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Effective task scheduling for heterogeneous distributed systems using firefly algorithm

INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2015年第2期11卷 132-142页

作者： Eswari, R. Nickolas, S. Natl Inst Technol Dept Comp Applicat Tiruchirappalli 620015 Tamil Nadu India

Achieving minimum execution time for any application with better resource utilisation is a major challenge in heterogeneous distributed systems. But the performance can be exploited in these systems through proper scheduling of application tasks. An efficient meta-heuristic algorithm called firefly algorithm is applied in this paper to solve static task scheduling problem in heterogeneous systems. The social behaviour of fireflies is mimicked to generate optimal task schedule length. The efficiency of the firefly-based task scheduling algorithm is compared with the existing particle swarm optimisation-based scheduling algorithm. The experimental results show that the firefly algorithm-based approach gives better results when compared to PSO algorithm and performs well with minimum processors for effective scheduling of tasks.

关键词： task scheduling heterogeneous systems firefly algorithm particle swarm optimisation algorithm

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Simulation for Path Planning of SLOCUM Glider in Near-bottom Ocean Currents Using Heuristic algorithms and Q-Learning

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DEFENCE SCIENCE JOURNAL 2015年第3期65卷 220-225页

作者： Gautam, Utkarsh Ramanathan, Malmathanraj JSS Acad Tech Educ Dept Instrumentat & Control Engn Noida Uttar Pradesh India Natl Inst Technol Dept ECE Tiruchirappalli Tamil Nadu India

Addressing the need for exploration of benthic zones utilising autonomous underwater vehicles, this paper presents a simulation for an optimised path planning from the source node to the destination node of the autonomous underwater vehicle SLOCUM Glider in near-bottom ocean environment. Near-bottom ocean current data from the Bedford Institute of Oceanography, Canada, have been used for this simulation. A cost function is formulated to describe the dynamics of the autonomous underwater vehicle in near-bottom ocean currents. This cost function is then optimised using various biologically-inspired algorithms such as genetic algorithm, Ant Colony optimisation algorithm and particle swarm optimisation algorithm. The simulation of path planning is also performed using Q-learning technique and the results are compared with the biologically-inspired algorithms. The results clearly show that the Q-learning algorithm is better in computational complexity than the biologically-inspired algorithms. The ease of simulating the environment is also more in the case of Q-learning techniques. Hence this paper presents an effective path planning technique, which has been tested for the SLOCUM glider and it may be extended for use in any standard autonomous underwater vehicle.

关键词： Simulation path planning AUV SLOCUM Glider near-bottom ocean currents Q-learning genetic algorithm ant colony optimisation algorithm particle swarm optimisation algorithm

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The forecasting residual life of underground pipeline based on particle swarm optimisation algorithm

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2009年第4期1卷 270-275页

作者： Liu, Qin Ming Lv, Wenyuan Shanghai Univ Sci & Technol Coll Management Shanghai 200093 Peoples R China

Grey theory explores the use of the PSO algorithm and this paper analysed the feasibility of using this algorithm to forecast the residual life of underground pipeline. It is allowed to offer fewer data while using this method to forecast its residual life. Example shows that the method of the PSO algorithm based on grey theory optimisation is superior to the existing forecasting methods such as the grey forecasting method and grey theory based on the GA optimisation method.

关键词： underground pipeline residual life grey theory particle swarm optimisation algorithm PSO algorithm

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An improved particle swarm optimisation based on cellular automata

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INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2014年第1期5卷 94-106页

作者： Dai, Yuntao Liu, Liqiang Li, Ying Song, Jingyi Harbin Engn Univ Dept Sci 145 NanTong St Harbin Heilongjiang Peoples R China Harbin Engn Univ Dept Automat Harbin Heilongjiang Peoples R China Anhui Sun Create Elect Co Ltd Dept Image Proc New & High Technol Dev Dist Hefei Anhui Peoples R China Harbin Engn Univ Dept Sci Harbin Heilongjiang Peoples R China

particle swarm optimisation (PSO) algorithm is easy to fall into local optimum, so an improved PSO based on cellular automata is proposed by combining cellular automata (CA) with PSO. In the proposed CAPSO, each particle of particle swarm is considered as cellular automata, and is distributed in two-dimensional grid. The state update of each cell is not only related to its own state and the neighbour state, but also related with the state of the optimal cell. If the state is too close with the optimal cell, then the cell state is re-update. Simulation experiments on typical test functions show that, compared with other algorithms, the proposed algorithm has good robustness, strong local search ability and global optimisation ability, and can solve the optimisation problems effectively.

关键词： particle swarm optimisation algorithm cellular automata function optimisation

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Simultaneous Integrated stochastic electrical and thermal energy expansion planning

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IET GENERATION TRANSMISSION & DISTRIBUTION 2014年第6期8卷 1017-1027页

作者： Abbasi, Ali Reza Seifi, Ali Reza Shiraz Univ Sch Elect & Comp Engn Shiraz Iran

In this study, a stochastic multi-objective framework is proposed for energy expansion planning (EEP). The proposed multiobjective framework can concurrently optimise the competing objective functions including total real energy losses, voltage deviation and the total cost of the installation equipments. Also, regarding the uncertainties of the new complicated energy systems, in this study, for the first time, system uncertainties including load uncertainty are explicitly considered in the EEP problem by the use of the probabilistic load flow technique based on the point estimate method. Since the objectives are different and incommensurable, it is difficult to solve the problem by the conventional approaches that may optimise a single objective. Hence, the metaheuristic algorithm is applied to this problem. Here, the particle swarm optimisation (PSO) algorithm as a new evolutionary optimisation algorithm is utilised. To improve the total ability of the PSO for global search and exploration, a new modification adaptive process is suggested in such a way that the algorithm will search the total search space globally. To evaluate the feasibility and the effectiveness of the proposed algorithm, three modified standard distribution systems are used as the case studies.

关键词： load flow particle swarm optimisation power distribution planning thermal energy expansion planning integrated stochastic electrical energy expansion planning total real energy losses voltage deviation installation equipments. cost EEP problem probabilistic load flow technique metaheuristic algorithm particle swarm optimisation algorithm evolutionary optimisation algorithm modification adaptive process

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High-performance Brillouin distributed temperature sensor using Fourier wavelet regularised deconvolution algorithm

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IET OPTOELECTRONICS 2014年第6期8卷 203-209页

作者： Pradhan, Himansu Shekhar Sahu, Prasant Kumar Indian Inst Technol Bhubaneswar Sch Elect Sci Bhubaneswar 751013 Odisha India

In this study, the authors presented a distributed optical fibre temperature sensor whose performance is improved using the optimisation techniques and Fourier wavelet regularised deconvolution (ForWaRD) method. As the input power launched into the sensing fibre is critical, the authors have considered both conventional optimisation technique and evolutionary optimisation techniques namely: genetic algorithm, differential evolution algorithm and particle swarm optimisation algorithm to increase the stimulated Brillouin scattering (SBS) threshold power. Using the optimised value of the parameters and employing evolutionary computing techniques, the proposed 50 km long temperature sensing system offers a 4.7 dB improvement in SBS threshold power over the design of experiment based system. It is being verified that power of the backscattered signals approximately are the convolution of the input pulsed power and corresponding backscatter optical fibre impulse responses. The Fourier wavelet regularised deconvolution (ForWaRD) method is employed to improve the spatial resolution of the proposed sensing system without reducing the pulse width of the input pulse. Employing ForWaRD technique a 10 m better spatial resolution observed as compared with the Fourier deconvolution technique. The proposed 50 km long temperature sensing system exhibits a temperature resolution of 1.85 K because of suppression of SBS threshold and noise reduction.

关键词： fibre optic sensors temperature sensors stimulated Brillouin scattering Fourier transform optics wavelet transforms deconvolution genetic algorithms particle swarm optimisation distributed sensors optical information processing signal denoising high-performance Brillouin distributed temperature sensor Fourier wavelet regularised deconvolution algorithm optical fibre temperature sensor ForWaRD method evolutionary optimisation techniques genetic algorithm differential evolution algorithm particle swarm optimisation algorithm stimulated Brillouin scattering SBS threshold power input pulsed power convolution backscatter optical fibre impulse responses spatial resolution SBS threshold suppression noise reduction

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Optimal multiaxial sensor placement for modal identification of large structures

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STRUCTURAL CONTROL & HEALTH MONITORING 2014年第1期21卷 61-79页

作者： He, Longjun Lian, Jijian Ma, Bin Wang, Haijun Tianjin Univ State Key Lab Hydraul Engn Simulat & Safety Tianjin 300072 Peoples R China

Research on optimal sensor placement has become a very important topic because of the need to obtain effective testing results with limited testing resources in modal identification and structural health monitoring. An integer-encoding multi-swarm particle swarm optimisation (IMPSO) algorithm is proposed to place multiaxial sensors optimally on large structures for modal identification. The concepts of grade evaluation and migration strategy and the mutation operators of genetic algorithm are introduced into the integer-encoding particle swarm optimisation algorithm. Three different fitness functions for optimal multiaxial sensor placement (OMSP) are investigated. The second fitness function considers spatial correlation based on Moran's I to solve the information redundancy of multiaxial sensor placement, whereas the other two functions evolve from the existing methods for comparison with the second fitness function. The novel algorithm and three fitness functions are further applied to the Laxiwa arch dam for verifications. The results show that the proposed IMPSO outperforms two existing algorithms in its global optimisation capability. The results also prove that the second fitness function has advantages in sensor distribution and ensuring the well-conditioned information matrix and observability of multidimensional modal shapes. The multiaxial sensor placement scheme determined by the proposed method is applied to the modal test of the Laxiwa arch dam under simulative ambient excitation. The results show that the scheme determined by the second fitness function can identify the frequencies and multidimensional mode shapes accurately, indicating that this method may be used to provide guidance for OMSP in various types of large structures. Copyright (c) 2013 John Wiley & Sons, Ltd.

关键词： multiaxial sensor optimal placement particle swarm optimisation algorithm Moran's I spatial correlation large structure

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Optimal sizing of a grid integrated solar photovoltaic system

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IET RENEWABLE POWER GENERATION 2014年第1期8卷 67-75页

作者： Khare, Anula Rangnekar, Saroj Maulana Azad Natl Inst Technol Dept Energy Bhopal 462051 India

This study proposes an optimal sizing methodology for a solar photovoltaic (SPV) system considering lifetime cost requirements. The aim of the design is optimal sizing of SPV system, which is obtained by calculating SPV system output power at certain location, taking into account the calculated optimal number of SPV modules, optimal number of inverters, optimal tilt angle, for a given dimension of land. This design is aimed for minimising the annual cost of grid-integrated SPV system over its life or years of operation. The cost function takes into account the capital cost of installation, operation and maintenance, for each component of the system and the cost of selling energy to the grid. The sizing optimisation has been formulated as a non-linear, multi-variable problem and the particle swarm optimisation algorithm has been tested using MATLAB platform for a particular location to swot up the feasibility of integrated system. The monthly averaged daily and hourly solar radiation data for a given location is calculated using empirical relations on MATLAB platform. Other inputs are specifications of commercially available devices and meteorological details of location.

关键词： maintenance engineering particle swarm optimisation photovoltaic power systems power grids empirical relations MATLAB platform particle swarm optimisation algorithm multivariable problem nonlinear problem sizing optimisation maintenance cost function grid-integrated SPV system optimal sizing SPV system solar photovoltaic system optimal sizing methodology grid integrated solar photovoltaic system

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Environmental/economic dispatch incorporating renewable energy sources and plug-in vehicles

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IET GENERATION TRANSMISSION & DISTRIBUTION 2014年第12期8卷 2183-2198页

作者： Gholami, Amin Ansari, Javad Jamei, Mahdi Kazemi, Ahad Iran Univ Sci & Technol Sch Elect Engn Ctr Excellence Power Syst Automat & Operat Tehran Iran Florida Int Univ Elect & Comp Engn Dept Coll Engn & Comp Miami FL 33199 USA

Transportation and electricity industries are considered as major sources of greenhouse gases (GHGs) emission. Different methods have been proposed to deal with the increasing rate of the emission, such as employing plug-in electric vehicles (PEVs) and integrating renewable energy sources (RESs). However, it is important to scrutinise different scenarios of incorporating the mentioned elements to decrease the concerning emission rate while considering the economic constraints. In this study, a combined economic emission dispatch (CEED) is employed to investigate the effectiveness of using PEVs and RESs from different aspects. A sensitivity analysis is executed to survey the influence of emission and cost coefficients. Two test cases each including different scenarios are simulated to determine the efficacy of different types of integration in the proposed model. To have a more accurate and realistic survey, an extended model of the wind farm's cost function is employed in economic dispatch calculations. The particle swarm optimisation algorithm is applied to solve the CEED non-linear problem. The obtained results indicate that the integration of PEVs cannot necessarily reduce the net emission of two industries. In fact, the optimum solution should include the incorporation of PEVs along with RESs to return the desired results.

关键词： electric vehicles renewable energy sources air pollution control wind power plants power generation dispatch power generation economics particle swarm optimisation nonlinear programming renewable energy source transportation electricity industry greenhouse gas emission GHG emission PEV plug-in electric vehicle RES combined economic emission dispatch CEED environmental-economic dispatch sensitivity analysis wind farm particle swarm optimisation algorithm nonlinear problem

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Study on evaluation of perpendicularity errors with an improved particle swarm optimisation for planar lines

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INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2013年第1期18卷 54-60页

作者： Zhang, Ke Wang, Shengze Shanghai Inst Technol Sch Mech Engn 100 Haiquan Rd Shanghai Peoples R China Donghua Univ Coll Mech Engn Shanghai Peoples R China

According to characteristics of perpendicularity error evaluation of planar lines, an improved particle swarm optimisation (PSO) is proposed to evaluate the minimum zone error. The evolutional optimum model and the calculation process are introduced in detail. Compared with conventional optimum methods such as simplex search and Powell method, PSO can find the global optimal solution, and the precision of calculating result is very good. Compared to other intelligence computation algorithms such as genetic algorithm (GA), PSO is easier to carry out with fewer parameters to adjust. Then, the objective function calculation approaches for using the particle swarm optimisation algorithm to evaluate minimum zone error are formulated. Finally, the control experiment results evaluated by different method such as the least square, simplex search, Powell optimum methods and genetic algorithm (GA), indicate that the proposed method can provide better accuracy on perpendicularity error evaluation effectively, and is well suited for position error evaluation.

关键词： metrology perpendicularity error minimum zone solution particle swarm optimisation algorithm

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