Article
Computer Science, Artificial Intelligence
Yi Fan, Pengjun Wang, Majdi Mafarja, Mingjing Wang, Xuehua Zhao, Huiling Chen
Summary: The fruit fly optimization algorithm (FOA) is a swarm-based algorithm inspired by fruit flies' food search behaviors in nature. The conventional FOA, while simple and concise, has limitations in exploration and exploitation abilities when used for different optimization problems. By introducing an improved FOA approach called BSSFOA, which utilizes bat sonar strategy for global optimization and a hybrid distribution mechanism for local optimization, better solutions can be found in both continuous and discrete optimization problems.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Mathematics, Interdisciplinary Applications
M. A. El-Shorbagy
Summary: This article presents a chaotic fruit fly algorithm (CFFA) as an optimization approach for solving engineering design problems. CFFA combines the fruit fly algorithm (FFA) with chaotic local search (CLS) to address the difficulties of the basic FFA in optimization problems.
Article
Computer Science, Artificial Intelligence
Jieguang He, Zhiping Peng, Jinbo Qiu, Delong Cui, Qirui Li
Summary: This study proposes a novel elitist fruit fly optimization algorithm (EFOA) to address the issues of poor population diversity and imbalance between global exploration and local exploitation in the original fruit fly optimization algorithm (FOA). EFOA consists of two search phases with elite and random individual guiding, and incorporates the use of elite and boundary information to enhance population diversity. The experimental results show that the elite guiding strategy and the alternating execution of the three search stages in EFOA effectively balance exploration and exploitation, and improve its convergence speed.
Article
Computer Science, Interdisciplinary Applications
Helong Yu, Wenshu Li, Chengcheng Chen, Jie Liang, Wenyong Gui, Mingjing Wang, Huiling Chen
Summary: The Fruit Fly Optimization Algorithm (FOA) is a recently developed algorithm inspired by the foraging behavior of fruit fly populations. In order to improve its global search capability and solution quality, a dynamic step length mechanism, abandonment mechanism, and Gaussian bare-bones mechanism are introduced into FOA, resulting in BareFOA. Experimental results demonstrate that BareFOA outperforms other competitors in benchmark problems and engineering optimization design problems.
ENGINEERING WITH COMPUTERS
(2022)
Article
Computer Science, Information Systems
Qian Cao, Bo Liu, Ying Jin
Summary: The paper proposes a Fruit Fly Optimization Algorithm based on Locality Sensitive Hashing-aware (LSHFOA) to address the weak global optimization ability of the Fruit fly Optimization Algorithm (FOA). The LSHFOA improves individual diversity of the population using locality sensitive hashing mechanism, and jumps out of local optimum by changing the population location. Experimental results show that LSHFOA has faster convergence speed and higher precision for function optimization.
JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Fuqing Zhao, Ruiqing Ding, Ling Wang, Jie Cao, Jianxin Tang
Summary: This study proposes a hierarchical guidance strategy assisted fruit fly optimization algorithm with cooperative learning mechanism (HGCLFOA) that achieves a balance of exploration and exploitation through the hierarchical guidance strategy using different subpopulations.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Mathematics
Bin Yin, Jie Yang, Yue Li
Summary: This article proposes an improved fruit fly optimization algorithm by introducing evolutionary strategies to address the premature convergence and local extreme value problems of FOA. Experimental tests show that the improved algorithm performs well in terms of optimization ability and inversion accuracy.
JOURNAL OF MATHEMATICS
(2023)
Article
Automation & Control Systems
Heng-Wei Guo, Hong-Yan Sang, Xu-Jin Zhang, Peng Duan, Jun-Qing Li, Yu-Yan Han
Summary: In this paper, a discrete fruit fly optimization algorithm (DFFO) is proposed to solve the distributed permutation flowshop scheduling problem (DPFSP) with the goal of minimizing total flowtime. The DFFO algorithm adopts an initialization method that considers population quality and diversity, and it includes three perturbation operators and an improved reference local search method to improve its exploration and exploitation abilities. Experimental results on large-scale instances demonstrate the effectiveness of DFFO as a metaheuristic algorithm.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Ru-Yu Wang, Pei Hu, Chia-Cheng Hu, Jeng-Shyang Pan
Summary: The Fruit Fly Optimization Algorithm is a versatile and computationally efficient swarm intelligence algorithm. This article proposes a new optimized structure, called Quasi-affine Transformation evolutionary, to improve the Fruit Fly Optimization Algorithm's premature convergence on complex multi-peak problems. Experimental results demonstrate its competitiveness compared to other intelligent algorithms and its effectiveness in vehicle route planning for the Capacitated Vehicle Routing Problem.
INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS
(2022)
Article
Computer Science, Information Systems
Xinming Zhang, Qiuying Lin
Summary: This paper proposes an improved SL-PSO algorithm, called TLS-PSO, which enhances the optimization performance of PSO through the use of three learning strategies and a hybrid learning mechanism. Experimental results demonstrate that TLS-PSO outperforms state-of-the-art PSO variants and other algorithms on complex functions and engineering problems, indicating its superior performance and potential for practical problem-solving.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Theory & Methods
R. Roselin Kiruba, T. Sree Sharmila
Summary: The efficient image steganography using FOIS algorithm proposed in this work aims to safeguard medical data and prevent cybercrimes. By adaptively determining the optimal locations of pixels on the cover image, this method improves image quality and secures data effectively.
MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Review
Chemistry, Analytical
Bibi Aamirah Shafaa Emambocus, Muhammed Basheer Jasser, Aida Mustapha, Angela Amphawan
Summary: Swarm intelligence is a discipline that uses multiple agents to solve optimization problems efficiently. The dragonfly algorithm, inspired by dragonflies' swarming behaviors, has been found to outperform other swarm intelligence and evolutionary algorithms. This paper provides a comprehensive survey of the dragonfly algorithm, its applications, performance, and limitations, as well as an analysis of its hybrids.
Article
Engineering, Multidisciplinary
Zuyan Chen, Adam Francis, Shuai Li, Bolin Liao, Dunhui Xiao, Tran Thu Ha, Jianfeng Li, Lei Ding, Xinwei Cao
Summary: This paper introduces a novel meta-heuristic algorithm named Egret Swarm Optimization Algorithm (ESOA) inspired by hunting behavior of two egret species. ESOA consists of three primary components: a sit-and-wait strategy, an aggressive strategy, and discriminant conditions, providing high efficiency and stability.
Article
Computer Science, Information Systems
Broderick Crawford, Ricardo Soto, Hanns de la Fuente Mella, Claudio Elortegui, Wenceslao Palma, Claudio Torres-Rojas, Claudia Vasconcellos-Gaete, Marcelo Becerra, Javier Pena, Sanjay Misra
Summary: The paper discusses the application of the Fruit Fly Algorithm in dealing with binary-based combinatorial problems and how different binarization methods can be used to adapt the algorithm to binary search spaces. Experimental results show that the proposed algorithm is robust enough to handle the Set Coverage Problem effectively.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Computer Science, Artificial Intelligence
Ravneil Nand, Bibhya Nand Sharma, Kaylash Chaudhary
Summary: Intelligent swarm algorithm FA has been widely researched and seen as an efficient optimization algorithm. A new modification introduces a stepping ahead parameter for proactive behavior. Hybrid algorithm combining modified FA with CMAES improves exploitation and maintains exploration.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Ailing Yang, Weide Li, Xuan Yang
KNOWLEDGE-BASED SYSTEMS
(2019)
Article
Computer Science, Artificial Intelligence
Lan Qin, Weide Li, Shijia Li
Article
Biology
Shipeng Nie, Weide Li
Article
Ecology
Shipeng Nie, Weide Li
ECOLOGICAL MODELLING
(2020)
Article
Green & Sustainable Science & Technology
Lan Qin, Weide Li
Article
Meteorology & Atmospheric Sciences
Weide Li, Xi Gao, Zihan Hao, Rong Sun
Summary: This study predicted the 3-h precipitation situation in the semi-arid region of Lanzhou using a deep learning model, achieving better prediction performance by employing the Mutual Information feature extraction method and oversampling technique.
Article
Environmental Sciences
Xi Gao, Weide Li
Summary: The GLSTM model combines parameterized adjacency matrix and LSTM to introduce spatiotemporal information for PM2.5 prediction. The advantage of GLSTM is synchronous operation of all stations and enhanced interpretability.
ATMOSPHERIC POLLUTION RESEARCH
(2021)
Article
Environmental Sciences
Gaofeng Zhu, Xufeng Wang, Jingfeng Xiao, Kun Zhang, Yunquan Wang, Honglin He, Weide Li, Huiling Chen
Summary: Over the past 50 years, there has been a significant increase in global land surface air temperature at night compared to during the day. The impact of this asymmetric warming on vegetation activity has been a topic of debate. This study finds that the effects of daytime and nighttime warming on vegetation activity are generally in the same direction and that properly addressing the issue of multicollinearity is critical for understanding these effects.
SCIENCE OF THE TOTAL ENVIRONMENT
(2022)
Article
Environmental Sciences
X. Liu, W. Li
Summary: A hybrid model combining multi-graph convolution and Long Short-Term Memory network (MGC-LSTM) is proposed to predict PM2.5 concentration. By extracting spatial and temporal features, this model can achieve more accurate predictions of PM2.5 concentration.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Weide Li, Zihan Hao, Zhihe Zhang
Summary: This paper focuses on time series clustering and uses deep learning to discover the shape characteristics of time series. A new neural network model is established to optimize the representation learning and clustering tasks of time series. Experimental results show that this model achieves good clustering performance on multiple datasets.
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
(2022)
Article
Environmental Sciences
Zihan Hao, Weide Li, Jinran Wu, Shaotong Zhang, Shujuan Hu
Summary: In this study, a new deep learning model called Attention-GRU was constructed to predict lake surface water temperature. The model extracted variable correlation information and temporal correlation information to achieve the most accurate prediction results for Qinghai Lake. The analysis revealed that air temperature was the dominant factor influencing the lake surface water temperature.
Article
Mathematical & Computational Biology
Zhiyin Gao, Sen Liu, Weide Li
Summary: This study focuses on the selection and release timing of control species in biological control, and investigates the effects of their initial density and spatial structures on control outcomes. The research utilizes a food web system with native species, invasive species, and introduced control species, considering the spatial landscape of biological invasion, dispersal strategies, and predation preferences. The results suggest that earlier introduction of control species leads to better control effects, especially for globally spreading invasive species. Additionally, the study finds that an optimal predation preference for invasive species is crucial for successful introduction and effective control.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2022)
Proceedings Paper
Computer Science, Theory & Methods
Hsin-Yu Huang, Yong-Yi Fanjiang, Ting Hsuan Lee, Chia An Lee, Tzu Min Zhang, Wei De Li
2020 IEEE CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (CCECE)
(2020)
Article
Computer Science, Artificial Intelligence
Hui Chen, Weide Li, Xuan Yang
EXPERT SYSTEMS WITH APPLICATIONS
(2020)
