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Design of SCADA water resource management control center by a bi-objective redundancy allocation problem and particle swarm optimization

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2015年 133卷 11-21页

作者： Dolatshahi-Zand, Ali Khalili-Damghani, Kaveh Islamic Azad Univ South Tehran Branch Dept Ind Engn Tehran Iran

SCADA(1) is an essential system to control critical facilities in big cities. SCADA is utilized in several sectors such as water resource management, power plants, electricity distribution centers, traffic control centers, and gas deputy. The failure of SCADA results in crisis. Hence, the design of SCADA system in order to serve a high reliability considering limited budget and other constraints is essential. In this paper, a bi-objective redundancy allocation problem (RAP) is proposed to design Tehran's SCADA water resource management control center. Reliability maximization and cost minimization are concurrently considered. Since the proposed RAP is a non-linear multi-objective mathematical programming so the exact methods cannot efficiently handle it. A multi-objective particle swarm optimization (MOPSO) algorithm is designed to solve it. Several features such as dynamic parameter tuning, efficient constraint handling and Pareto gridding are inserted in proposed MOPSO. The results of proposed MOPSO are compared with an efficient epsilon-constraint method. Several non-dominated designs of SCADA system are generated using both methods. Comparison metrics based on accuracy and diversity of Pareto front are calculated for both methods. The proposed MOPSO algorithm reports better performance. Finally, in order to choose the practical design, the TOPSIS algorithm is used to prune the Pareto front. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： SCADA multi-objective redundancy allocation problem Meta-heuristics multi-objective particle swarm optimization epsilon-Constraint method Adaptive grid

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multiple objective particle swarm optimization technique for economic load dispatch

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2005年第5期6卷 420-427页

作者：赵波曹一家 School of Electrical Engineering Zhejiang University Hangzhou 310027 China

A multi-objective particle swarm optimization (MOPSO) approach for multi-objective economic load dispatch problem in power system is presented in this paper. The economic load dispatch problem is a non-linear constrained multi-objective optimization problem. The proposed MOPSO approach handles the problem as a multi-objective problem with competing and non-commensurable fuel cost, emission and system loss objectives and has a diversity-preserving mechanism using an external memory (call “repository”) and a geographically-based approach to find widely different Pareto-optimal solutions. In addition, fuzzy set theory is employed to extract the best compromise solution. Several optimization runs of the proposed MOPSO approach were carried out on the standard IEEE 30-bus test system. The results revealed the capabilities of the proposed MOPSO approach to generate well-distributed Pareto-optimal non-dominated solutions of multi-objective economic load dispatch. Com- parison with multi-objective Evolutionary Algorithm (MOEA) showed the superiority of the proposed MOPSO approach and confirmed its potential for solving multi-objective economic load dispatch.

关键词： Economic load dispatch multi-objective optimization multi-objective particle swarm optimization

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Thermo-economic and environmental optimization of solar assisted heat pump by using multi-objective particle swam algorithm

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ENERGY 2014年 72卷 680-690页

作者： Khorasaninejad, Ehsan Hajabdollahi, Hassan Islamic Azad Univ Dept Mech Engn Behbahan Branch Behbahan Iran Vali E Asr Univ Rafsanjan Dept Mech Engn Rafsanjan Iran

In this paper, a solar assisted heat pump is modelled and optimized. Solar panel surface areas, evaporator pressure, condenser pressure, capacity of heat storage tank as well as the value of superheating/subcooling in evaporator/condenser are selected as design parameters. MOPSO (multi-objective particle swarm optimization) algorithm is used to find the optimum value of design parameters where TAC (total annual cost) and COP (coefficient of performance) taken as two objective functions. TAC is included with sum of investment, operation and environmental costs. The optimization is separately performed for five working fluids including R123, R134a, R245fa, R407C and R22. The optimization results showed that the best studied working fluid is R245fa in both thermo-economical and environmental view point with 1746.1 $/year as TAC, 3.76 for COP and annual environmental cost of 81.825. The optimum results of R245fa as working fluid, showed 15.22%, 21.28%, 22.31% and 44.66% improvement in TAC compared with R134a, R123, R22 and R407C, respectively. Furthermore, COP improvement for R245fa was obtained 26.77%, 30.92%, 34.31% and 48.12% compared with R134a, R123, R22 and R407C, respectively. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Solar assisted heat pump Refrigerant Total annual cost multi-objective particle swarm optimization

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Wave Environment Decomposition with Adaptive Tri-objective particle swarm optimization for Mobile Robot Path Planning

Wave Environment Decomposition with Adaptive Tri-Objective P...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Tran Thi Cam Giang Nguyen Tien Dung Huynh Thi Thanh Binh Do Quoc Huy Mac Thi Thoa Thuyloi Univ Ho Chi Minh City Vietnam Hanoi Univ Sci & Technol Sch Informat & Commun Technol Hanoi Vietnam

ISBN: (纸本)9781665487689

Mobile robots are being used more and more in various fields of agriculture, industry, military as well as disaster relief. However, the robot's limited battery energy power has resulted in many failed missions. To utilize this limited energy of the robot, this paper focuses on solving the problem of multi-objective path planning for a robot that satisfies a freecollision constraint and tri-optimization objectives: path length, path safety, and smoothness of the path. An original environment decomposition method is proposed, which is inspired by wave propagation in physics. The working environment is divided into arcs with the same center. Then, an Adaptive Tri-objective particle swarm optimization algorithm (denoted, ATOPSO) that combines the advantages between the multi-objective particle swarm optimization and the Differential Evolution, is also introduced to solve the multi-objective problem above. The performance of the proposed decomposition method and the path planning algorithm ATOPSO is compared to existing methods and experimented with various scenarios and data. Obtained results show that our proposed method is more effective, it can solve the multi-objective problem in many complex environments including obstacles of different shapes.

关键词： Robot path planning environment modeling multi-objective particle swarm optimization differential evolution

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A particle swarm Approach Embedded with Numerical Analysis for multi-response optimization in Electrical Discharge Machining 1

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5th International Conference on swarm, Evolutionary, and Memetic Computing (SEMCCO)

作者： Mohanty, Chinmaya P. Singh, Manas Ranjan Mahapatra, Siba Sankar Chatterjee, Suman Natl Inst Technol Dept Mech Engn Rourkela 769008 Rourkela India

ISBN: (数字)9783319202945

ISBN: (纸本)9783319202945;9783319202938

Present work proposes a thermo-numerical model for accurate prediction of material removal and tool erosion for electrical discharge machining (EDM) process. The data collected for the numerical analysis is based on Box-Behnken's experimental design, a popular response surface methodology (RSM) approach. The numerical model is validated by comparing experimental results on a die sinking EDM machine. A sequentially coupled thermo-structural model has also been proposed to estimate the residual stress distribution on the work piece. Analysis of variance is conducted to identify significant parameters. Regression analysis is conducted on the model to develop valid mathematical models relating responses with process parameters. Finally, a multi objective particle swam (MOPSO) algorithm has been adopted for simultaneous optimization of responses. The proposed model can be employed for selecting ideal process states to improve process productivity and finishing capabilities.

关键词： Electrical discharge machine Finite element method multi-objective particle swarm optimization

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An adaptive assisted method based on MOPSO for THz MMA effective designing

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JOURNAL OF PHYSICS D-APPLIED PHYSICS 2025年第3期58卷

作者： Qu, Weiwei Wang, Yurong Li, Guilin Xie, Zhaohui Li, Zongren Deng, Hu Shang, Liping Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621010 Peoples R China Southwest Univ Sci & Technol Tianfu Inst Res & Innovat Chengdu 610299 Peoples R China

To address the high time cost and low efficiency of traditional terahertz (THz) metamaterial absorber (MMA) design, an adaptive method based on multi-objective particle swarm optimization (PSO) was proposed. First, we presented a symmetric absorber of a four-split-ring resonator with a cross-shaped top layer. The structural parameters were optimized by the PSO and multi-objective PSO (MOPSO) algorithms. The simulation results indicated that the MOPSO rapidly obtained geometric parameters that balance the high absorptivity and high quality factors. This method quickly identified a symmetric structure with absorptivity greater than 99% and quality factor of 377.45 at 1.584 THz. Additionally, the asymmetric structure achieved near-perfect absorption at 1.585 THz with a Q value of 281.32. By studying the electric field distribution and the surface current distribution, the physical mechanism of the absorber designed has been explained. This adaptive assisted method based on MOPSO can efficiently design MMAs and offer an artificial intelligence strategy for THz functional device design.

关键词： metamaterial absorber terahertz multi-objective particle swarm optimization parameter optimization

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.A multi-objective search-based approach for position and orientation deviations in assemblies with multiple non-ideal surfaces

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ADVANCES IN ENGINEERING SOFTWARE 2025年 199卷

作者： Zhang, Jian Xia, Huanxiong Liu, Jianhua Shen, Hongda Luo, Juncheng Zhang, Xuerui Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China Beijing Inst Technol Tangshan Res Inst Tangshan 063015 Peoples R China Beijing Inst Technol Tangshan Res Inst Hebei Key Lab Intelligent assembly & Detect Techno Tangshan 063000 Peoples R China

Computing position and orientation deviations (PODs) considering non-ideal morphologies and local deformations of mating surfaces is a key challenge in controlling the performances of assembled mechanical products through tolerance analysis. However, existing computational models based on Skin Model Shapes face difficulty in handling the PODs for all types of assembly issues. This paper proposes a novel multi-objective search-based approach for POD computation on a conception of force equilibrium constraints. A multiobjective particle swarm optimization algorithm is employed to solve the multi-objective model, and the efficiency and accuracy of the proposed approach are tested and compared with the NSGA-II and FEM methods. To demonstrate its distinctive capability to deal with assembly issues with multiple non-ideal mating surfaces, the proposed approach is applied to assemblies with multiple planar and cylindrical surfaces. Finally, an application of predicting the coaxial accuracy of a fuel pump is carried out, and the capabilities of the proposed approach are further demonstrated.

关键词： Tolerance analysis Position and orientation Skin Model Shapes(SMSs) Local deformation multi-objective particle swarm optimization (MOPSO)

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Battery state-of-health estimation based on random charge curve fitting and broad learning system with attention mechanism

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JOURNAL OF POWER SOURCES 2025年 636卷

作者： Dai, Houde Huang, Yiyang Zhu, Liqi Lin, Haijun Yu, Hui Lai, Yuan Yang, Yuxiang Fuzhou Univ Sch Elect Engn & Automat Fuzhou 350116 Peoples R China Chinese Acad Sci Quanzhou Inst Equipment Mfg Fujian Inst Res Struct Matter Jinjiang 362215 Fujian Peoples R China Hunan Normal Univ Dept Elect Changsha 410081 Peoples R China Fujian Special Equipment Inspect & Res Inst Fujian Key Lab Special Intelligent Equipment Safet Fuzhou 350008 Peoples R China

Due to the usage habits, it is challenging to conduct the complete process of lithium-ion batteries (LIBs) from fully discharged to the maximum charge in real situations. To achieve battery accuracy state-of-health (SOH) estimation in random charging situations, this study proposes a novel health feature extraction strategy based on random charging curve fitting and an enhanced broad learning system (BLS). First, a multi-objective particle swarm optimization (MOPSO) algorithm is utilized to determine the optimal voltage interval for data extraction. Second, the random charging curve segments are fitted by a quadratic function to characterize health features (HFs). Finally, this study proposes a battery SOH estimation model, i.e., the attention mechanism-based BLS (ABLS). The attention mechanism reduces the uncertainty caused by the random weights of the BLS for the inputs. A dropout layer is incorporated into the BLS model to mitigate the risk of overfitting. Experiments are conducted on the NASA, Oxford, and Michigan datasets, with most estimation errors below 1 %. Experimental results demonstrate that the proposed method has the potential for implementation in practical situations involving LIBs. Furthermore, the estimation efficacy of the battery SOH is both reliable and accurate.

关键词： Lithium-ion batteries Broad learning system State of health multi-objective particle swarm optimization

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Optimal configuration for power grid battery energy storage systems based on payload fluctuation guided multi-objective PSO

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JOURNAL OF ENERGY STORAGE 2025年 105卷

作者： Cai, Jun Wang, Kangli Cheok, Adrian David Yan, Ying Nanjing Univ Informat Sci & Technol Sch Automat C MEIC CICAEET Nanjing 210044 Peoples R China Anhui Jianzhu Univ Sch Mech & Elect Engn Hefei 230009 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 211106 Peoples R China

This article proposes a payload fluctuation guided multi-objective particle swarm optimization algorithm (PFGMOPSO) based optimal configuration strategy for power grid battery energy storage systems (BESS). This method comprehensively considers the stability and economy of distribution network operation, and establishes an optimization configuration model for BESS location and capacity by selecting node voltage fluctuations, BESS investment costs, and system active power losses as the objective functions. In response to the disadvantage of poor population diversity in traditional MOPSO algorithms, two main improvements have been made: 1) An optimal particle selection strategy has been proposed to increase population diversity;2) the fluctuation of payload is selected as the parameter to guide particle motion, making particles move in the optimal direction and effectively utilizing system parameters to improve the convergence speed of the algorithm. Based on the IEEE33 node distribution network system, four configuration scenarios are analyzed with system simulation. With the proposed scheme, the optimal configuration area, capacity, and power of the energy storage system were selected, effectively improving the system node voltage fluctuations and line losses. Finally, a simulation comparison was conducted between the traditional MOPSO and the proposed PFG-MOPSO algorithm, which verifies the superiority of the proposed method.

关键词： Optimal configuration Battery energy storage system (BESS) multi-objective particle swarm optimization algorithm (MOPSO) Distribute power network

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A task-driven scheme for forming clustering-structure-based heterogeneous FANETs

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VEHICULAR COMMUNICATIONS 2025年 52卷

作者： Chen, Siji Jiang, Bo Xu, Hong Pang, Tao Gao, Mingke Liu, Ziyang Chongqing Univ Posts & Telecommun Sch Comp Sci & Technol Chongqing 400065 Peoples R China East China Res Inst Comp Technol Shanghai 201800 Peoples R China Hangzhou Dianzi Univ Sch Comp Sci Hangzhou 310018 Peoples R China

Unmanned aerial vehicles (UAVs) are an emerging technology with the potential to be used in industries and various sectors of human life to provide a wide range of applications and services, significantly enhancing its applicability in different fields. When a UAV swarm performs complex tasks, flying Ad-hoc networks (FANETs) based on cluster structures have become a key research topic in the field of topology control due to their strong scalability and low routing overhead. However, current research mainly concentrates on the selection of the cluster head (CH), considering all UAVs within the CH's communication radius as cluster members (CMs), often neglecting whether the cluster can effectively accomplish the task, thereby potentially leading to mission failure. To overcome this problem, this paper innovatively proposes a task-driven clustering (TDC-MOPSO) algorithm based on improved multi-objective particle swarm optimization (MOPSO) for clustering-structure- based heterogeneous FANETs, which introduces the transfer function to improve the search range of particles and the mutation mechanism to avoid falling into local optima, and a more reasonable fitness function is designed to select CHs. The simulation results indicate that the proposed TDC-MOPSO algorithm dramatically improves the task completion rate by up to about 41.32% and extends the node lifetime by up to about 50.12% compared to traditional clustering algorithms. Meanwhile, the TDC-MOPSO algorithm improves the task completion rate by up to about 11.02% compared to other mopso-based algorithms. Furthermore, the TDC-MOPSO algorithm obtains more clustering solutions with higher average energy, less waste of resources, less CH handover rate, and less routing overhead in simulation. The proposed algorithm is also verified in a real-life scenario, which also effectively supports the completion of the task. All of which demonstrates that the TDC-MOPSO algorithm enhances the efficiency of task execution while

关键词： Unmanned aerial vehicles (UAVs) Clustering algorithm multi-objective particle swarm optimization (MOPSO) Flying ad-hoc networks (FANETs)

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