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Performance evaluation of proposed Differential Evolution and particle swarm optimization algorithms for scheduling m-machine flow shops with lot streaming

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JOURNAL OF INTELLIGENT MANUFACTURING 2013年第1期24卷 175-191页

作者： Chakaravarthy, G. Vijay Marimuthu, S. Sait, A. Naveen Fathima Michael Coll Engn & Technol Dept Mech Engn Madurai 625020 Tamil Nadu India Latha Mathavan Engn Coll Dept Mech Engn Madurai 625301 Tamil Nadu India Chendhuran Coll Engn & Technol Dept Mech Engn Pudukkottai 622507 India

We consider n-job, m-machine lot streaming problem in a flow shop with equal size sub lots where the objective is to minimize the makespan and total flow time. Lot streaming (Lot sizing) is a technique that splits a production lot consisting of identical items into sub lots to improve the performance of a multi stage production system by over lapping the sub lots on successive machines. There is a scope for efficient algorithms for scheduling problems in m-machine flow shop with lot streaming. In recent years, much attention is given to heuristics and search techniques. To solve this problem, we propose a Differential Evolution algorithm (DEA) and particle swarm optimization (PSO) to evolve best sequence for makespan/total flow time criterion for m-machine flow shop involved with lot streaming and set up time. In this research, we propose the DEA and PSO algorithms for discrete lot streaming with equal sub lots. The proposed methods are tested and the performances were evaluated. The computational results show that the proposed algorithms are very competitive for the lot streaming flow shop scheduling problem.

关键词： Flow shop Lot streaming Scheduling Differential evolution algorithm particle swarm optimization algorithm

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Plasma Breakdown optimization Calculation Based on Improved particle swarm algorithm for TT-1 Device

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JOURNAL OF FUSION ENERGY 2024年第2期43卷 24-24页

作者： Shu, Shuangbao Zhang, Jiaxin Zhang, Shurui Luo, Jiarong Liu, Shanlin Hefei Univ Technol Sch Instrument Sci & Optoelect Engn Hefei 230009 Peoples R China Chinese Acad Sci Inst Plasma Phys Hefei 230031 Peoples R China

In the Tokamak discharge experiment, obtaining the largest possible null field region is a necessary condition for the smooth breakdown of the plasma, and adjusting the poloidal field coil current is key to achieving a better null field region. This paper, based on the Sino-Thai Tokamak cooperation project Thailand Tokamak-1 (TT-1) device, employs an exponentially decreasing particle swarm optimization (PSO) algorithm to optimize the poloidal field coil current to create the desired null field region in the vacuum chamber area. First, a calculation model for the mutual inductance coefficient and the null field region is established according to the characteristics and magnetic structure of the TT-1 device, enabling the calculation of the null field region. Then, an optimization model for the poloidal field coil current is established, aiming to create a sufficiently large null field region (less than 10 Gauss) to facilitate breakdown. The optimization is carried out using both a typical linearly decreasing PSO algorithm and an improved PSO algorithm to determine the optimal poloidal field coil current. Compared to the unmodified PSO algorithm, the improved PSO algorithm reduces the root mean square error by 31.80%. The results show that the improved PSO algorithm is more suitable for the optimization of the poloidal field coil, has stronger optimization capabilities, and can effectively create the desired null field region, providing an important reference for the smooth breakdown of plasma in the TT-1 device.

关键词： Tokamak device Mutual inductance coefficients Null field region The poloidal field coil current optimization particle swarm optimization algorithm

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Evaluating the performance of genetic and particle swarm optimization algorithms to select an appropriate scenario for forecasting energy demand using economic indicators: residential and commercial sectors of Iran

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INTERNATIONAL JOURNAL OF ENERGY AND ENVIRONMENTAL ENGINEERING 2015年第4期6卷 345-355页

作者： Nazari, Hesam Kazemi, Aliyeh Hashemi, Mohammad-Hosein Sadat, Mir Mahmoud Nazari, Mahboobeh Univ Tehran Fac Management Chamran HighwayPOB 14155-6311 Tehran Iran Shahid Beheshti Univ Fac Power & Water Shahid Abbaspour Tehran Iran Avicenna Res Inst ACECR Nanobiotechnol Res Ctr Tehran Iran

Energy supply security is one of the strategic issues of all states. In Iran, about 35 % of the total energy is consumed by the residential and commercial sectors. According to the importance of residential and commercial sectors in energy consumption, this paper develops different models to analyze energy demand of residential and commercial sectors. The GA and PSO energy demand estimation models (GA-DEM, PSO-GEM), a suitable model for this study, is used to estimate future energy demand of the sectors. Energy demand of these sectors has been estimated in two various forms, exponential and linear models. These sectors consumption in Iran from 1967 to 2010 is considered as the case of this study. The available data are partly used for finding the optimal, or near-optimal values of the coefficient parameters (1967-2006) and partly for testing the models (2007-2010). Our results show that PSO-DEM exponential model with inputs including, value added of all economic sectors, value of made buildings, the population and the electrical and fuel appliance price index using the mean absolute percentage error on test data is the most suitable model. Finally, based on the best scenario, the energy demand of residential and commercial sectors is estimated 1718 mega barrel of crude oil equivalent (MBOE) (1 barrel = 0.159 m(3)) up to the year 2032.

关键词： Forecasting Residential and commercial sectors Energy demand Genetic algorithm particle swarm optimization algorithm

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Parameters optimization of hybrid strategy recommendation based on particle swarm algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 168卷 114388-114388页

作者： Cai, Biao Zhu, Xinping Qin, Yangxin Chengdu Univ Technol Coll Informat Sci & Technol Chengdu 610059 Peoples R China

With the unprecedented development in the internet technology, the information overload issues have become more and more complex, resulting in users being unable to obtain the target information accurately and effectively in selecting the required information from a large pool of surfed data. In view of this a recommendation system can be used to predict the user's selection probability for different potential objects as an important tool, which can help to solve the information overload issues. So far, many personalized recommendation algorithms based on bipartite graphS have been proposed, most of which are based on the similarity degree among users or items, such as collaborative filtering (CF), mass diffusion (MD) and heat conduction (HC). Among many recommendation algorithms, the performances of algorithms are varied. MD algorithm has high recommendation accuracy but poor diversity, while HC algorithm has good diversity but low accuracy. In order to solve the dilemma in accuracy and diversity, some hybrid recommendation algorithm have been proposed. This paper has mainly focused on the hybrid recommendation algorithm HHM, and pointed out its shortcomings. Based on the reconsideration of the effect of item popularity in the recommendation process, an improved hybrid recommendation algorithm using dual parameter called IHM was proposed. The particle swarm optimization (PSO) algorithm was applied to the parameter optimal of the hybrid recommendation algorithm to obtain the parameters of the algorithm. Experiments on 3 real datasets indicated that the IHM algorithm is better than HHM algorithms in terms of the recommendation accuracy, diversity and novelty. Meanwhile, the IHM algorithm can also improve the recommendation for items with lower popularity and solve the cold start problem.

关键词： Recommendation system Personalized recommendation Information overload Hybrid recommendation algorithm particle swarm optimization algorithm

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Chaotic Butterfly optimization algorithm based on particle swarm optimization

International Core Journal of Engineering

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International Core Journal of Engineering 2022年第3期8卷 420-432页

作者： Gang Yang

In this article, I proposes an improved particle swarm hybrid butterfly optimization algorithm (IPBOA).In order to solve the problem of low accuracy and slow convergence in the butterfly optimization algorithm (BOA), I designed two strategies to improve the basic butterfly optimization algorithm (BOA) algorithm. The improved particle swarm hybrid butterfly optimization algorithm (IPBOA) includes three main steps. First, I added the cubic chaotic map to initialize the butterfly population. This has increased the diversity of butterfly populations to a certain extent; Secondly, I adopt a non-linear parameter control strategy. This strategy can effectively balance the global search and local search capabilities of the algorithm to a certain extent; finally, in order to improve the basic butterfly optimization algorithm (BOA) for global optimization, I mixed the particle swarm optimization (PSO) algorithm with BOA. In order to verify the effectiveness of the proposed algorithm, I selected 10 test functions. I use these test functions for comparative experiments. Experimental comparison results show that, compared with famous group optimization algorithms such as PSO and BOA,the performance of the IPBOA algorithm I proposed has been further improved. It has fast convergence speed, higher optimization precision and stronger robustness in the optimization problem of benchmark test functions.

关键词： Butterfly optimization algorithm Cubic Chaos Nonlinear Parameters particle swarm optimization algorithm Benchmark Test Function

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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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Numerical Analyses of Three Inertia-weight-improvement-based particle swarm optimization algorithms 2

Numerical Analyses of Three Inertia-weight-improvement-based...

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2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA)

作者： Chen, Jie Ye, Fang Jiang, Tao Harbin Engn Univ Coll Informat & Commun Engn Harbin Heilongjiang Peoples R China

ISBN: (纸本)9781538620304

As one widely applied swarm intelligent algorithm, particle swarm optimization (PSO) algorithm has obtained the attention of various scholars with its advantages of easy implementation, high precision and fast convergence. Firstly, aiming to solve the problems that PSO has low searching speed and PSO is easy to fall into local optimal solution especially when dealing with high-dimension model, this paper modifies the PSO from the point of inertia weight improvements. The three improved inertia weights separately update the searching speed of particles with different principles, which are called linear-decline inertial weight, stochastic inertial weight and adaptive inertia weight. Then, three problems of evaluation non-linear function extremum are adopted in the simulation part to verify the convergence speed and optimization ability of three modified PSO algorithms. The numerical analyses demonstrate that these three improved inertia weights can guide particles to find the optimal solution more precisely and quickly. Thus, three proposed inertia-weight-improvement-based PSO algorithms have certain significance.

关键词： particle swarm optimization algorithm inertia weight convergence analyses

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Prediction of compressive strength of multiple types of fiber-reinforced concrete based on optimized machine learning models

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 152卷

作者： Zhao, Ning Zhang, Haonan Xie, Peilun Chen, Xiaowei Wang, Xuewei Sichuan Agr Univ Sch Civil Engn Chengdu 611830 Peoples R China Southeast Univ Sch Civil Engn Nanjing 211189 Peoples R China

The accurate prediction of compressive strength of fiber-reinforced concrete (FRC) is essential for its design optimization and performance assessment, as it can significantly reduce testing costs. However, the high variability of FRC's compressive strength poses considerable prediction challenges. Current research has predominantly focused on developing prediction models for single-type FRC, while the prediction of compressive strength across multiple types of FRC remains a critical and unresolved issue in the field. To address this gap, this study proposes a novel hybrid approach integrating Deep Neural Networks (DNN), Generalized Regression Neural Networks (GRNN), and Extreme Gradient Boosting (XGBoost) with optimization techniques-particle swarm optimization (PSO), Bayesian optimization (BO), and Bald Eagle Search (BES). A comprehensive dataset of 386 peer-reviewed compressive strength measurements was utilized, with K-means++ algorithm ensuring balanced training and testing set distributions. Hyperparameter optimization for DNN, GRNN, and XGBoost was conducted by combining PSO, BO, and BES with five-fold cross-validation. Results demonstrate strong model performance, with the BES-XGBoost model achieving the highest accuracy, exhibiting deviations of approximately 15 % between actual and predicted values. Additionally, Shapley Additive Explanations (SHAP) and partial dependence plots were employed to analyze the feature importance on compressive strength and the coupling effects of fiber characteristics. The proposed approach not only provides enhanced prediction accuracy for multiple types of FRC but also delivers valuable insights for FRC proportioning design, advancing the field of FRC performance evaluation.

关键词： Fiber-reinforced concrete Neural network Compressive strength prediction particle swarm optimization algorithm Bayesian optimization algorithm Bald eagle search optimizer

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A Weighted Ensemble Learning algorithm Based on Diversity Using a Novel particle swarm optimization Approach

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algorithmS 2020年第10期13卷 255-255页

作者： You, Gui-Rong Shiue, Yeou-Ren Yeh, Wei-Chang Chen, Xi-Li Chen, Chih-Ming Fujian Business Univ Dept Management Engn Fuzhou 350012 Peoples R China Fujian Business Univ Informat Technol Ctr Fuzhou 350012 Peoples R China Natl Tsing Hua Univ Dept Ind Engn & Management Engn Hsinchu 300 Taiwan Fujian Business Univ Dept Finance Fuzhou 350012 Peoples R China

In ensemble learning, accuracy and diversity are the main factors affecting its performance. In previous studies, diversity was regarded only as a regularization term, which does not sufficiently indicate that diversity should implicitly be treated as an accuracy factor. In this study, a two-stage weighted ensemble learning method using the particle swarm optimization (PSO) algorithm is proposed to balance the diversity and accuracy in ensemble learning. The first stage is to enhance the diversity of the individual learner, which can be achieved by manipulating the datasets and the input features via a mixed-binary PSO algorithm to search for a set of individual learners with appropriate diversity. The purpose of the second stage is to improve the accuracy of the ensemble classifier using a weighted ensemble method that considers both diversity and accuracy. The set of weighted classifier ensembles is obtained by optimization via the PSO algorithm. The experimental results on 30 UCI datasets demonstrate that the proposed algorithm outperforms other state-of-the-art baselines.

关键词： ensemble learning diversity machine learning particle swarm optimization algorithm weighted ensemble

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Research on pulse laser detection anti-interference method in smoke environment

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INFRARED PHYSICS & TECHNOLOGY 2025年 145卷

作者： Qu, Qinyang Su, Zerun Xu, Xiaobin Li, Moyuan Chen, Shanshan Ran, Yingying Tan, Zhiying Hohai Univ Coll Mech & Elect Engn Chang Zhou 213200 Peoples R China Hohai Univ Coll Informat Sci & Engn Chang Zhou 213200 Peoples R China

To address the problem of smoke medium interference on pulse laser detection signals, this study employs Monte Carlo method to precisely establish the backscattered signals model of pulsed lasers in smoke conditions, and proposes an anti-interference strategy combining particle swarm optimization (PSO) and convolutional neural network (CNN) by echo signals collected experimentally. Specifically, this study uses the Monte Carlo method and scattering phase function to simulate particle collisions and constructs a sampling model of reflected photons based on the target surface's reflective properties. Semi-analytical reception technology is used to extract the received backscattered photon signals. Finally, a CNN is built taking all the backscattered data points as the input. The parameter of the network is optimized using PSO algorithm. To verify the accuracy of the simulated backscattered echo signals and the effectiveness of the anti-interference algorithm, a laser detection experiment in a smoke environment was conducted. The experimental results show that the simulated detection back- scattered signals and the experimental backscattered signals have a high degree of consistency, with a maximum root mean square error of only 0.043. The recognition accuracy of the CNN optimized by the PSO algorithm is 96.154 %, with a computational parameter size of 193.008 KB. This study provides solid theoretical support and technical assurance for the pulse laser detection technology in the anti-interference field.

关键词： Pulse laser Smoky environment Echo model Monte Carlo method particle swarm optimization algorithm

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