Article
Computer Science, Artificial Intelligence
Xinsen Zhou, Wenyong Gui, Ali Asghar Heidari, Zhennao Cai, Guoxi Liang, Huiling Chen
Summary: Continuous ant colony optimization algorithm incorporates a random following strategy to enhance global optimization performance and effectively handle high-dimensional feature selection problems. The algorithm performs competitively with other state-of-the-art algorithms in benchmark tests and outperforms well-known classification methods on high-dimensional datasets.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Information Systems
Mohammed Hamdi
Summary: The paper proposes an optimization-based classification algorithm called AACOSVM for sentiment analysis by mimicking ants' foraging behavior to identify and classify emotions in product reviews. Results indicate that AACOSVM achieves better performance in terms of F-Measure and Classification Accuracy compared to existing classifiers such as EBC and EFAN.
Article
Automation & Control Systems
Zhiwei Cao, Yichao Zhang, Jihong Guan, Shuigeng Zhou, Guanghui Wen
Summary: The proposed chaotic ant colony optimized (CACO) link prediction algorithm shows significantly higher prediction accuracy and robustness in experiments, outperforming most state-of-the-art algorithms.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Xiaobo Zhu, Jiale Gao, Yijie Dai, Jianguo Zhang, Weidong Zhang, Daying Sun, Wenhua Gu
Summary: A novel human gait recognition method is proposed in this study, which effectively combines grid-less planar flexible pressure sensors and multilayer heterogeneous machine learning algorithms to accurately characterize the dynamic gait features of the human body. This method not only has high accuracy and low cost, but also has advantages such as multifunctionality, portability, and real-time monitoring. It provides important technical support for early intervention and rehabilitation treatments.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Computer Science, Artificial Intelligence
Ayah Helal, Fernando E. B. Otero
Summary: This paper presents a new ACO-based algorithm for data stream classification called sAnt-Miner. By using a hybrid pheromone model, sAnt-Miner efficiently handles mixed-type attributes and reduces computational time, while maintaining high predictive accuracy.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Ziqian Wang, Shangce Gao, Yong Zhang, Lijun Guo
Summary: Feature selection is a crucial data-mining technique, and swarm intelligence has been successfully applied in this field. This study proposes a novel ant colony optimization method that significantly improves classification performance by constructing a probabilistic sequence-based graphical representation.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Ya-Hui Jia, Yi Mei, Mengjie Zhang
Summary: This article focuses on the capacitated electric vehicle routing problem and proposes a confidence-based bilevel ant colony optimization algorithm to solve it. The algorithm divides the problem into two subproblems: capacitated VRP and fixed routing vehicle charging problem. Experimental results show that the proposed algorithm has reached the state-of-the-art level and achieved new best-known solutions.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Hamid Hussain Awan, Waseem Shahzad
Summary: This article introduces a novel self-training approach named ST-AC-ACO, which improves classification accuracy by exploiting associations between attribute values and class labels.
PEERJ COMPUTER SCIENCE
(2021)
Article
Computer Science, Artificial Intelligence
Abdelrahman Elsaid, Karl Ricanek, Zimeng Lyu, Alexander Ororbia, Travis Desell
Summary: Continuous Ant-based Topology Search (CANTS) is a novel nature-inspired neural architecture search algorithm based on ant colony optimization. It utilizes a continuous search space to automate the design of artificial neural networks, removing the limitation of predetermined structure sizes. By adding an extra dimension for neural synaptic weights, CANTS can optimize both architecture and weights, significantly reducing optimization time while maintaining competitive performance.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Bo-Lun Chen, Wen-Xin Jiang, Yong-Tao Yu, Lei Zhou, Claudio J. Tessone
Summary: This study introduces the concept of swarm intelligence in social networks to simulate the propagation of negative influence using ant colony, and identifies high-value and low-cost suppression nodes. Experimental results show that the proposed algorithm effectively limits the spread of negative influence.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Wenping Ma, Xiaobo Zhou, Hao Zhu, Longwei Li, Licheng Jiao
Summary: The paper introduces a two-stage hybrid ACO algorithm for high-dimensional feature selection, which is capable of handling large-scale datasets efficiently with shorter running time.
PATTERN RECOGNITION
(2021)
Article
Environmental Sciences
Ravinder Bhavya, Kaveri Sivaraj, Lakshmanan Elango
Summary: The quality of groundwater is crucial for human health and the environment, especially as it is the main source of drinking water in many parts of the world. Traditional water-quality monitoring methods are expensive and time-consuming, but data-driven models using artificial intelligence offer a more efficient way to predict groundwater quality. This study aims to build an optimized neural network using ant colony optimization and artificial neural network techniques for predicting groundwater quality parameters.
Article
Computer Science, Artificial Intelligence
Lin Sun, Yusheng Chen, Weiping Ding, Jiucheng Xu, Yuanyuan Ma
Summary: This article proposes a novel adaptive fuzzy neighborhood-based multilabel feature subset selection approach with ant colony optimization (ACO) for multilabel classification. It addresses the issue of ignoring correlations among labels and the manual setting of neighborhood radius in existing feature selection models. The approach combines feature cosine similarity and label Jaccard similarity to effectively reflect overall similarity between samples, and utilizes dynamic adjustment coefficients to control label similarity importance. Experimental results demonstrate the effectiveness of the proposed algorithm in achieving excellent feature subset for multilabel classification.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Grazia Lo Sciuto, Giacomo Capizzi, Rafi Shikler, Christian Napoli
Summary: Physical defects in organic solar cells can reduce their functionality, emphasizing the need for proper detection and classification of these defects. Zernike moments are utilized to represent texture variations caused by defects, with high resistance to noise. An elliptical basis function neural network is used for classification, achieving an impressive 89.3% correct classification rate.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Article
Computer Science, Interdisciplinary Applications
Zana Azeez Kakarash, Farhad Mardukhia, Parham Moradi
Summary: This paper proposes a method of filtering and multi-label feature selection to address the issue of reduced machine learning performance in high-dimensional data. The method utilizes a graph-based density peaks clustering to group similar features and uses ant colony optimization search process to rank features based on their relevancy and redundancy. Experimental results show the superiority of the proposed method over baseline and state-of-the-art methods.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2023)
Article
Automation & Control Systems
Yue Xu, Dechang Pi, Shengxiang Yang, Yang Chen, Shuo Qin, Enrico Zio
Summary: This study proposes a novel angle-based bi-objective redundancy allocation algorithm to address the uncertainty issue in multi-objective optimization. The algorithm achieves better performance, reduced computational time, and improved result distribution.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Xu Yang, Juan Zou, Shengxiang Yang, Jinhua Zheng, Yuan Liu
Summary: This article proposes a fuzzy decision variables framework for large-scale multiobjective optimization. The framework divides the evolutionary process into fuzzy evolution and precise evolution stages. Fuzzy evolution blurs the decision variables to reduce the search range in the decision space for quick convergence, while precise evolution directly optimizes the actual decision variables to increase population diversity. Experimental results demonstrate that this framework significantly improves the performance and computational efficiency of multiobjective optimization algorithms in large-scale problems.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Theory & Methods
Shouyong Jiang, Juan Zou, Shengxiang Yang, Xin Yao
Summary: Evolutionary dynamic multi-objective optimisation (EDMO) is a rapidly growing area that uses evolutionary approaches to solve multi-objective optimisation problems with time-varying changes. After nearly two decades, significant advancements have been made in theoretic research and applications. This article provides a comprehensive survey and taxonomy of existing research on EDMO, as well as highlighting multiple research opportunities for further development.
ACM COMPUTING SURVEYS
(2023)
Article
Computer Science, Artificial Intelligence
Meirong Chen, Yinan Guo, Yaochu Jin, Shengxiang Yang, Dunwei Gong, Zekuan Yu
Summary: This study proposes an environment-driven hybrid dynamic multi-objective evolutionary optimization method to balance the quality of obtained solutions and the computation cost, and select an appropriate optimization method based on the characteristics of the dynamic environment.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Engineering, Environmental
Gaobo Lou, Qingqing Rao, Qing Li, Zhicheng Bai, Xingwei He, Youhua Xiao, Jinfeng Dai, Shenyuan Fu, Shengxiang Yang
Summary: In this study, a novel ionic complex MPA-DAD was successfully synthesized for use as a flame retardant and toughening additive in epoxy resin (EP). Results showed that adding 8 wt% MPA-DAD enabled EP to pass the UL 94 V-0 rating and greatly reduced the heat release, fulfilling the fire safety requirement. Additionally, the EP/8% MPA-DAD composite exhibited significantly enhanced toughness and preserved strength. This work provides a facile fabrication route for high efficiency, multifunctional flame retardants in advanced EP composite materials manufacturing.
CHEMICAL ENGINEERING JOURNAL
(2023)
Article
Computer Science, Artificial Intelligence
Botao Jiao, Yinan Guo, Shengxiang Yang, Jiayang Pu, Dunwei Gong
Summary: In traditional data stream mining, classification models are trained on labeled samples from a single source, which is difficult and expensive in real-world scenarios with multiple concurrent data streams. To address this issue, multistream classification is proposed, leveraging biased labels from a source stream to train a model for another stream with unlabeled samples. However, previous methods are mostly designed for single-source stream scenarios and ignore the effect of redundant or low-quality features. This article proposes a reduced-space multistream classification based on multiobjective evolutionary optimization, narrowing the distribution difference between source and target streams and improving classification accuracy and G-mean.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Information Systems
Jialiang Zhang, Juan Zou, Shengxiang Yang, Jinhua Zheng
Summary: This paper proposes a multi-modal multi-objective evolutionary algorithm (MMEAs) based on independently evolving sub-problems to solve the problem of poor diversity maintenance in traditional algorithms. A two-stage environmental selection strategy is used to ensure the convergence of the objective space and the distribution of the decision space. The k-nearest neighbor deletion strategy is employed in the decision space to guarantee the distributivity of each equivalent Pareto optimal solution.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Xiaoling Gong, Ziheng Rong, Jian Wang, Kai Zhang, Shengxiang Yang
Summary: In this paper, a hybrid algorithm based on state-adaptive slime mold model and fractional-order ant system (SSMFAS) is proposed to solve the travelling salesman problem (TSP). The SSMFAS algorithm emphasizes critical connections and balances exploration and exploitation ability through two targeted auxiliary strategies in the state-adaptive slime mold (SM) model. The incorporation of fractional-order calculus in the ant system (AS) takes advantage of neighboring information. The modified pheromone update rule of AS dynamically integrates the flux information of SM. Convergence analysis is provided through mathematical proofs to understand the search behavior of the proposed algorithm. Experimental results show the efficiency of the hybridization and demonstrate the competitive ability of the proposed algorithm in finding better solutions for TSP instances compared to state-of-the-art algorithms.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Likai Wang, Qingyang Zhang, Xiangyu He, Shengxiang Yang, Shouyong Jiang, Yongquan Dong
Summary: This study proposes a novel and lightweight bio-inspired computation technique named biological survival optimizer (BSO), which simulates the escape behavior of prey in the natural environment. The algorithm consists of two important courses, escape phase and adjustment phase. The effectiveness of the BSO is validated on the CEC2017 benchmark problems, three classical engineering structural problems, and neural network training models. Results show that BSO has competitive performance compared with other state-of-the-art optimization techniques in terms of both convergence and accuracy.
Article
Plant Sciences
Peng Liu, Yue Tan, Jian Yang, Yan-Duo Wang, Qi Li, Bing-Da Sun, Xiao-Ke Xing, Di-An Sun, Sheng-Xiang Yang, Gang Ding
Summary: Endophytic fungi from desert plants are a unique microbial community that has not been extensively studied chemically. They may serve as a new source for bioactive natural products. In this study, new secondary metabolites were obtained from an endophytic fungus isolated from two desert plant species. The compounds showed potential cytotoxic and phytotoxic activities.
FRONTIERS IN PLANT SCIENCE
(2023)
Article
Automation & Control Systems
Yaru Hu, Jinhua Zheng, Shouyong Jiang, Shengxiang Yang, Juan Zou
Summary: This article proposes an evolutionary algorithm based on layered prediction (LP) and subspace-based diversity maintenance (SDM) for handling dynamic multiobjective optimization (DMO) environments. The algorithm predicts population changes in response to environmental changes and maintains a balance between population diversity and convergence.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Medicine, Research & Experimental
Yi Kuang, Wenjing Shen, Xiaodong Ma, Ziwei Wang, Rui Xu, Qingqing Rao, Shengxiang Yang
Summary: 1200 natural compounds from 19 Chinese herbal medicines were screened through computational methods. The top 20 compounds mainly originated from Ranunculus ternatus and Picrasma quassioides, exhibited low binding free energies below -9.0 kcal/mol. Compounds Japonicone G and Picrasidine T with favorable drug-likeness were obtained. The complex of Japonicone G and Mpro showed prominent stability, suggesting Japonicone G as a highly promising inhibitor against SARS-CoV-2 for further study.
Article
Chemistry, Applied
Yanxin Zhang, Jinlong Dai, Xiaoyun Ma, Chengguo Jia, Junyou Han, Chenggang Song, Yuqing Liu, Dongsheng Wei, Hongfei Xu, Jianchun Qin, Shengxiang Yang
Summary: The reduction in blueberry harvest due to pathogen infection was reported to reach 80%. Essential oil (EO) and its nano-emulsion (MNE) derived from Monarda didyma L were found to inhibit the growth of pathogenic fungi isolated from blueberries, with MNE exhibiting superior antimicrobial activity and causing morphological changes in the fungi, as well as reducing the rot and weight loss rate of blueberries.
Article
Computer Science, Information Systems
Juan Zou, Jian Luo, Yuan Liu, Shengxiang Yang, Jinhua Zheng
Summary: The core element in solving CMOPs is to balance objective optimization and constraint satisfaction. We propose a flexible two-stage evolutionary algorithm based on automatic regulation (ARCMO) to adapt to complex CMOPs.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Jie Chen, Shengxiang Yang, Zhu Wang, Hua Mao
Summary: Sparse representation techniques have shown impressive impact on various fields such as image processing, computer vision, and pattern recognition, due to their capability in effectively learning intrinsic structures from high-dimensional data. In this article, two algorithms based on locality-constrained linear representation learning with probabilistic simplex constraints are proposed to learn sparse representations. Experimental results demonstrate that these algorithms perform better than several state-of-the-art algorithms for learning with high-dimensional data.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
