Chen, Qiang Yang, Yiqiao Wang, Dong Liu, Sang-Woon Jeon, Jun Zhang
Summary: This paper proposes a novel differential evolution framework called proximity ranking-based multimodal differential evolution (PRMDE) for multimodal optimization. Through the cooperative cooperation among three main mechanisms, PRMDE is capable of locating multiple global optima simultaneously. Experimental results show that PRMDE is effective and achieves competitive or even better optimization performance than several representative methods.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Automation & Control Systems
Xiao-Fang Liu, Jun Zhang, Jun Wang
Summary: This article presents a cooperative differential evolution algorithm with an attention-based prediction strategy for dynamic multiobjective optimization. Multiple populations are used to optimize multiple objectives and find subparts of the Pareto front. The algorithm achieves a balanced approximation of the Pareto front and adapts to changes in the environment by using a new attention-based prediction strategy. Experimental results demonstrate the superiority of the proposed method to state-of-the-art algorithms.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yong Wang, Zhen Liu, Gai-Ge Wang
Summary: Recently, multimodal multi-objective problem (MMOP) has attracted significant attention in the field of multi-objective optimization problems. The proposed algorithm addresses the issue of finding all equivalent Pareto sets in MMOP by introducing a modified maximum extension distance (MMED) indicator and implementing two-stage and novel mutation strategies. Additionally, a MMED-based environmental selection strategy improves the overall performance of the population.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Information Systems
Zhiqiang Zeng, Xiangyu Zhang, Zhiyong Hong
Summary: A novel constraint handling technique (CHT) that fuses two rankings is proposed in this paper, addressing the tradeoff between objective functions and constraints in constrained multiobjective optimization algorithms. Based on this CHT, a constrained multiobject differential evolution algorithm is proposed which combines four mutation operations to generate high-quality offspring. Experimental results demonstrate that the proposed algorithm outperforms eight state-of-the-art algorithms in five test suites.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Kai Zhang, Chaonan Shen, Gary G. Yen, Zhiwei Xu, Juanjuan He
Summary: This article introduces a two-stage double niched evolution strategy, DN-MMOES, to effectively and efficiently search for global Pareto optimal solutions. The proposed algorithm employs niching strategy in the decision space in the first stage and double niching strategy in both decision and objective spaces in the second stage, as well as an effective decision density self-adaptive strategy to improve imbalanced decision space density. The experimental results demonstrate that DN-MMOES outperforms eight state-of-the-art MMOEAs in searching for complete Pareto subsets and Pareto front on IDMP and CEC 2019 MMOPs test suite.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2021)
Review
Computer Science, Information Systems
Heba Abdel-Nabi, Mostafa Ali, Arafat Awajan, Mohammad Daoud, Rami Alazrai, Ponnuthurai N. Suganthan, Talal Ali
Summary: Medical Imaging has become an essential tool in the diagnosis and treatment of cancer. The digitization of histopathological slides to generate Whole Slide Images (WSI) has gained attention as a secondary decision support tool for tumor analysis. Deep learning techniques show promise in automatically analyzing WSI features, such as tumor segmentation and classification. This survey focuses on deep learning-based CAD systems for tumor analysis in histopathological images, providing a visual taxonomy of approaches and addressing challenges and limitations.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Automation & Control Systems
Ruobin Gao, Ruilin Li, Minghui Hu, Ponnuthurai Nagaratnam Suganthan, Kum Fai Yuen
Summary: This paper proposes a novel hybrid neural network model that uses deep learning and ensemble learning to predict wave heights. The model extracts meaningful features from historical observations to overcome the challenges posed by the fluctuation and dynamic characteristics of wave data.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Engineering, Electrical & Electronic
Ramen Pal, Somnath Mukhopadhyay, Debasish Chakraborty, Ponnuthurai Nagaratnam Suganthan
Summary: Technological advancement in smart cities can negatively impact the environment, so timely monitoring is crucial for environmental sustainability. This can be achieved through change detection using multi-temporal satellite data. The success of these methods depends on the quality of image segmentation and land-use/land-cover classification techniques. Using cutting-edge classification algorithms is limited by the availability of suitable datasets and identification of different land-use/land-cover classes. In this research, we proposed a hybrid approach that combines a multi-class support vector machine (SVM) and ISODATA-embedded large-scale change detection method.
IETE JOURNAL OF RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Zhou Wu, Mingyuan Yu, Jing Liang
Summary: This paper emphasizes the significance of antenna systems in 5G and 6G communication networks and addresses the issue of energy loss in these systems. To tackle this problem, a novel period-based memetic algorithm framework is proposed to optimize the energy efficiency of antenna models by accurately estimating their parameters. Experimental results demonstrate the effectiveness of this approach in designing energy-efficient antenna models.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Jing Liang, Zhaolin Chen, Yaonan Wang, Xuanxuan Ban, Kangjia Qiao, Kunjie Yu
Summary: This paper proposes a dual-population based algorithm to solve constrained multi-objective optimization problems (CMOPs). The main population considers both objectives and constraints, while the auxiliary population focuses on optimizing objectives only. The algorithm also employs a dynamic population size reducing mechanism and an independent external archive to enhance performance.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yaxin Li, Jing Liang, Kunjie Yu, Caitong Yue, Yingjie Zhang
Summary: Fitness landscape analysis is important for optimization problems, and this study proposes a method called "keenness" to characterize the sharpness of fitness landscapes. It uses a mirror random walk algorithm to construct the relevance between search points and computes feature scalars using cumulative calculations. Experimental results show that keenness has advantages in accuracy, reliability, and sample coverage, making it useful for predicting algorithm performance in continuous optimization problems.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Cybernetics
H. M. Dipu Kabir, Moloud Abdar, Abbas Khosravi, Darius Nahavandi, Subrota Kumar Mondal, Sadia Khanam, Shady Mohamed, Dipti Srinivasan, Saeid Nahavandi, Ponnuthurai Nagaratnam Suganthan
IEEE SYSTEMS MAN AND CYBERNETICS MAGAZINE
(2023)
Article
Computer Science, Artificial Intelligence
A. K. Malik, Ruobin Gao, M. A. Ganaie, M. Tanveer, Ponnuthurai Nagaratnam Suganthan
Summary: Neural networks have been successfully applied in various domains. However, the back propagation based iterative methods have limitations, such as issues of local minima, sensitivity to learning rate, and slow convergence. To overcome these issues, randomization based neural networks, such as RVFL, have been proposed. This article provides a comprehensive review of the evolution of RVFL model, discussing its variations, improvements, and applications. It also explores hyperparameter optimization techniques to improve the generalization performance of the RVFL model, and suggests potential future research directions.
APPLIED SOFT COMPUTING
(2023)
Article
Automation & Control Systems
Yuan-Zhen Li, Kaizhou Gao, Lei-Lei Meng, Ponnuthurai Nagaratnam Suganthan
Summary: This work addresses the distributed permutation flowshop scheduling problem (DPFSP) with peak power consumption. An improved artificial bee colony (IABC) algorithm is proposed to solve the problem, utilizing new solution generation operators and a local search operation. Experimental results show that the IABC algorithm performs well in solving the DPFSP with peak power consumption.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Automation & Control Systems
Jikai Si, Yanqi Wei, Rui Nie, Jing Liang, Chun Gan, Yihua Hu
Summary: This article proposes a 2-D analytical model for slotless axial flux permanent magnet motor. The model includes submodels for armature winding and permanent magnet magnetic fields. The model reveals the operating mechanism of the armature magnetic field and quantitatively analyzes its magnetic fields. The model is verified by a finite element model and a prototype motor.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Engineering, Electrical & Electronic
Zhongwen Li, Zhiping Cheng, Jing Liang, Jikai Si
Summary: With the increase of inverter-interfaced renewable energy in future new power systems, the traditional units' system rotary inertia and frequency regulation capacity are decreasing and becoming insufficient. It is necessary to explore various types of frequency regulation resources, especially for inverter-interfaced units with fast response speed. In this paper, a novel distributed cooperative automatic generation control (AGC) method is proposed to improve the frequency regulation performance of heterogeneous frequency regulation resources.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Ruobin Gao, Ruilin Li, Minghui Hu, P. N. Suganthan, Kum Fai Yuen
Summary: This paper introduces a three-stage online deep learning model based on the ensemble deep random vector functional link (edRVFL) for time series analysis. The edRVFL includes multiple randomized layers to improve the representation ability and utilizes the hidden layer's representation for training an output layer. However, the original edRVFL is not suitable for online learning and the randomized features hinder the extraction of meaningful temporal features. To address these limitations, this paper proposes a dynamic edRVFL with online decomposition, online training, and online dynamic ensemble components. The proposed model is evaluated and compared with state-of-the-art methods on sixteen time series datasets.
Article
Automation & Control Systems
Jinzhu Peng, Haijing Wang, Shuai Ding, Jing Liang, Yaonan Wang
Summary: In this article, a robust high-order control barrier functions (HoCBFs)-based optimal control method is proposed for nonlinear systems with state constraints to achieve safety-stability perspectives. The HoCBFs are presented for constrained nonlinear systems to address state constraints with high relative degrees. The robustness property of the HoCBFs is analyzed based on the asymptotic stability of the forward invariant set. A robust HoCBFs-based optimal control scheme is proposed for the constrained nonlinear system to achieve the safety-stability perspectives of constraints satisfaction and system stabilization.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Proceedings Paper
Engineering, Aerospace
Andriy Sarabakha, Ponnuthurai Nagaratnam Suganthan
Summary: This research introduces a package for ROS1 and ROS2, which allows straightforward interfacing with off-the-shelf drones from the Parrot ANAFI family for autonomous flight in research or teaching. The package is hardware agnostic and can connect seamlessly to all four supported drone models. It has been extensively tested and documented for easy use by other research groups.
2023 INTERNATIONAL CONFERENCE ON UNMANNED AIRCRAFT SYSTEMS, ICUAS
(2023)
Article
Computer Science, Artificial Intelligence
Bin Wu, Xun Su, Jing Liang, Zhongchuan Sun, Lihong Zhong, Yangdong Ye
Summary: In this paper, we propose a new solution for sequential recommendation called GMRec. We improve the accuracy and effectiveness of recommendation by using a graph gating-mixer recommender module. Extensive experiments show that GMRec outperforms recent state-of-the-art methods on multiple datasets.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
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)