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WPO: A Whale particle optimization algorithm

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2023年第1期16卷 115-115页

作者： Huang, Ko-Wei Wu, Ze-Xue Jiang, Chang-Long Huang, Zih-Hao Lee, Shih-Hsiung Natl Kaohsiung Univ Sci & Technol Dept Elect Engn Kaohsiung Taiwan Natl Kaohsiung Univ Sci & Technol Dept Intelligent Commerce Kaohsiung Taiwan

Metaheuristic algorithms are novel optimization algorithms often inspired by nature. In recent years, scholars have proposed various metaheuristic algorithms, such as the genetic algorithm (GA), artificial bee colony, particle swarm optimization (PSO), crow search algorithm, and whale optimization algorithm (WOA), to solve optimization problems. Among these, PSO is the most commonly used. However, different algorithms have different limitations. For example, PSO is prone to premature convergence and falls into a local optimum, whereas GA coding is difficult and uncertain. Therefore, an algorithm that can increase the computing power and particle diversity can address the limitations of existing algorithms. Therefore, this paper proposes a hybrid algorithm, called whale particle optimization (WPO), that combines the advantages of the WOA and PSO to increase particle diversity and can jump out of the local optimum. The performance of the WPO algorithm was evaluated using four optimization problems: function evaluation, image clustering, permutation flow shop scheduling, and data clustering. The test data were selected from real-life situations. The results demonstrate that the proposed algorithm competes well against existing algorithms.

关键词： Metaheuristic algorithm particle swarm optimization algorithm Whale optimization algorithm Whale particle optimization algorithm optimization problems

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Improving collaborative recommender system using hybrid clustering and optimized singular value decomposition

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartD期126卷

作者： Movafegh, Zahra Rezapour, Abdoreza Mehrastan Univ Dept Comp Engn Guilan Iran Islamic Azad Univ Dept Comp Engn Astaneh Ashrafieh Branch Guilan Iran

In the era of internet and big data, recommender systems are necessary to filter out useless information. Collaborative filtering (CF) is one of the most successful technique used in recommender systems, this technique suffers from a large number of users and items, or the scalability issue. In this paper, a new hybrid method based on K-means clustering (KM) and Singular Value Decomposition (SVD) which uses evolutionary algorithms is proposed to deal with scalability issue. On the one hand, KM optimized by particle swarm optimization (PSO) and denoising by Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to cluster users and reduce the number of comparisons between the target user and other users. On the other hand, SVD optimized by Genetic algorithm (GA) is used to reduce the number of items. The proposed method is assessed on three standard datasets and the results are compared with basic CF and other extended versions that use clustering algorithms, evolutionary algorithms and dimensionality reduction techniques. The results show that our proposed method performed better than other methods in terms of precision, recall and MAE, and the scalability problem was improved by reducing the time complexity. Also, the combined clustering method was optimized in terms of Davies-Bouldin and the Dunn's index compared to the basic clustering methods.

关键词： Recommender system Collaborative filtering Clustering Singular value decomposition particle swarm optimization algorithm Genetic algorithm

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Calculation and optimization of inter-regional available transfer capability considering wind power uncertainty and system improvement measures

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FRONTIERS IN ENERGY RESEARCH 2023年 11卷

作者： Liu, Wenli Zhang, Tao Yang, Xiaolei Wang, Jin China Three Gorges Univ Coll Elect Engn & New Energy Yichang Hubei Peoples R China State Grid Jiaxing Power Supply Co Jiaxing Zhejiang Peoples R China

The intermittency and uncertainty of wind energy make transmission congestion management increasingly complex. In this paper, we investigate the impact of wind energy on available transmission capacity (ATC) and then explore ways to mitigate transmission congestion from the grid and load side, respectively. The impact of a stochastic variable is considered by applying Latin Hypercube Sampling (LHS) and backward curtailment techniques to generate typical scenarios. ATC is improved by the optimal allocation of Thyristor Controlled Series Compensation (TCSC) on the grid side and the use of Demand Response (DR) on the load side. The source-grid-load cooperative optimisation model for ATC is solved by an improved particle swarm optimisation (PSO) algorithm. Based on the IEEE-30 bus system, an experimental scheme is designed and analytical calculations are performed. The results show that the joint application of TCSC and DR can help to improve ATC in a comprehensive way. The work done in this paper can achieve the purpose of promoting renewable energy consumption by improving the utilisation efficiency of the transmission network without changing the network structure.

关键词： wind power's stochastic characteristics available transfer capability demand response thyristor-controlled series compensation particle swarm optimization algorithm

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Design of improved four-coil structure with high uniformity and effective coverage rate

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HELIYON 2023年第4期9卷 e15193页

作者： Zhu, Xuehua Liu, Chuangchuang Su, Hao Miao, Yutong Cheng, Hongde AnHui Polytech Univ AnHui Key Lab Detect Technol & Energy Saving Devic Wuhu 241000 Peoples R China

Helmholtz coils have extensive applications in biological medicine, aerospace, and other in-dustries depending on the simple structure and miraculous magnetic field characteristics. How-ever, the uniform zone generated by them is not appropriate for scientific experiments with large devices. Due to the limitations of Helmholtz coils in application, a novel design technique is proposed to improve the homogeneity and region of magnetic field. The main approach is to add an auxiliary coil on each side of Helmholtz coils to compensate for the magnetic field that exists farther out from the center point. To analyze the size relationship between the auxiliary coil and the main coil to obtain the best magnetic field distribution, the traditional Maclaurin expansion method and particle swarm optimization (PSO) algorithm are used to research and discuss. The magnetic field distribution and the corresponding effective coverage rate (ECR) of the improved schemes with different structural parameters are calculated under the relative deviations of 0.1%, 0.5% and 1%, respectively. The results obtained by the above optimization methods are verified by the finite element software COMSOL and specific experiments. Both optimization methods manifest that the maximum effective coverage rate can be achieved when the size of the auxiliary coil is consistent with that of the main coil. In addition, we compare the improved four-coil structure proposed in this paper with the existing four-coil square structure under the same volume. The data show that the improved structure has certain advantages in the spatial magnetic field distribution. The corresponding tri-axial coil system is established by adopting the param-eters on the single axis, which can achieve a constant magnetic field in arbitrary directions by controlling the magnitude and direction of current on each axis. This provides a theoretical basis for the application of magnetic navigation technology.

关键词： Uniform magnetic field Maclaurin expansion method particle swarm optimization algorithm Effective coverage rate Finite element simulation

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A new deterministic PSO algorithm for real-time systems implemented on low-power devices

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2023年第1期429卷

作者： Dlugosz, Zofia Rajewski, Michal Dlugosz, Rafal Talaska, Tomasz Pedrycz, Witold Bydgoszcz Univ Sci & Technol Fac Telecommun Comp Sci & Elect Engn ul Al Prof S Kaliskiego 7 PL-85796 Bydgoszcz Poland Aptiv Serv Poland Podgorki Tynieckie 2 PL-30399 Krakow Poland Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2V4 Canada Polish Acad Sci Syst Res Inst PL-00901 Warsaw Poland Istinye Univ Fac Engn & Nat Sci Dept Comp Engn Istanbul Turkiye King Abdulaziz Univ Comp Engn Fac Engn Dept Elect Jeddah 21589 Saudi Arabia

The paper presents a novel, deterministic particle swarm optimization (PSO) algorithm, facilitating its implementation in low-power application specific integrated circuits (ASIC) realized in CMOS technology. The PSO algorithms are commonly used in a variety of optimization problems. They allow to search for a global extreme in a selected fitness function, which is used for numerical modeling of specific optimized problems. The conventional version of the algorithm is relatively difficult to implement directly in hardware, especially when each particle is represented by a separate circuit working in parallel with other particles. In such a situation, each particle has to be equipped with its own copy of each function. One of the problems is, for example, an efficient realization of the random function block that provides for each particle some factors used in computing a cognitive and a social velocity components. In this paper we shows a novel way to work around this problem. In the proposed approach the random function does not need to be used. It has been replaced by a simple computationally deterministic mechanism, which at the level of the overall swarm allows for effective emulation of the former random mechanism. We proposed such simplifications that reduce the formulas describing the algorithm to only basic arithmetic and logic operations. Comprehensive investigations have shown that performance of the proposed approach is comparable with its original version or in may situations even better. The proposed algorithm was tested with numerous test functions (50 runs for each tested function), to verify its flexibility and adaptation abilities to different problems.(c) 2023 Elsevier B.V. All rights reserved.

关键词： particle swarm optimization algorithm Parallel computing Deterministic approach Hardware implementation Low power circuits CMOS technology

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How to realize the power demand side actively matching the supply side? --A virtual real-time electricity prices optimization model based on credit mechanism

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APPLIED ENERGY 2023年第1期343卷

作者： Guo, Zhilong Xu, Wei Yan, Yue Sun, Mei Jiangsu Univ Ctr Energy Dev & Environm Protect Zhenjiang 212013 Jiangsu Peoples R China

Intermittence and fluctuation of renewable energy increase the cost of real-time balance of power supply and demand, which will affect the stability of the power grid. How to design the incentive mechanism to increase the flexibility of the distributed energy system and reduce the cost of real-time supply-demand balance is an urgent problem to be solved. In this study, from the perspective of the demand side actively matching the supply side, a virtual real-time electricity price optimization model based on the credit mechanism is proposed. We use the linear decreasing inertia weight particle swarm optimization algorithm to optimize the user's electricity price and realize the minimization of the total electricity cost. The influence of the virtual real-time electricity price incentive mechanism on the matching degree between the power load curve and the distributed energy output curve is also studied. Moreover, a novel index is put forward to quantitatively measure the matching degree between users' power load curve and distributed energy output curve. Taking a multi-microgrid system in a China's industrial park as the case, we have found the results that the virtual real-time electricity price optimization model can not only reduce users' total power cost by 7.8108%, but also improve the matching degree between the demand side and the supply side by 2.4643%. The new demand response incentive mechanism we have presented in this paper may promote the effective utilization of distributed energy and reduce the impact of renewable energy on power grid.

关键词： Distributed energy Virtual real-time electricity price Demand side matches supply side particle swarm optimization algorithm

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Optimal Sizing of Battery and Super-Capacitor Based on the MOPSO Technique via a New FC-HEV Application

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ENERGIES 2023年第9期16卷 3902页

作者： Djouahi, Abdeldjalil Negrou, Belkhir Rouabah, Boubakeur Mahboub, Abdelbasset Samy, Mohamed Mahmoud Univ Kasdi Merbah Ouargla Lab Promot & Valorisat Ressources Sahariennes VPRS BP 511 Ouargla 30000 Algeria Univ Kasdi Merbah Ouargla Elect Engn Dept BP 511 Ouargla 30000 Algeria Beni Suef Univ Fac Engn Elect Engn Dept Bani Suwayf 2722165 Egypt

In light of the energy and environment issues, fuel cell vehicles have many advantages, including high efficiency, low-temperature operation, and zero greenhouse gas emissions, making them an excellent choice for urban environments where air pollution is a significant problem. The dynamics of fuel cells, on the other hand, are relatively slow, owing principally to the dynamics of the air compressor and the dynamics of manifold filling. Because these dynamics can limit the overall performance of fuel cell vehicles, two key technologies that have emerged as critical components of electric vehicle powertrains are batteries and supercapacitors. However, choosing the best hybrid energy storage system that combines a battery and a supercapacitor is a critical task nowadays. An electric vehicle simulated application by MATLAB Code is modeled in this article using the multi-objective particle swarm optimization technique (MOPSO) to determine the appropriate type of batteries and supercapacitors in the SFTP-SC03 drive cycle. This application optimized both component sizing and power management at the same time. Batteries of five distinct types (Lithium, Li-ion, Li-S, Ni-Nicl2, and Ni-MH) and supercapacitors of two different types (Maxwell BCAP0003 and ESHSR-3000CO) were used. Each storage component is distinguished by its weight, capacity, and cost. As a consequence, using a Li-ion battery with the Maxwell BCAP0003 represented the optimal form of hybrid storage in our driving conditions, reducing fuel consumption by approximately 0.43% when compared to the ESHSR-3000CO.

关键词： fuel-cell hybrid electric vehicle particle swarm optimization algorithm hydrogen consumption multi-objective function problem energy management strategy

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Improved Whale optimization algorithm for VSG Control

Improved Whale Optimization Algorithm for VSG Control

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2025 International Conference on Electrical Automation and Artificial Intelligence, ICEAAI 2025

作者： Xia, Mingliang Wang, Xihuai Shanghai Maritime University School of Logistics Engineering Shanghai China

ISBN: (纸本)9798331506797

With the increasing seriousness of energy problems and the extensive use of new energy sources, how to ensure that the new energy power generation system has good grid-connected stability has become a new research hotspot. Virtual synchronous generator (VSG) technology introduces the operating characteristics of synchronous generator (SG) on the basis of grid-connected inverter, so that it has the same rotor inertia and damping. However, the design of VSG system parameters is complicated, and the traditional whale optimization (WOA) algorithm often fails to achieve the best optimization effect. To address this problem, a mathematical model of the VSG system is established, and based on the WOA algorithm, a mixed-strategy improved whale optimization (IWOA) algorithm is proposed. By comparing the simulation results under the environment of grid voltage and frequency fluctuation, it is proved that the IWOA algorithm is more suitable for VSG grid stabilization. © 2025 IEEE.

关键词： grid particle swarm optimization algorithm power oscillation stability virtual synchronous generator

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DIC measurement method based on binocular stereo vision for image 3D displacement detection

JOURNAL OF MEASUREMENTS IN ENGINEERING

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JOURNAL OF MEASUREMENTS IN ENGINEERING 2023年第4期11卷 437-452页

作者： Dong, Xue Zibo Vocat Inst Int Sch Zibo Peoples R China

The deformation detection of large machinery is usually achieved using three-dimensional displacement measurement. Binocular stereo vision measurement technology, as a commonly used digital image correlation method, has received widespread attention in the academic community. Binocular stereo vision achieves the goal of three-dimensional displacement measurement by simulating the working mode of the human eyes, but the measurement is easily affected by light refraction. Based on this, the study introduces particle swarm optimization algorithm for target displacement measurement on Canon imaging dataset, and introduces backpropagation neural network for mutation processing of particles in particle swarm algorithm to generate fusion algorithm. It combines the four coordinate systems of world, pixel, physics, and camera to establish connections. Taking into account environmental factors and lens errors, the camera parameters and deformation coefficients were revised by shooting a black and white checkerboard. Finally, the study first conducted error analysis on binocular stereo vision technology in three dimensions, and the relative error remained stable at 1 % within about 60 seconds. At the same time, three algorithms, including the spotted hyena algorithm, were introduced to conduct performance comparison experiments using particle swarm optimization and backpropagation network algorithms. The experiment shows that the three-dimensional error of the fusion algorithm gradually stabilizes within the range of [-0.5 %, 0.5 %] over time, while the two-dimensional error generally hovers around 0 value. Its performance is significantly superior to other algorithms, so the binocular stereo vision of this fusion algorithm can achieve good measurement results.

关键词： binocular stereo vision digital image correlation method backpropagation neural network particle swarm optimization algorithm displacement measurement

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optimization of hydropneumatic suspension for articulated wheel loader based on kriging model and particle swarm algorithm

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ADVANCES IN MECHANICAL ENGINEERING 2018年第11期10卷

作者： Zhao, Huanyu Wang, Guoqiang Lv, Weidong Cao, Yue Li, Xuefei Jilin Univ Sch Mech Sci & Engn Changchun 130022 Jilin Peoples R China

Articulated wheel loaders that travel on unstructured roads experience severe vibration and poor stability. Introducing suspended axles on wheel loaders, which are traditionally constructed without wheel suspension, is desirable for ride comfort. This study mainly focuses on the parameter optimization of the hydropneumatic suspension to obtain the minimum root mean square of vertical accelerations under different driving conditions, thereby improving the ride comfort of the wheel loader. The multibody model of the wheel loader with hydropneumatic suspension was developed by RecurDyn in co-simulation with MATLAB/Simulink. The vertical acceleration root mean square at the seat position was analyzed when the wheel loader was traveling on class C, D, and E roads with different travel speeds. The surrogate model of the vertical acceleration root mean square with respect to the suspension parameters was established based on kriging method. The established surrogate model was then optimized using particle swarm optimization algorithm. The optimization results of the hydropneumatic suspension parameters of the wheel loader under different road excitations and driving speeds were obtained. Simulation and optimization results show that a well-designed hydropneumatic suspension system can significantly improve wheel loader performance in reducing the vertical acceleration at the seat position compared to a suspension system without optimization.

关键词： Wheel loader vehicle suspension systems optimization of hydropneumatic suspension kriging-based surrogate model particle swarm optimization algorithm

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