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Production planning and scheduling by means of artificial immune systems and particle swarm optimisation algorithms

INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2012年第4期4卷 237-248页

作者： Budinska, Ivana Kasanicky, Tomas Zelenka, Jan Slovak Acad Sci Inst Informat Bratislava 84507 Slovakia

The main task of the scheduling optimisation process in production systems is to minimise production cost, overall production time and to ensure optimal utilisation of the resources. Application of stochastic search techniques to find a feasible schedule that minimise cost and satisfy all constraints jointed with all products can bring a particular solution of the complexity problem. On the other hand, the cost and the time of an optimisation process have to reciprocate with the found schedule;otherwise the optimisation loses its meaning. The article presents two stochastic methods, based on biologically inspired techniques, applied on a scheduling optimisation process. The first one is based on the mechanism inspired by biological evolution and the one method applies the swarm intelligence. The application of methods is illustrated on a real world example of a production line.

关键词： artificial immune systems AISs particle swarm optimisation algorithm production planning and scheduling bio-inspired computation

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Soft computing applied to the build of textile defects inspection system

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IET COMPUTER VISION 2013年第5期7卷 373-381页

作者： Darwish, Saad Mohamed Univ Alexandria Dept Informat Technol Inst Grad Studies & Res Alexandria 21526 Egypt

The inspection of textile defects is challenging because of the large number of defects categories that are characterised by their imprecision and uncertainty. In this study, novel interval type-2 fuzzy system is proposed for resolving defects recognition problem of textile industries. The proposed system mixes interval type-2 fuzzy reasoning and swarm optimisation algorithm together in order to enhance the defects classification capabilities. Interval type-2 fuzzy logic is powerful in handling high level of indecisions in the human decision making process, including uncertainties in measurements of textile features and data used to calibrate the examination's parameters. swarm intelligence algorithm is used to optimise parameters of the membership functions to increase the accuracy of fuzzy controller. Besides, the problem of fuzzy linguistic rules learning has been tackled by utilising ant colony meta-heuristic method to reduce the complexity of the inspection system. Excellent recogniser results on real textile samples, using this system, are demonstrated.

关键词： decision making fuzzy logic fuzzy reasoning heuristic programming inspection learning (artificial intelligence) particle swarm optimisation production engineering computing textile industry ant colony optimisation soft computing textile defect inspection system defects recognition problem textile industries interval type-2 fuzzy reasoning system particle swarm optimisation algorithm defects classification capability interval type-2 fuzzy logic human decision making process examination parameter calibration swarm intelligence algorithm membership functions fuzzy controller fuzzy linguistic rules learning ant colony metaheuristic method

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Global MPPT of Solar PV Modules using a Dynamic PSO algorithm under Partial Shading Conditions

Global MPPT of Solar PV Modules using a Dynamic PSO Algorith...

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IEEE Conference on Clean Energy and Technology (CEAT)

作者： Huynh, Duy C. Nguyen, Tuong M. Dunnigan, Matthew W. Mueller, Markus A. Univ Technol Ho Chi Minh City Vietnam Heriot Watt Univ Edinburgh Midlothian Scotland Univ Edinburgh Edinburgh Midlothian Scotland

ISBN: (纸本)9781479932375

This paper proposes a novel global maximum power point tracking (MPPT) strategy for solar photovoltaic (PV) modules under partial shading conditions using a dynamic particle swarm optimisation (PSO) algorithm. Solar PV modules have non-linear V-P characteristics with local maximum power points (MPPs) under partial shading conditions. In order to continuously harvest maximum power from solar PV modules, it always has to be operated at its global MPP which is determined using the proposed dynamic PSO algorithm. The obtained simulation results are compared with MPPs achieved using the standard PSO, and Perturbation and Observation (P&O) algorithms to confirm the effectiveness of the proposed algorithm under partial shading conditions.

关键词： Solar photovoltaic modules maximum power point tracking partial shading conditions particle swarm optimisation algorithm

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Relaxed stabilisation criterion for discrete T-S fuzzy systems by minimum-type piecewise non-quadratic Lyapunov function

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IET CONTROL THEORY AND APPLICATIONS 2012年第12期6卷 1918-1925页

作者： Chen, Y. -J. Ohtake, H. Tanaka, K. Wang, W. -J. Wang, H. O. Natl Cent Univ Dept Elect Engn Jhongli 32001 Taiwan Univ Electrocommun Dept Mech Engn & Intelligent Syst Chofu Tokyo 1828585 Japan Boston Univ Dept Aerosp & Mech Engn Boston MA 02215 USA

Piecewise Lyapunov functions and non-quadratic Lyapunov functions have been employed to analyse Takagi-Sugeno (T-S) fuzzy systems for getting relaxed results in the literature. Nevertheless, until now piecewise non-quadratic Lyapunov functions have not been used to design T-S fuzzy control systems. Motivated by the aforementioned concerns, this study utilises the minimum-type piecewise non-quadratic Lyapunov function to design the discrete T-S fuzzy control system. Based on the piecewise non-quadratic Lyapunov function, the switching non-parallel distributed compensation control law is proposed to obtain the relaxed stabilisation criterion. Owing to that some conditions of the proposed criterion are bilinear matrix inequalitiy conditions, the particle swarm optimisation algorithm is applied for finding out the solution of the criterion. An example is provided to illustrate the effectiveness of the proposed criterion.

关键词： optimisation techniques particle swarm optimisation discrete T-S fuzzy systems minimum-type piecewise nonquadratic Lyapunov function Takagi-Sugeno fuzzy systems discrete systems switching nonparallel distributed compensation control law stability control system synthesis Stability in control theory Control system analysis and synthesis methods Fuzzy control relaxed stabilisation criterion T-S fuzzy control system design compensation bilinear matrix inequalitiy conditions distributed control Lyapunov methods fuzzy control Discrete control systems piecewise linear techniques particle swarm optimisation algorithm

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Pattern synthesis for multi-feed reflector antennas using invasive weed optimisation

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IET MICROWAVES ANTENNAS & PROPAGATION 2012年第14期6卷 1583-1589页

作者： Foudazi, A. Mallahzadeh, A. R. Shahed Univ Elect & Elect Engn Dept Tehran Iran

The authors present a new optimisation method for pattern synthesis of multi-feed reflector antennas in this study. By using an invasive weed optimisation (IWO) algorithm, shaped beam antennas are designed. The results of the IWO algorithm for the synthesis of cosecant squared and contoured beam patterns are presented and compared with particle swarm optimisation algorithm results. The far-field radiation patterns of reflector antennas, fed by linear and planar array of conical horn antennas, are provided by a physical optics and the error between desired and provided radiation patterns is calculated by a root mean square method. The results show that the IWO algorithm, which provides good convergence, is stable and adaptive-to-change for different boundary conditions in pattern synthesis applications.

关键词： conical horn antennas particle swarm optimisation optimisation techniques contoured beam pattern synthesis IWO algorithm horn antennas Antenna accessories planar antenna arrays physical optics particle swarm optimisation algorithm conical antennas far-field radiation patterns planar antenna array mean square error methods shaped beam antennas Antenna arrays invasive weed optimisation method antenna radiation patterns Interpolation and function approximation (numerical analysis) cosecant squared pattern synthesis root mean square method reflector antenna feeds linear antenna array multifeed reflector antennas linear antenna arrays

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Hybrid shuffled frog leaping algorithm and Nelder-Mead simplex search for optimal reactive power dispatch

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IET GENERATION TRANSMISSION & DISTRIBUTION 2011年第2期5卷 249-256页

作者： Khorsandi, A. Alimardani, A. Vahidi, B. Hosseinian, S. H. Amirkabir Univ Technol Dept Elect Engn Tehran Iran

The optimal reactive power dispatch (ORPD) problem is a non-linear mixed-variable optimisation problem. This study employs a new evolutionary algorithm that expands the original shuffled frog leaping algorithm (SFLA) to solve this problem. In order to fully exploit the promising solution region, a local search algorithm known as Nelder-Mead (NM) algorithm is integrated with SFLA. The resultant NM-SFLA is very efficient in solving ORPD problem. The most important benefit of the proposed method is higher speed of convergence to a better solution. The proposed method is applied to ORPD problem on IEEE 30-bus, IEEE 57-bus and IEEE 118-bus power systems and compared with four versions of particle swarm optimisation algorithm, two versions of differential evolutionary algorithm and SFLA. The optimal setting of control variables including generator voltages, transformer taps and shunt VAR compensation devices for active power loss minimisation in a transmission system is determined while all the constraints are satisfied. The simulation results show the efficiency of the proposed method.

关键词： particle swarm optimisation transformer taps evolutionary algorithm non-linear mixed-variable optimisation problem IEEE 57-bus power systems optimisation techniques Nelder-Mead simplex search particle swarm optimisation algorithm IEEE 30-bus reactive power Transformers and reactors power transformers evolutionary computation optimal reactive power dispatch ORPD problem IEEE 118-bus power systems Nelder-Mead algorithm hybrid shuffled frog leaping algorithm Power systems

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Multi-scale assimilation of root zone soil water predictions

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HYDROLOGICAL PROCESSES 2011年第20期25卷 3158-3172页

作者： Lu, Haishen Yu, Zhongbo Horton, Robert Zhu, Yonghua Wang, Zhenlong Hao, Zhenchun Xiang, Long Hohai Univ State Key Lab Hydrol Water Resources & Hydraul En Nanjing 210098 Peoples R China Univ Nevada Dept Geosci Las Vegas NV 89154 USA Iowa State Univ Dept Agron Ames IA 50011 USA Key Lab Water Resources Anhui Prov Bengbu 233000 Peoples R China

When hydrology model parameters are determined, a traditional data assimilation method (such as Kalman filter) and a hydrology model can estimate the root zone soil water with uncertain state variables (such as initial soil water content). The simulated result can be quite good. However, when a key soil hydraulic property, such as the saturated hydraulic conductivity, is overestimated or underestimated, the traditional soil water assimilation process will produce a persistent bias in its predictions. In this paper, we present and demonstrate a new multi-scale assimilation method by combining the direct insertion assimilation method, particle swarm optimisation (PSO) algorithm and Richards equation. We study the possibility of estimating root zone soil water with a multi-scale assimilation method by using observed in situ data from the Wudaogou experiment station, Huaihe River Basin, China. The results indicate there is a persistent bias between simulated and observed values when the direct insertion assimilation surface soil water content is used to estimate root zone soil water contents. Using a multi-scale assimilation method (PSO algorithm and direct insertion assimilation) and an assumed bottom boundary condition, the results show some obvious improvement, but the root mean square error is still relatively large. When the bottom boundary condition is similar to the actual situation, the multi-scale assimilation method can well represent the root zone soil water content. The results indicate that the method is useful in estimating root zone soil water when available soil water data are limited to the surface layer and the initial soil water content even when the soil hydraulic conductivities are uncertain. Copyright (C) 2011 John Wiley & Sons, Ltd.

关键词： surface soil water content root zone soil water content Richards equation multi-scale assimilation method particle swarm optimisation algorithm

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A novel hybrid particle swarm optimisation method applied to economic dispatch

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2010年第1期2卷 9-17页

作者： Zhang, Tao Cai, Jin-ding Fuzhou Univ Coll Elect Engn & Automat Fuzhou 350002 Fujian Peoples R China

Economic load dispatch is complex and non-linear problem, it is difficult to solve the problem using traditional optimisation method. PSO has been successfully applied to a wide range of applications, in solving continuous non-linear optimisation problems. This paper presents a novel hybrid PSO algorithm with dynamic inertial weight and chaotic search to solve the problem. The improved hybrid PSO enhanced the global search ability of the algorithm and improved its convergence speed. The hybrid PSO algorithm was successfully validated for two test systems. Numerical simulation results show that the modified hybrid PSO algorithm outperformed standard PSO algorithm and genetic algorithm on the same problem and can save considerable cost of economic load dispatch.

关键词： economic dispatch particle swarm optimisation algorithm genetic algorithm GA chaotic search valve-point effect globe optimisation dynamic inertial weight non-linear optimisation

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Parallel asynchronous control strategy for target search with swarm robots

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2009年第3期1卷 151-163页

作者： Xue, Songdong Zhang, Jianhua Zeng, Jianchao Taiyuan Univ Sci & Technol Div Syst Simulat & Comp Applicat Taiyuan 030024 Peoples R China Lanzhou Univ Technol Coll Elect & Informat Engn Lanzhou 730050 Peoples R China

Upon mapping swarm robots search to particle swarm optimisation (PSO) and proposing concept of lime-varying character swarm (TVCS), the authors extend PSO to model swarm robotic system. Based on control principle of expected evolution position, an asynchronous communication policy is presented. Robot detects target signals in parallel to decide expected evolution position. The required time steps for completing the distance between two consecutive expected positions depend on kinematics constraints of robot. Meanwhile, robot evaluates positions it passes in every time step, updating its cognition as soon as when a better finding of itself has been found, updating shared information and broadcasting within TVCS if a better finding of swami appears. Either listening change of shared information or reaching the current expected position, robot starts to compute new expected position and turn out next control round. Simulation results indicate that the presented communication strategy has advantage over popular ones in search efficiency.

关键词： swarm robots target search particle swarm optimisation algorithm parallel asynchronous control communication strategy

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Dynamic modelling of a single-link flexible manipulator system: a particle swarm optimisation approach

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JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL 2007年第1期26卷 57-72页

作者： Alam, M. S. Tokhi, M. O. Univ Sheffield Dept Automat Control & Syst Engn Sheffield S1 3JD S Yorkshire England

This paper presents investigations into modelling of a single-link flexible manipulator system using the particle swarm optimisation (PSO) algorithm. PSO is a population-based search algorithm and is initialised with a population of random solutions, called particles. Basic PSO with best model can hardly provide suitable solutions in the case of real-world multimodal problems. In order to improve diversity in the population set and hence to improve the global searching capability a local version of PSO with time varying inertia and acceleration coefficients is proposed and used in this work. The effectiveness of the algorithm in modelling is validated and verified in terms of tracking, stability and the ability of the derived model in capturing a system's dynamics. The effect of swarm size on the convergence of the proposed PSO algorithm is also analysed. Time domain and frequency domain results of derived models clearly show the potential of the modelling technique and the proposed algorithm in solving such control problems.

关键词： particle swarm optimisation algorithm parametric model single-link flexible manipulator system

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