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检索条件"主题词=bacterial foraging algorithm"

共 103 条记录，以下是81-90 订阅

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Parallel Image Registration using Bio-inspired Computing

Parallel Image Registration using Bio-inspired Computing

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4th IEEE International Conference on E-Health and Bioengineering (EHB)

作者： Bejinariu, Silviu-Ioan Rotaru, Florin Nita, Cristina Luca, Ramona Costin, Hariton Romanian Acad Iasi Branch Inst Comp Sci Iasi Romania Gr T Poja Univ Med & Pharm Fac Med Bioengn Iasi Romania Romanian Acad Inst Comp Sci Iasi Romania

ISBN: (纸本)9781479923724

In this paper it is proposed a parallel approach for the pixel intensity based image registration (IR) problem on multi-core processors. While IR is an optimization problem which computes the optimal parameters for a geometric transform, two classes of bio-inspired algorithms are studied: bacterial foraging Optimization algorithm (BFOA) and Genetic algorithm (GA). The optimal transform is applied to a source image in order to align it to a model image by maximizing a similarity measure. In the presented experiment, mutual information (MI) is used to evaluate the IR quality and most of the processing time is spent in this evaluation. The proposed parallel approach aims to reduce the processing time by using the full computing power of multi-core processors. A comparison of the sequential and parallel versions for different registration problems is presented.

关键词： bacterial foraging algorithm genetic algorithm image registration optimization parallel computing

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An Optimal Scheduling of Smart Home Appliances using Heuristic Techniques with Real-time Coordination 1

An Optimal Scheduling of Smart Home Appliances using Heurist...

引用

1st IEEE International Conference on Power, Energy and Smart Grid (ICPESG)

作者： Khalid, Muhammad Usman Javaid, Nadeem COMSATS Inst Informat Technol Islamabad 44000 Pakistan

ISBN: (纸本)9781538654828

With the development of smart grids (SG), it has become possible to schedule the smart appliances effectively in smart homes. The energy consumption pattern of smart appliances modified by home energy management system (HEMS) deployed in smart home. In this paper, two metaheuristic hybrid optimization techniques presented to program the schedule of household appliances. The proposed schemes are hybrid of tabu search (TS) and bacterial foraging algorithm (BFA) named as hybrid bacterial foraging tabu search algorithm (HBT) and the hybrid of TS and bat algorithm (BA) named as hybrid bat tabu search algorithm (HBAT). The aim is to reduce expenses of the customer while maintaining user comfort in terms of waiting time based on varying price signal. This paper describes two types of scheduling: first appliance scheduling using metaheuristic optimization techniques and second appliance rescheduling using dynamic programming (DP). Metaheuristic optimization techniques move the appliance from high price peak to low price peak minimizing consumer energy bill and peak to average ratio (PAR). DP used to handle the real-time user interrupts to turn ON any appliance. Appliance rescheduling formulated as knapsack problem, which divides a problem into subproblems to achieve an optimal solution. A coordination concept among appliances has been proposed for real-time appliance rescheduling. The simulation results validated the effectiveness of proposed hybrid techniques.

关键词： bacterial foraging algorithm Tabu Search Bat algorithm Dynamic Programming Smart Grid

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Hysteretic Damping System Parameter Identification Based on HABC-CS algorithm 13

Hysteretic Damping System Parameter Identification Based on ...

引用

IEEE International Conference on Mechatronics and Automation

作者： Kang Weixin Ye Youdao Zhao Tianwei Wagan, Raja Asif Harbin Engn Univ Dept Informat & Commun Engn Harbin 150001 Peoples R China

ISBN: (纸本)9781509023967

Least squares estimate method in parameter identification of hysteretic damping system on the application of ubiquitous computing efficiency is not high, or complicated calculation, calculation accuracy is not high, there is always the problem such as error or error approximation ability is limited. Similar problem happened when inversion analysis of the defect is done in a component torsion wave propagation model, how to get these related parameters quickly and precisely with a good inversion algorithm is the main work we want to do. In this paper, an intelligent algorithm called HABC - CS algorithm is proposed. By introducing chaos search and bacterial foraging algorithm, adaptive probability of Boltzman, strengthen the ability of bacteria foraging behaviour, to speed up the global convergence of bees, improve the accuracy of solution to solve the existed problems of the least squares estimation method. Through the function test platform, the reliability of the HABC - CS algorithm is verified. And through the viscous damping system as an example, the Runge-Kutta algorithm is used to get the simulation data, and then the proposed algorithm is applied to identifying the parameters of hysteretic damping system. At last, the detailed process of parameter identification and the identification result of parameter identification of hysteretic damping system is given in this article.

关键词： parameter identification chaotic searching bacterial foraging algorithm adaptive probability Runge-Kutta algorithm

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Image Enhancement using Hybrid Intelligent Optimization

Image Enhancement using Hybrid Intelligent Optimization

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4th International Conference on Intelligent Systems Design and Engineering Applications (ISDEA)

作者： Lei Xuanhua Hu Qingping Kong Xiaojian Xiong Tianlin Naval Univ Engn Wuhan 430033 Hubei Peoples R China

ISBN: (纸本)9781479927913

Contrast enhancement plays an important role in image processing system, which is used to improve image quality or extract the fine details in degraded images. Image enhancement was regarded as an optimization problem and a kind of hybrid intelligent algorithm was proposed in this paper to optimize parameters of image enhancement operator who took the advantage of local gray distribution and the global statistical information of source image. Advantages of bacterial foraging algorithm and particle swarm optimization were combined into the Hybrid intelligent algorithm proposed in this paper, and the optimized fitness function was based on entropy and edge information of image. The results of simulation and experiment showed that after the application of this method not only the overall image contrast was enhanced but also details information of target image was effectively enriched, and noise amplification was restrained.

关键词： bacterial foraging algorithm Particle Swarm Optimization Intelligent optimization Image enhancement

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Bio inspired BFA optimization for Inductor less Voltage gain Converter

Bio inspired BFA optimization for Inductor less Voltage gain...

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International Conference on Energy Efficient Technologies for Sustainability (ICEETS)

作者： Sivakumar, A. Premalatha, L. VIT Univ Sch Elect Engn Madras 127 Tamil Nadu India

ISBN: (纸本)9781467399258

The proposed research work deals with the execution of BFOA control for switched capacitor converter (SCC) with closed loop performance analysis. The above-culled converter is incipiently developed novel DC to DC converter with voltage hoist technique. The dynamic behavioural characteristics of power electronic converters is exceedingly nonlinear due to the nature of switching operation and unstable nature of time and, hence BFOA predicated bio-inspired controller is introduced for the developed novel converter[Intelligent swarm optimization network was developed for the current research work]. The closed loop performance investigation of the above converter utilizing software simulation (MATLAB) and proto type implementation with FPGA was validated. For the conditions like supply perturbances and load transmutes the performance of the converter is found to be preponderant.

关键词： Switched Capacitor converter MATLAB Direct Current bacterial foraging algorithm Field Programmable Gate Array

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Optimal Planning of Wind and PV Capacity in Provincial Power Systems Based on Two-Stage Optimization algorithm 5

Optimal Planning of Wind and PV Capacity in Provincial Power...

引用

5th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT)

作者： Yuan, Yue Cao, Yang Zhou, Jingjing Bao, Jiangmin Hohai Univ Coll Energy & Elect Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781467371063

With the rapid development of wind power and photovoltaic (PV) power industry, the curtailment of wind power or PV power in 'Three North' areas is serious, due to their short planning and construction time period, as well as being disjoined with the regional generation and grid plan. A novel formulation based on two-stage optimization under low-carbon economy is proposed in present paper to optimize the proportion of wind and PV capacity for provincial power systems, in which, carbon emissions of generator units and features of renewable resources are all taken into account. In the lower-level formulation, a time sequence production simulation (TSPS) model that is suitable for actual power system has been adopted. In order to maximize benefits of energy-saving and emissions reduction resulted from renewable power generation, General Algebraic Modeling System (GAMS), a commercial software, is employed to optimize the annual operation of the power system. In the upper-level formulation, a hybrid bacterial foraging algorithm and particle swarm optimization (BFAPSO) algorithm is utilized to optimize the proportion of wind and PV capacity. The objective of the upper level formulation is to maximize benefits of energy conservation and carbon emissions reductions optimized in the lower-level problem. Simulation results in practical provincial power systems validate the proposed model and corresponding solving algorithms. The optimization results can provide support to policy makers to make renewable energy related policies.

关键词： time-sequence production simulation optimal proportion of renewable energy particle swarm optimization bacterial foraging algorithm

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BFO and GA based Optimization of Illumination Switching Patterns in Large Establishments 6

BFO and GA based Optimization of Illumination Switching Patt...

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6th International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Karlupia, Namrata Sambyal, Priyanka Abrol, Pawanesh Lehana, Parveen Univ Jammu Dept CS&IT Jammu India

ISBN: (纸本)9789380544342

Automatic appliances revamped the conventional electric systems by providing ease to complexity in activities of day today life. These appliances are smart, besides being energy efficient. Inefficient or poor illumination results in wastage of financial resources and also creates unsafe conditions. Therefore, in order to minimize the electricity consumption, smart technology has to be implemented for lighting system. In this paper, remotely operated hardware system is designed along with a suitable interface via ESP8266 (Wi-Fi Wemos) for generating illumination patterns. Further, optimization of lighting system is performed by generating different light patterns. The effectiveness and efficiency of two nature inspired techniques viz. bacterial foraging optimization (BFO) and genetic algorithm (GA) are compared on the basis of different parameters like convergence rate, computational time, population size etc. Results obtained indicate that GA generates optimized patterns of illumination in less time and the energy saved by it is nearly 36% which is fairly efficient.

关键词： ATmega328 bacterial foraging algorithm Embedded system Genetic algorithm Illumination system Wi-Fi Wemos

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Multi-objective Optimization Using BFO algorithm

引用

7th International Conference on Intelligent Computing (ICIC)

作者： Niu, Ben Wang, Hong Tan, Lijing Xu, Jun Chinese Acad Sci Hefei Inst Intelligent Machines Hefei 230031 Peoples R China Shenzhen Univ Coll Management Shenzhen 518060 Peoples R China Jinan Univ Sch Management Guangzhou 510632 Peoples R China Univ Hongkong Business Technol Inst Hongkong Peoples R China

ISBN: (纸本)9783642245527;9783642245534

This paper describes a novel bacterial foraging optimization (BFO) approach to multi-objective optimization, called Multi-objective bacterial foraging Optimization (MBFO). The search for Pareto optimal set of multi-objective optimization problems is implemented. Compared with the proposed algorithm MOPSO and NSGAII, simulation results (measured by Diversity and Generational Distance metric) on test problems show that the proposed MBFO is able to find a much better spread of solutions and faster convergence to the true Pareto-optimal front. It suggests that the proposed MBFO is very promising in dealing with multi-objective optimization problems.

关键词： bacterial foraging algorithm multi-objective optimization pareto optimal

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Bio-Inspired Node Localization in Wireless Sensor Networks

Bio-Inspired Node Localization in Wireless Sensor Networks

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Kulkarni, Raghavendra V. Venayagamoorthy, Ganesh K. Cheng, Maggie X. Southeast Missouri State Univ Real Time Power & Intelligent Syst Lab Rolla MO 65409 USA Missouri Univ Sci & Technol Dept Comp Sci Rolla MO USA

ISBN: (纸本)9781424427932

Many applications of wireless sensor networks (WSNs) require location information of the randomly deployed nodes. A common solution to the localization problem is to deploy a few special beacon nodes having location awareness, which help the ordinary nodes to localize. In this approach, non-beacon nodes estimate their locations using noisy distance measurements from three or more non-collinear beacons they can receive signals from. In this paper, the ranging-based localization task is formulated as a multidimensional optimization problem, and addressed using bio-inspired algorithms, exploiting their quick convergence to quality solutions. An investigation on distributed iterative localization is presented in this paper. Here, the nodes that get localized in an iteration act as references for remaining nodes to localize. The problem has been addressed using particle swarm optimization (PSO) and bacterial foraging algorithm (BFA). A comparison of the performances of PSO and BFA in terms of the number of nodes localized, localization accuracy and computation time is presented.

关键词： bacterial foraging algorithm localization particle swarm optimization wireless sensor networks

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Economic Scheduling in Smart Grid with Nondispatchable and Dispatchable loads

Economic Scheduling in Smart Grid with Nondispatchable and D...

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6th IEEE Conference on Cybernetics and Intelligent Systems (CIS)

作者： Ramachandran, Bhuvaneswari Cartes, David A. Univ W Florida Dept Elect & Comp Engn Pensacola FL 32514 USA Florida State Univ Dept Mech Engn Tallahassee FL 32310 USA

ISBN: (纸本)9781479910731

A smart grid is expected to be more flexible and more reliable than traditional electric power grids. Smart grid technology combines, photovoltaic systems, wind turbines, fuel cells, energy storages and electric vehicles charging from the grid and discharging back to the grid. Electric vehicles are one of the key pieces of the renewable energy economy of the future, but they do come with a few challenges-charging electric vehicles can add considerably to a home's overall electricity use, and when scaled up to thousands or millions of homes - that charging places a lot of extra demand on an electrical grid. This paper presents an economical insight into the management of renewable energy generation and charging/discharging of electric vehicles. This analysis will make use of an approach that will not only reduce the operational cost of renewables and capital cost of energy storages, it will also save cost for electric vehicle owners.

关键词： Economic load dispatch Electric vehicles Smart grid Charging strategies bacterial foraging algorithm

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