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modified particle swarm optimization for nonconvex economic dispatch problems

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2015年 69卷 304-312页

作者： Basu, M. Jadavpur Univ Dept Power Engn Kolkata 700098 India

This paper presents modified particle swarm optimization to solve economic dispatch problems with non-smooth/non-convex cost functions. particle swarm optimization performs well for small dimensional and less complicated problems but fails to locate global minima for complex multi-minima functions. This paper proposes Gaussian random variables in velocity term which improves search efficiency and guarantees a high probability of obtaining the global optimum without considerably worsening the speed of convergence and the simplicity of the structure of particle swarm optimization. The efficacy of the proposed method has been demonstrated on four test problems and four different non-convex economic dispatch problems with valve-point effects, prohibited operating zones with transmission losses, multiple fuels with valve point effects and the large-scale Korean power system with valve-point effects and prohibited operating zones. The results of the proposed approach are compared with those obtained by other evolutionary methods reported in the literature. It is found that the proposed modified particle swarm optimization based approach is able to provide better solution. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： modified particle swarm optimization Economic dispatch Prohibited operating zones Valve-point loading Multi-fuel option

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modified particle swarm optimization for Non-smooth Non-convex Combined Heat and Power Economic Dispatch

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2015年第19期43卷 2146-2155页

作者： Basu, Mousumi Jadavpur Univ Dept Power Engn Kolkata 700098 W Bengal India

This article presents modified particle swarm optimization to solve the non-smooth non-convex combined heat and power economic dispatch problem. Valve-point loading and prohibited operating zones of conventional thermal generators are taken into account. particle swarm optimization performs well for small-dimensional and less complicated problems but fails to locate global minima for complex multi-minima functions. This article proposes Gaussian random variables in the velocity term, which improves search efficiency and guarantees a high probability of obtaining the global optimum without significantly impairing the speed of convergence and the simplicity of the structure of particle swarm optimization. The effectiveness of the proposed method has been verified on two test systems. The results of the proposed approach are compared with those obtained by other evolutionary methods. It is found that the proposed modified particle swarm optimization based approach is able to provide a better solution.

关键词： modified particle swarm optimization combined heat and power economic dispatch cogeneration prohibited operating zone valve-point loading

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modified particle swarm optimization Algorithm for Sizing Photovoltaic System

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IEEE LATIN AMERICA TRANSACTIONS 2017年第2期15卷 283-289页

作者： Souza, J. S. Molina, Y. P. Araujo, C. S. Farias, W. P. Araujo, I. S. Univ Fed Paraiba UFPB Joao Pessoa Paraiba Brazil

This paper presents a method of sizing photovoltaic panels using modified particle swarm optimization (MPSO) in order to reduce energy losses and improve voltage profile. The proposed method was applied to the IEEE 13-bus distribution network, and finally was validated using the real micro grid of the Federal University of Paraiba (UFPB). The results show that the proposed method has the ability to provide sizing of photovoltaic modules distributed in a micro grid, improving the voltage profile and reducing energy losses.

关键词： Photovoltaic panels modified particle swarm optimization micro grid sizing photovoltaic panels

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modified particle swarm optimization in a time-dependent vehicle routing problem: minimizing fuel consumption

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optimization LETTERS 2017年第1期11卷 121-134页

作者： Norouzi, Narges Sadegh-Amalnick, Mohsen Tavakkoli-Moghaddam, Reza Univ Tehran Sch Ind Engn Coll Engn Tehran Iran Univ Tehran Coll Engn Engn Optimizat Res Grp Tehran Iran

In this paper, a new mathematical model for a time-dependent vehicle routing problem (TDVRP) is introduced as one of the sub-problems in the green VRP field. The model results in reducing carbon emissions by minimizing travel times, while congested routes are avoided and customers are served at the time of the day with an optimized speed. Hence, this paper presents a new bi-objective mathematical model in such a way that the total travel times and total fuel consumption are minimized in a time-dependent situation by considering the air drag, roads' physical condition, the physical features of each vehicle, and the weight and load of the vehicles. To solve such a hard problem, a new method based on the modified particle swarm optimization (PSO) algorithm is proposed and the results are compared with the standard PSO on well-known benchmark problems with constant travel speeds and time-dependent travel speeds. The results show the capability of the proposed optimization algorithm for handling this problem.

关键词： optimization Mathematical modelling Vehicle routing problem Transportation modified particle swarm optimization Fuel consumption

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modified particle swarm optimization based magnetic resonance imaging analysis for identification of risk factors tissue damage incidence

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EXPERT SYSTEMS 2023年第4期40卷 e12856-e12856页

作者： Ke, Ji Wang, Shufa Li, Jiangchun Liu, Quan Hubei Univ Police Dept Forens Sci & Criminal Intelligence 99 Nanniwan Ave Wuhan 430034 Hubei Peoples R China Hubei Key Lab Forens Sci Wuhan Hubei Peoples R China Hubei Univ Police Expt Res Ctr Wuhan Hubei Peoples R China

This study was to analyse the risk factors of unstable patella and the incidence of tissue damage (TD) with magnetic resonance imaging (MRI) image under the modified particle swarm optimization (MPSO). In this study, 45 patients with patella instability who visited hospital from 1 November 2018 to 28 September 2020 were selected as the experimental group (group E), and 45 volunteers with healthy knee joints were recruited as the control group (group C). MRI images under MPSO were to evaluate the four groups of related indicators (trochlear groove angle, trochlear groove depth, trochlear surface ratios, and Insall-Salvati index) of unstable patella in the two groups and count the incidence of TD caused by unstable patella. Comparison of four indicators revealed that there was an obvious difference in the data of the two groups. The abnormal rate of patients with unstable patella in the group E was much higher than that of the group C, and the difference was statistical (p < 0.05). The incidences of trochlea dysplasia and high patella accounted for 88.9% and 57.8% in group E and accounted for 4.4% and 3.2% in the group C, respectively, showing statistically obvious differences (p < 0.05). The incidence of patella cartilage TD, thigh cartilage TD, lateral meniscus TD, medial meniscus TD, patella medial retinaculum TD, patella lateral retinaculum TD, patellar ligament damage, and joint effusions were 14.5%, 11.45%, 50.38%, 49.62%, 10.69%, 16.03%, 9.16%, and 79.39%, respectively in the patients with unstable patella in the group E. In short, trochlear dysplasia and high patella were risk factors for unstable patella, and unstable patella was one of the risk factors for multiple TDs of the knee joint. This study provided a new research basis for the early treatment of unstable patella.

关键词： incidence of tissue damage magnetic resonance imaging modified particle swarm optimization risk factors unstable patella

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Quantum neural network-based intelligent controller design for CSTR using modified particle swarm optimization algorithm

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2019年第2期41卷 392-404页

作者： Salahshour, Esmaeil Malekzadeh, Milad Gordillo, Francisco Ghasemi, Javad Babol Noshirvani Univ Technol Dept Elect Engn Babol Sar Iran Univ Seville Dept Syst Engn & Automat Control Seville Spain ESIEE Paris Univ Dept Elect Engn Paris France

In this paper, a combination of a multi-layer quantum neural network (QNN) with the particle swarm optimization (PSO) algorithm is used with the aim of controlling a continuous stirred-tank reactor (CSTR) system. The CSTR process is highly non-linear and its dynamics are significantly sensitive to system parameter values. Normally, conventional controllers with fixed coefficients are applied to control this kind of system. In highly non-linear systems, having fixed controller coefficients in different operational conditions may decrease the performance of controllers. In the proposed scheme, by using a multi-layer QNN, an adaptive structure is designed for a PI-D controller. In order to train the QNN, the PSO algorithm is employed. With the aim of improving accuracy and convergence speed of the training process, some modifications have been applied to the movement of each particle towards the optimal point. Furthermore, in order to evaluate the performance of the system, the proposed scheme has been applied in various operational situations in the presence of disturbances and set-point change. The efficiency of the proposed control scheme is compared with PID and a perceptron neural network-based controller, and the simulation results endorse that the proposed scheme shows significantly better performance in different operating conditions.

关键词： Adaptive PI-D controller continuous stirred-tank reactor modified particle swarm optimization optimization quantum neural network

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Model order formulation of a multivariable discrete system using a modified particle swarm optimization approach

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swarm AND EVOLUTIONARY COMPUTATION 2011年第4期1卷 204-212页

作者： Deepa, S. N. Sugumaran, G. Anna Univ Technol Coimbatore Dept Elect & Elect Engn Coimbatore 641047 Tamil Nadu India

This paper proposes an algorithm for model order formulation of an absolutely stable higher order linear time invariant multivariable discrete system using a new version of evolutionary computing technique namely, modified particle swarm optimization (MPSO). A simple adjunct polynomial method has been proposed for obtaining the initial seed values of the lower order multivariable system. In the modified PSO, the movement of a particle is governed by three behaviors namely, inertia, cognitive and social. The cognitive behavior helps the particle to remember its previously visited best position. This paper proposes to split the cognitive behavior into two sections. This modification is efficiently utilized to obtain a better lower order system that reflects the characteristics of the original higher order system by minimizing the integral squared error with the steady state constraints. The results obtained are compared with the earlier techniques utilized, to validate its ease of computation. The proposed algorithm is illustrated with a numerical example from the literature. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Integral squared error Lower order system Model order formulation modified particle swarm optimization Multivariable discrete systems

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Simultaneous coordination of distinct plug-in Hybrid Electric Vehicle charging stations: A modified particle swarm optimization approach

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ENERGY 2017年 138卷 92-102页

作者： Suganya, S. Raja, S. Charles Venkatesh, P. Thiagarajar Coll Engn Dept Elect & Elect Engn Madurai 625015 Tamil Nadu India

As Plug-in Hybrid Electric Vehicles are environment friendly, it is envisioned as an alternative solution for the existing transportation system. However, simultaneous charging of electric vehicles may stimulate increase in peak load, causing voltage deviation and power loss issues. Different from other approaches, this paper presents an alternative way to tackle these issues. A novel two area distribution system, each following a different mobility pattern is presented. This model considers various sites namely optimal, midst, and unfit site for locating the charging stations in both the areas simultaneously. Moreover, the scheduling of the plug-in Hybrid Electric Vehicle is addressed using a meta-heuristic solving tool as it involves simultaneous scheduling of vehicles in both areas. To illustrate the cost reduction obtained with this model, the performance is evaluated in a standard test system namely IEEE 69-bus radial distribution system. The test results support the efficiency of the proposed two area framework and the new solving tool. The economic and the stability aspect of the system are addressed under various scenarios and several important findings are revealed. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Distribution system modified particle swarm optimization Plug-in Hybrid Electric Vehicles Charging and discharging

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Designing an interval type-2 fuzzy disturbance observer for a class of nonlinear systems based on modified particle swarm optimization

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APPLIED INTELLIGENCE 2020年第11期50卷 3731-3747页

作者： Naderi, Shokoufeh Rezaie, Behrooz Faramin, Mostafa Babol Noshirvani Univ Technol Fac Elect & Comp Engn Babol Sar Iran

This paper presents a new interval type-2 fuzzy disturbance observer design for a class of nonlinear systems using modified particle swarm optimization. The design procedure has two main parts, including the selection of the initial structure of the type-2 fuzzy disturbance observer, and the optimization of the observer parameters using a modified particle swarm optimization algorithm. The modified particle swarm optimization algorithm has a better performance in terms of the accuracy and convergence rate compared with the standard particle swarm optimization and many other evolutionary algorithms. In this algorithm, the upper and lower bounds of the search space are defined for the parameters of each particle based on their values, and weaker particles are substituted with new particles. To accentuate the outstanding performance of the modified particle swarm optimization for the considered task, its performance is compared with five famous meta-heuristic optimization algorithms. In addition, utilizing interval type-2 fuzzy systems in the proposed observer provides more robustness compared with type-1 fuzzy systems. The effectiveness of the proposed fuzzy disturbance observer is shown through computer simulation and experimental results for the ball and beam system, while the system is subjected to sinusoid and square disturbances, and a comparison is drawn to indicate the superiority of the proposed fuzzy disturbance observer over the other observers.

关键词： Fuzzy disturbance observer Interval type-2 fuzzy system modified particle swarm optimization Ball and beam system

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Schedule generation scheme for solving multi-mode resource availability cost problem by modified particle swarm optimization

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JOURNAL OF SCHEDULING 2015年第3期18卷 285-298页

作者： Qi, Jian-Jun Liu, Ya-Jie Jiang, Ping Guo, Bo Natl Univ Def Technol Coll Informat Syst & Management Changsha 410073 Hunan Peoples R China

The resource availability cost problem (RACP) (Mohring, Operations Research, 32:89-120, 1984) is commonly encountered in project scheduling. RACP aims to minimize the resource availability cost of a project by a given project deadline. In this study, RACP is extended from a single mode to a multi-mode called multi-mode RACP (MMRACP), which is more complicated than RACP but more convenient in practice. To solve MMRACP efficiently, forward activity list (FAL), a schedule generation scheme, is proposed. Heuristic algorithms are designed according to the characteristics of FAL to repair infeasible solutions and to improve the fitness of the solution. modified particle swarm optimization (MPSO), which combines the advantages of particle swarm optimization and scatter search, is proposed to make the search for the best solution efficient. Computational experiments involving 180 instances are performed to validate the performance of the proposed algorithm. The results reveal that MPSO using FAL is a very effective method to solve MMRACP.

关键词： Project scheduling Multi-mode resource availability cost problem Schedule generation Forward activity list modified particle swarm optimization

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