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Enhanced nanolaser power for metamaterial waveguide using Bragg reflector and pso algorithm

PHYSICA SCRIPTA 2025年第3期100卷 035532-035532页

作者： Kadhim, Nawres M. Vahed, Hamid Soofi, Hadi Univ Tabriz Fac Elect & Comp Engn Tabriz Iran

This work enhanced the laser output power from a nanolaser device utilizing an InGaAsP waveguide. We added the DBR structure to the meta-waveguide gain strips with 14 waveguides. Then, we used the particle swarm optimization (pso) algorithm to improve the dimensions, especially the meta-WG's width and the DBR structure's x, y, span, and period dimensions. From this new design, we obtained an increase in the output laser power in both the backward and forward directions, respectively, as the value of the increase in the backward direction reached ten orders of magnitude, meaning an output power of 2 mW. In comparison, it increased the laser output power in the forward direction by 2.61 orders of magnitude, indicating an output power value of 0.7 mW. This increase was achieved while maintaining the original device dimensions and operating at room temperature.

关键词： DBR structure FDTD metamaterial waveguide nanolaser pso algorithm SOI

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Optimisation of decision making for construction projects of assembly buildings based on improved pso algorithm

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ARCHIVES OF CIVIL ENGINEERING 2025年第1期71卷 113-125页

作者： Wang, Peng Henan Finance Univ Fac Engn & Econ Zhengzhou 451464 Peoples R China

A cutting-edge construction method called fitted construction allows for several parallel lines of work to speed up construction and enhance building quality. However, achieving optimal project decisions for global construction projects demands a high level of objective decision-making. To enhance the decision- making process, this research utilizes particle swarm algorithms to optimize construction project decisions in assembled buildings. To tackle the issue of early convergence in particle swarm algorithms, three swarm enhanced particle swarm algorithms are proposed by merging the variational mechanism of the differential evolution algorithm and quantifying the decision making tasks for assembly building construction projects to be solved by the enhanced particle swarm algorithm. Regarding the research results, the upgraded particle swarm algorithm achieved a fundamental convergence in 20 iterations whilst resolving the Sphere, Rosebrock, Rastrigin, and Griewank functions. The improved particle swarm algorithm converges to an optimal solution of -19.208 within 20 iterations on the Holder function, with an optimal domain of [8.055, -9.665]. The results of the optimization study for the decision-making problem of the assembly building project demonstrate that implementing Sigmoid smoothing yields a minimum duration problem of 0.755 and a minimum duration of 45 days. The optimal cost and time required to solve the problem of economic maximisation strategy using the enhanced particle swarm method are 500,000 and 52 days, respectively. The results indicate that the improved particle swarm approach outperforms conventional algorithms in the decision-making process for assembly building projects, maintaining computational accuracy throughout.

关键词： assembly building DE algorithm global optimisation search project decision making pso algorithm

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Hybrid Improved pso algorithm for Soil Property Parameter Estimation

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APPLIED SCIENCES-BASEL 2025年第8期15卷 4451-4451页

作者： Li, Mude Shi, Aiping Shi, Yefan Zhang, Tao Qu, Cu Ye, Lihua Jiangsu Univ Sch Automot & Traff Engn Zhenjiang 212013 Peoples R China Jiangsu Univ Sch Energy & Power Engn Zhenjiang 212013 Peoples R China

This study proposes a hybrid pso-EDO algorithm, integrating Particle Swarm Optimization (pso) and the Exponential Distribution Optimizer (EDO) for efficient and accurate estimation of soil property parameters. The proposed algorithm combines the strengths of Standard pso (Spso) and the Exponential Distribution Optimizer (EDO). Three key innovations are introduced: (1) SPM chaotic mapping enhances initial population diversity;(2) dynamic inertia weight balances global exploration and local exploitation;(3) the memoryless property of EDO improves escape capability from local optima. Benchmark tests demonstrate that pso-EDO achieves near-theoretical optimal convergence errors (mean error <= 10-16 for unimodal functions such as F1 and F2) and reduces the computation time by 14.5% compared to EDO. For multimodal functions (e.g., F3), pso-EDO significantly outperforms pso-WOA (Particle Swarm Optimization-Whale Optimization algorithm) with a 22.3% reduction in error. Simulation experiments further validate its engineering practicality: in soil parameter estimation, pso-EDO completes 1000 iterations in just 1.95 s, with key parameters (e.g., sinkage coefficient n) controlled within a 7.32% error margin. This provides an efficient solution for real-time traversability assessment of autonomous vehicles on soft terrains.

关键词： pso algorithm EDO algorithm soil property estimation off-road vehicles terrain mechanics parameter optimization autonomous vehicles

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A new method for optimal location and sizing of capacitors in distorted distribution networks using pso algorithm

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SIMULATION MODELLING PRACTICE AND THEORY 2011年第2期19卷 662-672页

作者： Taher, Seyed Abbas Karimian, Ali Hasani, Mohammad Univ Kashan Dept Elect Engn Kashan *** Iran

This paper presents an optimization algorithm for simultaneous improvement of power quality (PQ), optimal placement and sizing of fixed capacitor banks in radial distribution networks in the presence of voltage and current harmonics. The algorithm is based on particle swarm optimization (pso). The objective function includes the cost of power losses, energy losses and those of the capacitor banks. Constraints include voltage limits, number/size of installed capacitors at each bus, and PQ limits of standard IEEE-519. Using a newly proposed fitness function, a suitable combination of the objective function and relevant constraints is defined as a criterion to select a set of the most suitable buses for capacitor placement. This method is also capable of improving particles in several steps for both converging more readily to the near global solution as well as improving satisfaction of the power quality constraints. Simulation results for the 18-bus and 33-bus IEEE distorted networks using the proposed method are presented and compared with those of previous works. In the 18-bus IEEE distorted network, this indicated an improvement of 3.29% saving compared with other methods. Using the proposed optimization method and simulation performed on the 33-bus IEEE distorted network an annual cost reduction of 31.16% was obtained. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Capacitor banks pso algorithm Harmonic Placement Harmonic power flow Non-linear load Optimization Power quality (PQ) Sizing

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LocpsoRank-Prediction of Ranking of Web Services Using Location-Based Clustering and pso algorithm

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INTERNATIONAL JOURNAL OF WEB SERVICES RESEARCH 2018年第3期15卷 38-60页

作者： Mareeswari, V Sathiyamoorthy, E. Vellore Inst Technol Sch Informat Technol & Engn Vellore Tamil Nadu India

Online communities will provide the trustworthiness of their services and also, recommendation systems to improve the commercial value in this competitive business world. Prediction is the greatest method to get people interested whatever offered. Traditional QoS based prediction approach, predicts the QoS value of web service when the incompletion QoS records. This proposed approach introduced cluster based pso algorithm, which provides better scalability, simplicity, and efficiency. It uses the density-based clusters based on web service users' location and ranks the web services based on pso algorithm. Here, top-K users are selecting based on web service preferences and weights are giving for experienced neighbors. To achieve the high-quality outcome of the ranking sequence by the control of fitness function and verified by AP correlation coefficient method. The experimental results discussed how this proposed approach provided better prediction accuracy and compared with other existing approaches.

关键词： Average Precision Cluster Based Ranking DBSCAN Location Based Location Based pso Prediction Preference Relation pso algorithm QoS Ranking Web Service Ranking

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pso algorithm Based Thermal Contact Resistance Estimation for Variable Force Hand/Object Interaction 22

PSO Algorithm Based Thermal Contact Resistance Estimation fo...

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22nd Mediterranean Conference on Control and Automation (MED)

作者： Maamir, Fouad Guiatni, Mohamed Morsly, Yacine Kheddar, Abderrahmane Ecole Mil Polytech Control Lab Algiers 16111 Algeria CNRS Paris France

ISBN: (纸本)9781479959013

Integrating thermal interactions in multimodal systems requires understanding both users sensory abilities and the thermal behaviors of the surrounding environement. Thermal feedback, as a part of haptic feedback can assist in object identification, or in the creation of a more complete haptic signature of a given object. However, the heat exchange occurring between a fingertip and a given touched object is affected by the contact conditions. This paper proposes a Particle Swarm Optimization (pso) algorithm for identification of thermal contact resistance. This resistance is estimated for variable force Hand/Object Interaction using a thermal model describing the heat exchange. Experimental results validating the proposed approach are evaluated when touching real and simulated objects.

关键词： haptic interfaces heat transfer particle swarm optimisation pso algorithm haptic feedback heat exchange multimodal systems object identification particle swarm optimization thermal behavior thermal contact resistance estimation thermal feedback variable force hand-object interaction Contact resistance Force Heating Materials Temperature measurement Thermal resistance Thermal sensors

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Optimized pso algorithm based on the simplicial algorithm of fixed point theory

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APPLIED INTELLIGENCE 2020年第7期50卷 2009-2024页

作者： Ren, Minglun Huang, Xiaodi Zhu, Xiaoxi Shao, Liangjia Hefei Univ Technol Sch Management Hefei 230009 Peoples R China Minist Educ Key Lab Proc Optimizat & Intelligent Decis Making Hefei 230009 Peoples R China

Particle swarm optimization algorithm (pso) has been optimized from various aspects since it was proposed. Optimization of pso can be realized by optimizing its iterative process or the initial parameters and heuristic methods have been combined with the initial pso algorithm to improve its performance. In this paper, we introduce the Simplicial algorithm (SA) of fixed point theory into the optimization of pso and proposed a FP-pso (Fixed-point pso) improved algorithm. In FP-pso algorithm, the optimization of target function is converted into the problem of solving a fixed point equation set, and the solution set obtained by Simplicial algorithm (SA) of fixed point theory is used as the initial population of pso algorithm, then the remaining parameters can be obtained accordingly with classical pso algorithm. Since the fixed point method has sound mathematical properties, the initial population obtained with FP-pso include nearly all the approximate local extremes which maintain the diversity of population and can optimize the flight direction of particles, and shows their advantages on setting other initial parameters. We make an experimental study with five commonly used testing functions from UCI (University of California Irvine) which include two single-peak functions and three multi-peak functions. The results indicate that the convergence accuracy, stability, and robustness of FP-pso algorithm are significantly superior to existing improve strategies which also optimize pso algorithm by optimizing initial population, especially when dealing with complex situations. In addition, we nest the FP-pso algorithm with four classical improved pso algorithms that improve pso by optimizing iterative processes, and carry out contrast experiments on three multi-peak functions under different conditions (rotating or non-rotating). The experimental results show that the performance of the improved algorithm using nested strategy are also significantly enhanced compared with

关键词： pso algorithm FP-pso algorithm Simplicial algorithm Fixed point Initial population

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A comparison among the results of various dispersion relations for the ellipsometric data of Mn:ZnO thin films using pso algorithm

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PHYSICA B-CONDENSED MATTER 2018年 545卷 125-133页

作者： Faraji, M. Esmaeili, A. Shirzad, A. Urmia Univ Campus Dept Sci Shahid Beheshti Ave Orumiyeh Iran Urmia Univ Dept Phys 11 Km Serow Rd Orumiyeh Iran Urmia Univ Technol Dept Civil Engn Band Rd Orumiyeh Iran

Using particle swarm optimization (pso) algorithm, the Cauchy, Forouhi-Bloomer (FB) and Sellmeier parametrization models were compared for the Mn doped ZnO (Mn:ZnO) thin films. The variation of both refractive and extinction coefficients with Mn concentration was also discussed. Our calculations demonstrate that despite the Sellmeier model exhibits the best fit with the lowest root-mean-square error (RMSE) in the transparent region, the FB model is well-suited in the both transparent and absorbing areas. Different energy gaps, roughnesses and thicknesses were obtained by the mentioned models. A comparison with the results of Tauck-Loretz model along with an exciton-based dielectric function were also done for our results.

关键词： Ellipsometry Cauchy Sellmeier Forouhi-Bloomer ZnO pso algorithm

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A pso algorithm for multi-objective hull assembly line balancing using the stratified optimization strategy

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COMPUTERS & INDUSTRIAL ENGINEERING 2016年 98卷 53-62页

作者： Yuguang, Zhong Bo, Ai Yong, Zhan Harbin Engn Univ Coll Mech & Elect Engn Harbin 150001 Peoples R China

Hull assembly line (HAL) is a mixed-model assembly line on which different hull blocks can be assembled at the same time. Aiming at the balance problem of HAL, minimizing the cycle time, minimizing the static load *** workstations, minimizing the dynamic load balancing in all workstations, and minimizing the multi-station associated complexity are considered as optimization objectives. An improved discrete particle swarm optimization (IDpso) algorithm based on the stratified optimization idea is developed for scheduling of the multi-objective problem. In the proposed algorithm, particles were coded by a two-dimensional task-oriented representation method, and then collaboration and competition of particle individuals are simulated by crossover and mutation operators in the genetic algorithm (GA). The performance of the proposed hybrid algorithm is examined over several test problems in terms of solution quality and running time. Finally, a practical case is used to analyze the effectiveness and feasibility of the stratified scheduling strategy. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Assembly line balancing problem Shipbuilding industry pso algorithm Stratified optimization

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Neural emulator for nonlinear systems based on pso algorithm: real-time validation

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第4期27卷 5107-5119页

作者： Farhat, Yassin Atig, Asma Zribi, Ali Ben Abdennour, Ridha Univ Gabes Natl Engn Sch Gabes Res Lab Numer Control Ind Proc Omar Ibn Khattab St Gabes 6029 Tunisia Univ Tabuk King Faisal Rd Tabuk 47512 Saudi Arabia

Searching an optimal value of the neural emulator adaptive rate presents a great problem. Indeed, a new scheme of neural emulators based on the Particle Swarm Optimization (pso) algorithm for nonlinear systems is adopted in this paper. The main goal of this approach consists in adjusting effectively the neural emulator adaptive rate in order to accelerate the convergence speed and to improve the precision degree. The obtained results are compared with those reached with an intelligent tuning strategy. An experimental validation of the new emulator adaptation is carried on chemical reactor. Efficiency of the proposed method is proved according to the obtained performances.

关键词： Neural emulation pso algorithm Adaptive rate Nonlinear systems Experimental validation Chemical reactor

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