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A dynamic multi-objective evolutionary algorithm based on genetic engineering and improved particle swarm prediction strategy

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INFORMATION SCIENCES 2024年 660卷

作者： Yang, Yue Ma, Yongjie Zhao, Yan Zhang, Wenping Wang, Yu Northwest Normal Univ Coll Phys & Elect Engn Lan Zhou Peoples R China

Evolutionary and particle swarm optimisation (PSO) algorithms are widely used to solve various optimisation problems. However, both methods have limitations when applied to dynamic multi -objective optimisation problems. Therefore, we propose a dynamic multi -objective evolutionary algorithm based on genetic engineering and improved particle swarm prediction. Genetic engineering encompasses gene sequencing and editing, whereas improved particle swarm prediction involves enhancing information utilisation and dynamic performance. The improved PSO can be better combined with dynamic multi -objective optimization problems (DMOPs) for predicting changing populations, which we call improved particle swarm prediction. Gene sequencing clarifies the functions of different genes during the evolution process, and gene editing enables the algorithm to effectively avoid local optima. Expanding on this foundation, we redefined the notions of particle and global bests in the PSO strategy to improve the information utilisation. Additionally, we innovatively introduced the concept of acceleration, which greatly enhances the dynamic performance of the PSO. Finally, we conducted two experiments to verify the accuracy of our gene sequencing strategy and the performance of our optimisation algorithm. The results indicate that gene sequencing can effectively classify genes, and the developed optimisation algorithm significantly outperforms similar, state-of-the-art, and classical algorithms in terms of distribution and dynamics on 25 test problems.

关键词： Dynamic multi-objective optimization Genetic engineering improved particle swarm algorithm Prediction strategy

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Effective Form Error Assessment Using improved particle swarm Optimization

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MAPAN-JOURNAL OF METROLOGY SOCIETY OF INDIA 2017年第4期32卷 279-292页

作者： Pathak, V. K. Singh, A. K. Malaviya Natl Inst Technol Dept Mech Engn JLN Marg Jaipur 302017 Rajasthan India

This paper presents an improved variant of particle swarm optimization (MPSO) algorithm for the form error evaluation, from a set of coordinate measurement data points. In classical particle swarm optimization (PSO), new solution is updated by the existing one without really comparing which one is better. This behaviour is considered to be caused by lack in exploitation ability in the search space. The proposed algorithm generates new swarm position and fitness solution employing an improved and modified search equation. In this step, the swarm searches in proximity of the best solution of previous iteration to improve the exploitation behaviour. The particle swarm employs greedy selection procedure to choose the best candidate solution. A non-linear minimum zone objective function is formulated mathematically for each form error and consequently optimized using proposed MPSO algorithm. Five benchmark functions are used to prove the efficiency of the proposed MPSO algorithm, by comparing the proposed algorithm with established PSO and genetic algorithm. Finally, the results of the proposed MPSO algorithm are compared with previous literature and with other nature inspired algorithms on the same problem. The results validate that proposed MPSO algorithm is more efficient and accurate as compared to other conventional methods and is well suited for effective form error evaluation using CMMs.

关键词： Form error assessment improved particle swarm algorithm Minimum zone evaluation

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Edge Computing Offload Optimization Strategy Based on improved particle swarm 36

Edge Computing Offload Optimization Strategy Based on Improv...

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36th Chinese Control and Decision Conference (CCDC)

作者： Wang, Yi Li, Xiaohui Mao, Shanli Cai, Bin He, Jie Zhu, Jialu Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan Peoples R China Wuhan Netizen Technol Co Ltd Res Ctr Wuhan Peoples R China Shenglong Elect Grp Co Ltd Res Ctr Wuhan Peoples R China

ISBN: (纸本)9798350387780;9798350387797

In multi-access edge computing, it is crucial to use computing offloading to ensure low processing latency for the task of the user device. This implies that efficient computing offloading is an effective way to reduce the average task latency problem. Therefore, this paper proposes an offloading strategy for multi-user multi-edge server scenarios by using an improved particle swarm. First, it takes transmission latency, processing latency, and the number of users into account when designing an objective function to minimize the average task latency. Then, the improved particle swarm algorithm with dynamic weights and learning factors is used to optimize the task offloading strategy. The simulation results show that the proposed algorithm can find a better offloading strategy compared with the classical particle swarm algorithm and the genetic algorithm.

关键词： multi-access edge computing computational offloading minimizing latency improved particle swarm algorithm

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Optimization Scheduling of Power System Based on improved particle swarm Optimization

Optimization Scheduling of Power System Based on Improved Pa...

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International Conference on Power System Technology (POWERCON)

作者： Lu, Mengke Du, Wei Tian, Ruiping Li, Deyi Xi An Jiao Tong Univ Sch Elect Engn Xian 710049 Shaanxi Peoples R China

ISBN: (纸本)9781538664612

To address the issue of short-term scheduling of power system that contains clean energy, in this paper, an adaptive weighted immune particle swarm optimization algorithm is proposed to overcome the shortcomings of the traditional particle swarm optimization algorithm including easily falling into the local optimum value and slow convergence speed. The superiority of the improved algorithm is verified on the 10-unit test system. On the basis of the existing scheduling model, we propose the spinning reserve model to improve the ability of power system to cope with risks. In view of the complex inequalities and equality constraints in the scheduling model, this paper proposes an extra penalty point hybrid constraint plan based on elite retention. By introducing the elite retention mechanism, the quality of reservation and information transferring in the iterative process is improved, the quality of the particle swarm optimization algorithm is optimized and the optimization speed of the original hybrid constraint plan is effectively accelerated. Finally, the rationality and feasibility of the improved particle swarm algorithm and scheduling model are verified on the improved IEEE-RTS test system, which provides tactics and feasible suggestions for the economic dispatching operation of power systems with clean energy.

关键词： clean energy improved particle swarm algorithm spinning reserve model elite retention

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Trust Evaluation of Computing Power Network Based on improved particle swarm Neural Network 17

Trust Evaluation of Computing Power Network Based on Improve...

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17th IEEE International Conference on Mobility, Sensing and Networking (MSN)

作者： Yu, Chaodong Xia, Geming Wang, Zhaohang Natl Univ Def Technol Sch Comp Sci & Engn Changsha 410003 Peoples R China

ISBN: (纸本)9781665406680

In order to satisfy the trust evaluation of efficient cooperative scheduling of computing power in Computing Power Network, we propose a trust evaluation management system with adaptive detection and an efficient lightweight trust evaluation algorithm. In our work, the multi-attribute trust evaluation data combined with the active detection and global trust database are used as samples to train the BP neural network. And the structure and weight coefficient of the neural network are optimized by the improved particle swarm optimization algorithm, so as to reduce the size of neural network and improve its performance. The trust evaluation model in this study effectively improves the detection ratio of malicious state nodes and reduces the detection time.

关键词： Edge Computing Computing Power Network Trust Evaluation Neural Network improved particle swarm algorithm

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Optimization of Indoor Luminaire Layout for General Lighting Scheme Using improved particle swarm Optimization

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ENERGIES 2022年第4期15卷 1482-1482页

作者： Qu, Ji-Qing Xu, Qi-Lin Sun, Ke-Xue Nanjing Univ Posts & Telecommun Coll Elect & Opt Engn Nanjing 210000 Peoples R China Nanjing Univ Posts & Telecommun Nat Local Joint Project Engn Lab RF Integrat & Mi Nanjing 210000 Peoples R China

An improved mathematical model and an improved particle swarm optimization (IPSO) are proposed for the complex design parameters and conflicting design goals of the indoor luminaire layout (ILL) problem. The ILL problem is formulated as a nonlinear constrained mixed-variable optimization problem that has four decision variables. For a general lighting scheme (GLS), the number and location of luminaires can be uniquely determined by optimizing four decision variables, which avoid using program loops to determine the number of luminaires. We improve the particle swarm optimization (PSO) in three aspects: (1) up-down probabilistic rounding (UDPR) method proposed to solve mixed integer, (2) improving the velocity of the best global particle, and (3) using nonlinear inertia weights with random items. The IPSO has better optimization results in an office study compared with the PSO and genetic algorithm (GA). The results are validated by DIALux simulation software, and a maximum deviation of 2.2% is found. The validated results show that the method using four decision variables increased the speed by 10.6% and the success rate by 23.33%. Furthermore, Indoor Luminaire Layout System APP is designed to provide guidelines visually for lighting designers and related researchers.

关键词： luminaire layout improved particle swarm algorithm particle swarm optimization genetic algorithm optimization general lighting scheme APP

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Multi-Station Fixture Location Layout Optimization Design for Sheet Metal Parts

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JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE 2015年第9期12卷 2903-2908页

作者： Xing, Yanfeng Chen, Weifeng Li, Xuexing Lu, Jingjing Zhang, Heng Shanghai Univ Engn Sci Automobile Engn Coll Shanghai 201620 Peoples R China

Assembly dimensional quality is an important factor to wind noise, driving steady and external appearance, which can be improved by optimizing fixture layout. However, synchronous multi-station optimization algorithm is lacked to design fixture schemes. In this paper, the multi-station comprehensive quality evaluation model is firstly established to evaluate the fixture locating strategies by using the analytic hierarchy process (AHP). Then, an improved particle swarm algorithm is applied to optimize multi-station fixture layout for sheet metal assemblies. Finally, a body floor assembly case is used to illustrate the fixture design method proposed in this paper, and the results show the method is effective.

关键词： Sheet Metal Part Quality Comprehensive Evaluation Model (QCEM) improved particle swarm algorithm Fixture Layout Optimization

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Hydrogen-fueled microgrid energy management: Novel EMS approach for efficiency and reliability

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2024年 80卷 1466-1476页

作者： Yu, Na Duan, Weiyang Fan, Xintao Hebei Vocat Univ Technol & Engn Dept Elect Engn Tianjin 054000 Hebei Peoples R China

Effective energy management within microgrids is crucial, especially given system uncertainties. This study presents a novel Energy Management System (EMS) designed for microgrids with diverse energy sources, notably hydrogen and fuel cells. The EMS integrates artificial intelligence algorithms to predict and adapt to rapid changes, enhancing energy resource efficiency and minimizing wastage. Responsive in nature, the EMS enhances efficiency, reliability, and cost reduction in microgrid energy, dynamically responding to varying conditions for improved stability and environmental protection. Employing a random pattern approach, the EMS focuses on operational efficiency and reliability in supplying electrical and thermal loads. Addressing uncertainties in the electricity market and renewable production, a stochastic framework with scenario generation enables operation in grid-connected and island modes. Enhanced optimization through an improved particle swarm algorithm ensures cost-effectiveness and system reliability in dynamic environmental conditions. This research pioneers innovative microgrid energy management, emphasizing hydrogen and fuel cell technologies to elevate efficiency and performance reliability.

关键词： Microgrid Renewable production Load management improved particle swarm algorithm

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Hybrid Path Planning Model for Multiple Robots Considering Obstacle Avoidance

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IEEE ACCESS 2022年 10卷 71914-71935页

作者： Zhang, Tianrui Xu, Jianan Wu, Baoku Shenyang Univ Sch Mech Engn Shenyang 110044 Peoples R China

To solve the problem that the single robot task execution capability is not enough to meet the whole handling task demand under complex conditions, the hybrid path planning models such as multi-robot path planning and formation cooperative control considering obstacle avoidance are studied. Firstly, for the robot global path finding problem, on the basis of the construction for a robot working environment model based on the geometric map model building method, an improved particle swarm algorithm-based global path planning model is proposed to solve the problems of low robot path planning solution efficiency and easy to fall into local optimal solutions. Secondly, for the multi-robot cooperative formation control and obstacle avoidance and inter-robot collision avoidance problems, a multi-robot formation local path planning model based on the improved artificial potential field method is constructed, a simulated annealing algorithm is introduced to optimize the traditional artificial potential field method, and a multi-robot formation control strategy, robot obstacle avoidance, and inter-robot collision avoidance methods are designed in combination with the pilot-following method to improve the robot formation path exploration The proposed method can improve the path exploration capability and handling efficiency of robot formation. Finally, the global path planning model of the robot based on the improved particle swarm algorithm is simulated and analyzed using Matlab 7.0 to verify the outstanding performance of the model in pathfinding capability. Then the local path planning model of multi-robot formation based on the improved artificial potential field is simulated and analyzed to verify the improved algorithm has good path planning as well as obstacle avoidance performance. The hybrid path planning model is applied to a real case and simulated, and the results show that the improved algorithm improves the exploration capability of the robot formation, effective

关键词： Robots Collision avoidance Path planning Robot kinematics Task analysis Service robots Robot sensing systems Path planning improved particle swarm algorithm environment modeling artificial potential field algorithm formation control

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Segemented Driving Cycle Based Optimization of Control Parameters for Power-Split Hybrid Electric Vehicle With Ultracapacitors

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IEEE ACCESS 2019年 7卷 90666-90677页

作者： Zeng, Xiaohua Cui, Chen Wang, Yue Li, Guanghan Song, Dafeng Jilin Univ State Key Lab Automot Simulat & Control Changchun 130025 Jilin Peoples R China

As energy storage device of hybrid electric vehicles (HEVs), ultracapacitors feature the advantages of higher power density and longer life cycle compared with batteries. However, when ultracapacitors of the same power level are used instead of batteries, fuel consumption becomes more sensitive to changes in control parameters as ultracapacitors store much less energy, and the state of charge (SOC) is power-sensitive. In this paper, optimization of control parameters for a power-split HEV with ultracapacitors is addressed to achieve better fuel economy. First, a power-split HEV model and a corresponding control strategy are established under the MATLAB script environment for convenient analysis and application of an optimization algorithm. Second, focusing on the power-sensitive characteristic of the SOC, three key control parameters are determined, and their effects on fuel consumption are analyzed. Third, an improved particle swarm optimization (IPSO) algorithm is proposed to overcome the disadvantage of the PSO trapping into "local optimum" and improve optimization efficiency. To fully exploit the fuel-saving capability of the HEV, driving cycle segmentation is also considered. The IPSO is used to optimize three key control parameters under the segmented adapted world transient vehicle cycle. Finally, the optimal results are applied to hardware-in-the-loop test to verify the effectiveness of the proposed optimization method. Compared with the fuel consumption before optimization, the fuel saving rate reaches 9.20% in the urban section, 6.40% in the roadway section, and 5.40% in freeway section.

关键词： Control strategy optimization ultracapacitors power-split hybrid system improved particle swarm algorithm

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