Article
Computer Science, Information Systems
Xiaoshu Xiang, Ye Tian, Ran Cheng, Xingyi Zhang, Shengxiang Yang, Yaochu Jin
Summary: This study proposes a benchmark generator for online dynamic single-objective and multi-objective optimization problems. It adjusts the influence of solutions found in each environment on the problems in the next environment through different types of functions and predefined parameters, and suggests a test suite consisting of continuous and discrete online dynamic optimization problems. The proposed OL-DOP test suite exhibits time-deception compared to existing benchmark test suites and evaluates the ability of dynamic optimization algorithms to tackle the influence of solutions on successive environment problems.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Yun Hou, Guosheng Hao, Yong Zhang, Feng Gu, Wenyang Xu
Summary: This paper proposes a multi-objective discrete particle swarm optimization algorithm to solve the particle routing problem in distributed particle filters. Experimental results show that the algorithm is highly competitive and can provide multiple high-quality Pareto optimal solutions.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Interdisciplinary Applications
Xiaoli Shu, Yanmin Liu, Jun Liu, Meilan Yang, Qian Zhang
Summary: This paper proposes a multi-objective particle swarm optimization algorithm (D-MOPSO) to solve complex multi-objective optimization problems in the real world. It addresses the lack of convergence and diversity in traditional optimization methods and makes use of existing resources in the search process. D-MOPSO dynamically adjusts the population size based on the resources in the archive, improves particle exploration through local perturbations, and controls population size through non-dominated sorting and population density.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Jinhua Zheng, Zeyu Zhang, Juan Zou, Shengxiang Yang, Junwei Ou, Yaru Hu
Summary: This paper proposes a dynamic multi-objective particle swarm optimization algorithm based on adversarial decomposition and neighborhood evolution (ADNEPSO). The algorithm utilizes the complementary characteristics in the search area of the adversarial vector and introduces a novel particle update strategy to enhance performance and adaptability to environmental changes.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Zhenzhong Wang, Kai Ye, Min Jiang, Junfeng Yao, Neal N. Xiong, Gary G. Yen
Summary: This study proposes a framework to reuse knee points in a new environment to address the Dynamic Vehicle Routing Problem based on Hybrid Charging Strategy. Reusing knee points helps generate a better initial population and brings convenience to decision makers.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Engineering, Electrical & Electronic
S. Jagadeesh, I Muthulakshmi
Summary: Energy-efficient clustering and routing for wireless sensor networks is a challenging optimization problem. The proposed MOPSO-L algorithm effectively organizes clusters and CH selection, leading to extended network lifetime and efficient energy consumption compared to existing techniques.
INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS
(2021)
Article
Computer Science, Interdisciplinary Applications
Yifei Sun, Xin Sun, Zhuo Liu, Yifei Cao, Jie Yang
Summary: This study proposes a novel dynamic community detection algorithm based on particle swarm optimization, targeting the classification of nodes with similar attributes in networks that change over time. By calculating the resistance distance of each node, the core nodes in the network are identified and the constant community is formed by nodes associated with these core nodes. Knowledge gained from the evolution of core nodes in consecutive time steps is utilized to determine the constant community to be retained. Experimental results on various networks indicate the higher accuracy and stability of the proposed algorithm compared to other well-known algorithms.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Computer Science, Information Systems
Ying Yin, Yuhai Zhao, He Li, Xiangjun Dong
Summary: The paper proposes an efficient and effective multi-objective method, DYN-MODPSO, which addresses the issues in dynamic community detection by enhancing the traditional evolutionary clustering framework and particle swarm algorithm. The novel strategy and carefully designed operators contribute to the method's superior performance on both real and synthetic dynamic networks, outperforming competitors in terms of effectiveness and efficiency.
INFORMATION SCIENCES
(2021)
Article
Automation & Control Systems
Guosen Li, Ting Zhou
Summary: This paper proposes a particle swarm optimizer based on reference point, termed RPPSO, which effectively handles global and local solutions in multimodal multi-objective optimization problems, achieving competitive performance on multiple benchmark test functions.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Diana Cristina Valencia-Rodriguez, Carlos A. Coello Coello
Summary: Particle Swarm Optimization (PSO) is a bio-inspired metaheuristic algorithm that utilizes information exchange between particles to explore the search space. This study focuses on the influence of the number of connections among particles in Multi-Objective Particle Swarm Optimizers (MOPSOs) using random regular graphs as the swarm topology. Experimental results indicate that a higher connection degree can lead to algorithm instability in various problems, and MOPSOs with the same connection degree exhibit similar behavior.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Green & Sustainable Science & Technology
Sasan Barak, Reza Moghdani, Hamidreza Maghsoudlou
Summary: This paper proposes a novel scheduling approach for a resource-constrained Flexible Manufacturing System (FMS) by considering energy efficiency of AGVs and using a modified multi-objective particle swarm optimization algorithm to solve the problem, which outperforms the classic version of the algorithm according to the results.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Computer Science, Artificial Intelligence
Jintong Yang, Juan Zou, Shengxiang Yang, Yaru Hu, Jinhua Zheng, Yuan Liu
Summary: This paper proposes a particle swarm optimization algorithm based on a double search strategy for dynamic multi-objective optimization. The algorithm updates the speed of each particle using two search strategies to accelerate convergence and maintain population diversity in a dynamic environment. Additionally, an effective dynamic response mechanism is introduced to accelerate convergence to the Pareto optimal set and maintain good distribution in the new environment. Experimental results demonstrate the advantages of the proposed algorithm in dealing with DMOPs.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Interdisciplinary Applications
M. Salehi Sarbijan, J. Behnamian
Summary: Unlike classical vehicle routing problems, real-time vehicle routing problems are real-world dynamic problems without previous knowledge. This article proposes a real-time feeder vehicle routing problem in which trucks and motorcycles can join during the freight delivery process. The problem is solved using mixed-integer linear programming and a dynamic inertia weight particle swarm optimization algorithm.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Xiangyin Zhang, Shuang Xia, Xiuzhi Li, Tian Zhang
Summary: This paper proposes a multi-objective particle swarm optimization algorithm based on reinforcement learning (MCMOPSO-RL) to solve the collaborative path planning problem for multiple unmanned aerial vehicles (UAVs) in complex environments. The experimental results show that this algorithm can solve the path planning problem for multiple UAVs more efficiently and robustly.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Interdisciplinary Applications
Afsane Amiri, Hossein Zolfagharinia, Saman Hassanzadeh Amin
Summary: This study proposes a robust mathematical model for short-haul delivery problems with electric trucks, considering the uncertainty in energy consumption. The problem of on-time delivery is addressed by minimizing delay and earliness, while simultaneously reducing transportation costs and increasing customer satisfaction. The results demonstrate that the Adaptive Large Neighborhood Search algorithm combined with the weighted-sum method performs the best. Additionally, a simulation study provides insights for decision-makers by analyzing robust solutions under different levels of uncertainty.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Yi-Nan Guo, Xu Zhang, Dun-Wei Gong, Zhen Zhang, Jian-Jian Yang
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2020)
Article
Computer Science, Artificial Intelligence
Xian-Fang Song, Yong Zhang, Yi-Nan Guo, Xiao-Yan Sun, Yong-Li Wang
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2020)
Article
Physics, Multidisciplinary
Zhen Zhang, Jian Cheng, Yinan Guo
Summary: An active disturbance rejection optimal controller based on a proportional-derivative (PD) control law is proposed to improve the dynamic and steady-state control performances of a system, by integrating PID control, ADRC, and PSO. The controller eliminates the negative effects of dead-zone and optimizes parameters using PSO, with improved disturbance estimation performance through an improved linear extended state observer. Ten comparative experiments validate the effectiveness and superiority of the controller.
Article
Physics, Multidisciplinary
Zhen Zhang, Yinan Guo, Xianfang Song
Summary: This paper develops a sliding-mode control method with an improved nonlinear extended state observer (SMC-INESO) for the rotation system of a hydraulic roofbolter with dead-zones, uncertain gain, and disturbances to improve tracking performance. The proposed method models the rotation system, estimates disturbances in real time, and designs a sliding-mode control law and adaptation laws to compensate for estimation errors and uncertain gain. Comparative simulation studies demonstrate the effectiveness of the proposed SMC-INESO.
Article
Computer Science, Artificial Intelligence
Yinan Guo, Botao Jiao, Ying Tan, Pei Zhang, Fengzhen Tang
Summary: This paper proposes a transfer weighted extreme learning machine (TWELM) classifier to address the issue of limited labeled instances and poor generalization. The TWELM classifier extracts knowledge from other domains and combines it with limited labeled target domain data to improve the classification performance. Experimental results show that TWELM outperforms existing algorithms in terms of classification accuracy and computation cost.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
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
Automation & Control Systems
Lianbo Ma, Nan Li, Yinan Guo, Xingwei Wang, Shengxiang Yang, Min Huang, Hao Zhang
Summary: The article proposes an adaptive reference vector reinforcement learning approach for decomposition-based algorithms in industrial copper burdening optimization. The method utilizes reinforcement learning and reference point sampling operations to adapt reference vectors to problem characteristics and handle complex constraints. Experimental results confirm the competitiveness and effectiveness of the proposed algorithm in both benchmark problems and real-world instances.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Kangjia Qiao, Jing Liang, Kunjie Yu, Minghui Wang, Boyang Qu, Caitong Yue, Yinan Guo
Summary: This paper proposes a double-balanced evolutionary multi-task optimization (DBEMTO) algorithm to better solve constrained multi-objective optimization problems (CMOPs). DBEMTO evolves two populations to solve the main task (CMOP) and the auxiliary task (MOP extracted from the CMOP) respectively and uses three evolutionary strategies for offspring generation. DBEMTO has performed more competitively compared to other state-of-the-art CMOEAs according to the final results.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
Article
Automation & Control Systems
Zhen Zhang, Yinan Guo, Dunwei Gong, Jianxing Liu
Summary: In this study, an improved integral sliding-mode control method is proposed to address the difficulties in controlling the drilling of a hydraulic roofbolter. By using a nonlinear extended state observer and an uncertain gain adaptive law, the proposed method achieves better tracking performance and exhibits good dynamic and steady-state performance.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2023)
Article
Automation & Control Systems
Jian-Jiao Ji, Yi-Nan Guo, Xiao-Zhi Gao, Dun-Wei Gong, Ya-Peng Wang
Summary: Task allocation in mobile crowdsensing is a crucial issue, as existing systems do not consider the sudden departure of users, impacting the quality of long-term sensing tasks. To address this, a dynamic task allocation model is proposed, along with a novel indicator for evaluating the sensing ability of users. A Q-learning-based hyperheuristic evolutionary algorithm is suggested, showing superior performance compared to other algorithms.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Xinfang Ji, Yong Zhang, Dunwei Gong, Xiaoyan Sun, Yinan Guo
Summary: This article introduces a multisurrogate-assisted multitasking particle swarm optimization algorithm to find multiple optimal solutions of expensive multimodal optimization problems at a low computational cost. The algorithm transforms the problem into a multitasking optimization problem and employs cluster and skill factor strategies to balance computation and prediction accuracy. Experimental results show that the algorithm can find multiple highly competitive optimal solutions.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Jianjian Yang, Qiankun Huang, Shirong Ge, Xiao Wang, Long Chen, Yinan Guo, Tianmu Gui
Summary: Since the introduction of Industry 4.0, the level of intelligence in various industries has significantly improved. However, the challenges of employee well-being, resource conservation, and environmental preservation remain. Therefore, we propose Industry 5.0 based on the parallel system theory, which includes human-machine collaboration, virtual-real interaction, and local-global balanced resources. Mining 5.0 aims to achieve green and sustainable development goals with a focus on a human-centric perspective.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2023)
Article
Computer Science, Artificial Intelligence
Jie Chen, Shengxiang Yang, Conor Fahy, Zhu Wang, Yinan Guo, Yingke Chen
Summary: In this paper, an online sparse representation clustering (OSRC) method is proposed for data stream clustering. By introducing low-dimensional projection and l(2,1)-norm optimization technique, the proposed method can adaptively handle the noise and redundancy in high-dimensional data and exploit evolving subspace structures. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods for data stream clustering.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Botao Jiao, Yinan Guo, Dunwei Gong, Qiuju Chen
Summary: This study proposes a dynamic ensemble selection method to deal with concept drift in imbalanced data streams. By using a novel technique to generate new instances and selecting the optimal combination based on candidate classifier performance, the proposed method outperforms others in terms of classification accuracy and tracking new concepts.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Lianbo Ma, Shi Cheng, Mingli Shi, Yinan Guo
Summary: This paper presents a multi-objective search-based approach for generating balanced maps in real-time strategy games, which considers convergence, diversity, and strengthens the selection pressure towards Pareto fronts, demonstrating effectiveness in generating game maps.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2022)
