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Adaptive distance-based multi-objective particle swarm optimization algorithm with simple position update

swarm AND EVOLUTIONARY COMPUTATION 2025年 94卷

作者： Wang, Liangying Hong, Lihuan Fu, Haoxuan Cai, Zhiling Zhong, Yiwen Wang, Lijin Fujian Agr & Forestry Univ Coll Comp & Informat Fuzhou 350002 Fujian Peoples R China Fujian Prov Univ Key Lab Smart Agr & Forestry Fuzhou 350002 Fujian Peoples R China Wuyi Univ Fujian Key Lab Big Data Applicat & Intellectualiza Wuyishan 354300 Fujian Peoples R China

In recent years, multi-objective particle swarm optimization algorithms have been widely used in science and engineering due to their advantages of fast convergence speed and easy implementation. However, the selection of globally optimal particle is an important and challenging problem in the design of multi-objective particle swarm optimization algorithms. In this regard, this paper proposes an adaptive distance-based multi-objective particle swarm optimization algorithm with simple position update, named ADMOPSO. First, an adaptive penalty-based boundary intersection (PBI) distance strategy is designed to select the globally optimal particle from two elite particles which are randomly chosen from an elite particle set. This strategy better balances the diversity and convergence requirements of particle swarm optimization algorithm in the optimization process. Second, a simple position probabilistic update strategy is constructed to rewrite the velocity update method with the weight and use the learning rate to control the scale of the updated velocity in the position update equation to avoid particle swarm falling into the local optimum. Finally, an extensive experimental study is conducted to test the performance of several selected multi-objective optimization algorithms on ZDT, WFG and DTLZ benchmark problems, as well as 7 real-world problems were conducted to test the proposed algorithm. Comparative experimental results show that the algorithm proposed in this paper has significant advantages over other algorithms. This shows that the ADMOPSO algorithm is competitive in dealing with multi-objective problems.

关键词： Competitive mechanism particle swarm optimization algorithm Multi-objective optimization Adaptive distance Position update

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Enhancing Continuum Robotics Accuracy Using a particle swarm optimization algorithm and Closed-Loop Wire Transmission Model for Minimally Invasive Thyroid Surgery

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APPLIED SCIENCES-BASEL 2025年第4期15卷

作者： Guo, Na Zhang, Haoyun Li, Xingshuai Cui, Xinnan Liu, Yang Pan, Jiachen Song, Yajuan Zhang, Qinjian Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China Tsinghua Univ Inst Precis Med Beijing 100084 Peoples R China Univ Southern Calif Phys Dept Los Angeles CA 90007 USA Beijing Informat Sci & Technol Univ Sch Mech & Elect Engn Beijing 100192 Peoples R China Univ Sci & Technol Beijing Sch Adv Engn Beijing 100083 Peoples R China

To address the challenges of confined workspaces and high-precision requirements in thyroid surgery, this paper proposes a modular cable-driven robotic system with a hybrid rigid-continuum structure. By integrating rigid mechanisms and continuum joints within a closed-loop cable-driven framework, the system achieves a balance between flexibility in narrow spaces and operational stiffness. To tackle kinematic model inaccuracies caused by manufacturing errors, an innovative joint decoupling strategy combined with the particle swarm optimization (PSO) algorithm is developed to dynamically identify and correct 19 critical parameters. Experimental results demonstrate a 37.74% average improvement in repetitive positioning accuracy and a 52% reduction in maximum absolute error. However, residual positioning errors (up to 4.53 mm) at motion boundaries highlight the need for integrating nonlinear friction compensation. The feasibility of a safety-zone-based force feedback master-slave control strategy is validated through Gazebo simulations, and a ring-grasping experiment on a surgical training platform confirms its clinical applicability.

关键词： continuum robots particle swarm optimization algorithm closed-loop wire transmission model minimally invasive thyroid surgery

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Reliability Analysis of Three-Dimensional Slopes Considering the Soil Spatial Variability Based on particle swarm optimization algorithm

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APPLIED SCIENCES-BASEL 2025年第5期15卷

作者： Chen, Xin Xu, Jiale Wan, Yukuai Yang, Rong Sun, Jiewen Wu, Di Suzhou Univ Sci & Technol Sch Civil Engn Suzhou 215011 Peoples R China Ningxia Univ Sch Civil & Hydraul Engn Yinchuan 750021 Peoples R China Ningxia Inst Remote Sensing Survey Yinchuan 750021 Peoples R China

This paper presents a new algorithm for assessing the reliability of three-dimensional (3D) slope stability considering the spatial variability of soil based on the particle swarm optimization (PSO) algorithm. First, a 3D random field is generated using the Karhunen-Lo & egrave;ve (K-L) expansion method. Then, the simplified Bishop method of limit equilibrium is coupled with the PSO algorithm to calculate safety factors of the slope. Finally, the failure probability of the slope is determined using the Monte Carlo Simulation method. After validating the rationality of the proposed method through a typical case study, this paper offers an in-depth examination of how soil spatial variability affects the stability of 3D slopes. It is observed that, given identical soil correlation lengths, slope geometric parameters, and failure surface widths, the failure probability is positively correlated with soil spatial variability parameters, while the mean safety factor demonstrates an inverse relationship with these variability parameters. Additionally, the failure probability tends to increase as the soil correlation lengths increase, and it also escalates with the expansion of the failure surface width. In contrast, the mean safety factor exhibits an upward trend with the augmentation of the horizontal correlation length, while it diminishes progressively as the vertical correlation length grows, and it also shows a decline with the widening of the failure surface width. The proposed algorithm significantly improves computational efficiency while ensuring accuracy, making it suitable for the reliability analysis of three-dimensional slopes.

关键词： three-dimensional slope soil spatial variability particle swarm optimization algorithm limit equilibrium method safety factor failure probability

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Cloud computing task scheduling based on particle swarm optimization algorithm

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Procedia Computer Science 2025年 261卷 1349-1355页

作者： Yan Han Department of Science and Research Chengdu Polytechnic Chengdu 610041 China

The task scheduling of cloud computing is the key to cloud services. It is an intelligent scheduling strategy that reasonably allocates tasks in the cloud computing platform to meet users’ resource requirements. A good cloud task scheduling strategy should not only meet the needs of users, but also improve the utilization of cloud computing resources and reduce energy consumption as much as possible. However, the traditional cloud task scheduling algorithm is mainly implemented through experience and manual intervention, and its efficiency and result quality are not ideal. In this paper, a cloud computing task scheduling method based on particle swarm optimization algorithm is proposed, and the influence of this algorithm on task completion time and energy consumption in cloud computing environment is analyzed through experiments.

关键词： particle swarm optimization algorithm Cloud computing Task scheduling Cloud technology

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Research on a hybrid model for flood probability prediction based on time convolutional network and particle swarm optimization algorithm

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SCIENTIFIC REPORTS 2025年第1期15卷 1-13页

作者： Yu, Qiying Liu, Chengshuai Li, Runxi Lu, Zhenlin Bai, Yungang Li, Wenzhong Tian, Lu Shi, Chen Xu, Yingying Cao, Biao Zhang, Jianghui Hu, Caihong Zhengzhou Univ Sch Water Conservancy & Transportat Zhengzhou 450001 Peoples R China Xinjiang Inst Water Resources & Hydropower Res Urumqi 830049 Xinjiang Peoples R China

Accurate flood forecasting in advance is crucial for planning and implementing watershed flood prevention measures. This study developed the PSO-TCN-Bootstrap flood forecasting model for the Tailan River Basin in Xinjiang by integrating the particle swarm optimisation (PSO) algorithm, temporal convolutional network (TCN), and Bootstrap probability sampling method. Evaluated on 50 historical flood events from 1960 to 2014 using observed rainfall-runoff data, the model showed, under the same lead time conditions, a higher Nash efficiency coefficient, along with lower root mean square and relative peak errors in flood forecasting. These results highlight the PSO-TCN-Bootstrap model's superior applicability and robustness for the Tailan River Basin. However, when the lead time exceeds 5 h, the model's relative peak error remains above 20%. Future work will focus on integrating flood generation mechanisms and enhancing machine learning models' generalisability in flood forecasting. These findings provide a scientific foundation for flood management strategies in the Tailan River Basin.

关键词： Flood forecasting Machine learning Temporal convolutional neural network particle swarm optimization algorithm Bootstrap probability sampling algorithm PSO-TCN model Tailan River Basin

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particle swarm optimization algorithm based on parameter improvements

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2017年第3期17卷 557-568页

作者： Wu, Qinghua Song, Tao Liu, Hanmin Yan, Xuesong Wuhan Inst Technol Fac Comp Sci & Engn Wuhan 430205 Hubei Peoples R China China Univ Geosci Ctr Network & Educ Technol Wuhan 430074 Hubei Peoples R China Wuhan Inst Ship Bldg Technol Wuhan 430050 Hubei Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Hubei Peoples R China

Through the researches on various item improvements of particle swarm optimization algorithm, the improved particle swarm optimization algorithm is proposed for the insufficient global searching ability. This paper has improved the parameters of velocity formula and introduced new distance calculation formula to modify various parameters in the velocity formula. The improved particle swarm optimization algorithm has been tested by adopting standard test function, and the result has been compared with other particle swarm optimization algorithms. The experimental result indicated that the proposed improved particle swarm optimization algorithm in this paper got the better result for function optimization problems.

关键词： particle swarm optimization algorithm parameter improvement particle learning factor inertia weight

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particle swarm optimization algorithm for Neuro-Fuzzy Prospectivity Analysis Using Continuously Weighted Spatial Exploration Data

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NATURAL RESOURCES RESEARCH 2019年第2期28卷 309-325页

作者： Roshanravan, Bijan Aghajani, Hamid Yousefi, Mahyar Kreuzer, Oliver Shahrood Univ Technol Fac Min Petr & Geophys Shahrood *** Iran Malayer Univ Fac Engn Malayer Iran Corp Geosci Grp POB 5128 Rockingham Beach WA 6969 Australia James Cook Univ Sch Earth & Environm Sci EGRU Townsville Qld 4811 Australia

Classification of spatial exploration data for exploration targeting using neuro-fuzzy models means that the many spatial values have to be simplified and assigned to a few classes. The simplification of complex geological information, which illustrates a high degree of variability, results in overly simplistic models based on the presumption of homogeneous earth. However, such an assumption is not valid. In this paper, we illustrate the superiority of using continuously weighted spatial evidence values compared to discretely weighted evidence data, and how continuously weighted spatial evidence values can increase the efficiency of neuro-fuzzy exploration targeting models. The results of this study demonstrate that neuro-fuzzy targeting model generated with continuously weighted spatial evidence values is superior to that of the neuro-fuzzy model generated with discretely weighted exploration evidence data.

关键词： Continuous weighting Exploration targeting Neuro-fuzzy particle swarm optimization algorithm

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particle swarm optimization algorithm with Variety Inertia Weights to Solve Unequal Area Facility Layout Problem 32

Particle Swarm Optimization Algorithm with Variety Inertia W...

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32nd Chinese Control And Decision Conference (CCDC)

作者： Zhou, Jia-Li Wang, Jie-Sheng Zhang, Yong-Xin Guo, Qing-Sheng Li, Hui Lu, Yi-Xuan Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114044 Peoples R China

ISBN: (纸本)9781728158556

The unequal area facility layout problem (UA-FLP) is to place some objects in a specified space according to certain requirements, which is a NP-hard problem in mathematics because of the complexity of its solution, the combination explosion and the complexity of engineering system. particle swarm optimization (PSO) algorithm is a kind of swarm intelligence algorithm by simulating the predatory behavior of birds. Aiming at the minimization of material handling cost and the maximization of workshop area utilization, the optimization mathematical model of UA-FLPP is established, and it is solved by the particle swarm optimization (PSO) algorithm which simulates the design of birds' predation behavior. The improved PSO algorithm is constructed by using nonlinear inertia weight, dynamic inertia weight and other methods to solve static unequal area facility layout problem. The effectiveness of the proposed method is verified by simulation experiments.

关键词： particle swarm optimization algorithm Inertia Weight Unequal Area Facility Layout Problem

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particle swarm optimization algorithm can Promote Cooperation in Public Goods Game with Costly Punishment 39

Particle Swarm Optimization Algorithm can Promote Cooperatio...

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39th Youth Academic Annual Conference of Chinese-Association-of-Automation (YAC)

作者： Zhao, Xiaowei Liu, Guangrui Xu, Xiujuan Ilan, Xu Dalian Inst Technol Sch Software Dalian Peoples R China Liaoning Normal Univ Sch Management Dalian Peoples R China

ISBN: (纸本)9798350390780;9798350379228

Previous researches have well demonstrated the importance of costly punishment for promoting the evolution of cooperation in spatial public goods games, while it remains unconsidered sides about the role of strategy-updating mechanisms. Inspired by the algorithms in the field of Machine Learning, in this paper, we propose a game strategy updating mechanism based on particle swarm optimization algorithm for spatial public goods game with continuous strategies, and explore the impact of tolerance-based punishment mechanisms on the evolution of cooperation. The results of simulation experiments show that the particle swarm optimization algorithm can effectively promote cooperation under appropriate parameter settings. This result reveals that hybrid learning is more conducive to maintaining cooperation than a single learning mechanism (social learning or self-learning), and can prevent the spread of betrayal and maintain a high level of cooperation when betrayal strategies invade.

关键词： public goods game particle swarm optimization algorithm spatial reciprocity evolution of cooperation

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particle swarm optimization algorithm for Power Scheduling Problem Using Smart Battery

Particle Swarm Optimization Algorithm for Power Scheduling P...

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IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT)

作者： Makhadmeh, Sharif Naser Khader, Ahamad Tajudin Al-Betar, Mohammed Azmi Naim, Syibrah Alyasseri, Zaid Abdi Alkareem Abasi, Ammar Kamal Univ Sains Malaysia Sch Comp Sci George Town Malaysia Al Balqa Appl Univ Dept Informat Technol Irbid Jordan Univ Kufa ECE Dept Fac Engn Najaf Iraq

ISBN: (纸本)9781538679425

Power Scheduling Problem (PSP) is a problem of schedule the smart home appliances at appropriate time period according to an electricity pricing scheme. The smart home appliances can be scheduled by shifting their time operations from period to another. The significant objective of the scheduling process is to reduce the electricity bill and Peak-to-average ratio (PAR) and improve the user comfort level. In this paper, particle swarm optimization (PSO) algorithm is adapted in order to handle the PSP and to obtain an optimal smart home appliances schedule. Smart battery (SB) is formulated and used in this work to enhance the schedule of the appliances by storing the power at low peak periods and use the stored power by the smart home appliances at peak periods. The simulation results proved the efficiency of using the proposed SB in terms of reducing electricity bill and improving the user comfort level. In addition, PSO is compared with genetic algorithm (GA) in order to evaluate its performance. PSO outperforms GA in terms of achieving the PSP objectives.

关键词： optimization particle swarm optimization algorithm Power Scheduling Problem Smart Battery

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