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检索条件"主题词=Firefly algorithm"

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firefly algorithm for Reliable Protection in Distribution Networks

Firefly Algorithm for Reliable Protection in Distribution Ne...

引用

International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT)

作者： Anand, Rimmi Kumar, Vijay Thapar Univ Comp Sci & Engn Dept Patiala Punjab India

ISBN: (纸本)9781509061068

The aim of this paper is to propose a model for reliable Distribution centers (DCs) in case of unexpected disruption in DCs. Also, random disruptions between links in a distribution network system. The mixed-integer linear programming model (MILP) is formulated that aims to provide reliable DCs in case of random failures. The site-dependent failure probabilities, three investment levels for opening unreliable facility has been considered. The IBM CPLEX 12.6.3 solver has been used to implement recently developed metaheuristic firefly algorithm on the proposed model. Numerical results are presented on basis of random generated examples. The firefly algorithm has outperformed CPLEX on large instances up to 200 customers and 30 DCs.

关键词： Supply chain network design Distribution network Facility location Reliability Disruption firefly algorithm

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firefly algorithm Enhanced by Orthogonal Learning 7th

Firefly Algorithm Enhanced by Orthogonal Learning

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7th Computer Science On-Line Conference (CSOC)

作者： Kadavy, Tomas Pluhacek, Michal Viktorin, Adam Senkerik, Roman Tomas Bata Univ Zlin TG Masaryka 5555 Zlin 76001 Czech Republic

ISBN: (纸本)9783319911892

Orthogonal learning strategy, a proven technique, is combined with hybrid optimization metaheuristic, which is based on firefly algorithm and Particle Swarm Optimization. The hybrid algorithm firefly Particle Swarm Optimization is then compared, together with canonical firefly algorithm, with the newly created Orthogonal Learning firefly algorithm. Comparisons have been conducted on five selected basic benchmark functions, and the results have been evaluated for statistical significance using Wilcoxon rank-sum test.

关键词： firefly algorithm Particle Swarm Optimization Orthogonal learning

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firefly algorithm Based Multilingual Named Entity Recognition for Indian Languages 2nd

Firefly Algorithm Based Multilingual Named Entity Recognitio...

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2nd International Conference on Advanced Informatics for Computing Research (ICAICR)

作者： Biswas, Sitanath Dash, Sujata Acharya, Sweta North Orissa Univ Baripada Odisha India Centurian Univ Balangir Odisha India

ISBN: (纸本)9789811331404;9789811331398

Named Entity Recognition (NER) is considered as a very influential undertaking in natural language processing appropriate to Question Answering system, Machine Translation (MT), Information extraction (IE), Information Retrieval (IR) etc. Basically NER is to identify and classify different types of proper nouns present inside given filelike location name, person name, number, organization name, time etc. Multilingual NER is a task where NE can be recognized for variety of Languages by implementing one or more methods. In this paper, we have implemented Conditional Random Field (CRF) as a base and firefly algorithm (FA) to effectively combine different feature representation. For better performance of this system, we have combined both the methods. We have taken three Indian languages Hindi, Bengali, and Odiya for the purpose of evaluation. A promising result is observed for all three languages while implementing FA with CRF.

关键词： firefly algorithm Conditional Random Field Named Entity Recognition Recognition Information extraction Machine Translation Information Retrieval Multilingual NER NER

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firefly algorithm-based nonlinear MPC trajectory planner for autonomous driving

Firefly algorithm-based nonlinear MPC trajectory planner for...

引用

International Conference of Electrical and Electronic Technologies for Automotive

作者： Inghilterra, Giuseppe Arrigoni, Stefano Braghin, Francesco Cheli, Federico Politecn Milan Fac Mech Engn Milan Italy

ISBN: (纸本)9788887237382

The automotive ecosystem is experiencing a technological leap forward. The science and technology progress are converging to a new disruptive scenario, where the autonomous driving is the leading player. An autonomous vehicle can be described as a dynamical system, that manages its own state by performing a sense-plan-act loop. In this paper a hierarchical structure of the planning module is presented, with a specific focusing on the motion planning layer. This latter is based on a Model Predictive Control (MPC) strategy, where the optimization process is carried out by exploiting a firefly-algorithm (FA). This approach is able to make purposeful decisions in order to guide the vehicle towards the designed goal in an urban environment, within the imposed constraints (e.g. vehicle dynamics, road boundaries, obstacles avoidance, etc). The optimal control problem formulation is provided as well as the mathematical model of the vehicle. Simulation results are obtained to assess the controller performance and to validate the feasibility and the safety of the generated trajectories.

关键词： MPC firefly algorithm FA optimization autonomous driving obstacle avoidance motion planning

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firefly algorithm Based Clustering Technique for Wireless Sensor Networks

Firefly Algorithm Based Clustering Technique for Wireless Se...

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IEEE International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)

作者： Manshahia, Mukhdeep Singh Dave, Mayank Singh, S. B. Punjabi Univ Dept Math Patiala Punjab India NIT Dept Comp Engn Kurukshetra Haryana India

ISBN: (纸本)9781467393379

Clustering of nodes in Wireless Sensor Networks is a problem of concern for many researchers. The major challenge is to propose an algorithm which can optimize the values of various performance parameters like packet delivery ratio and network lifetime of a node in the network. This paper shows an implementation of firefly algorithm to perform clustering in wireless sensor networks. Simulation results have shown the improvement in the performance parameters. The network lifetime of a node is improved as the energy exhaustion is reduced while transferring data in the network.

关键词： Clustering firefly algorithm Wireless Sensor Networks

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firefly algorithm for Demand Estimation of Water Resources 24th

Firefly Algorithm for Demand Estimation of Water Resources

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24th International Conference on Neural Information Processing (ICONIP)

作者： Wang, Hui Cui, Zhihua Wang, Wenjun Zhou, Xinyu Zhao, Jia Lv, Li Sun, Hui Nanchang Inst Technol Jiangxi Prov Key Lab Water Informat Cooperat Sens Nanchang 330099 Jiangxi Peoples R China Nanchang Inst Technol Sch Informat Engn Nanchang 330099 Jiangxi Peoples R China Taiyuan Univ Sci & Technol Complex Syst & Computat Intelligence Lab Taiyuan 030024 Peoples R China Nanchang Inst Technol Sch Business Adm Nanchang 330099 Jiangxi Peoples R China Jiangxi Normal Univ Coll Comp & Informat Engn Nanchang 330022 Jiangxi Peoples R China

ISBN: (纸本)9783319700939;9783319700922

firefly algorithm (FA) is an efficient swarm intelligence optimization technique, which has been used to solve many engineering optimization problems. In this paper, we present a new FA (called NFA) variant for demand estimation of water resources in Nanchang city of China. The performance of the standard FA highly depends on its control parameters. To tackle this issue, a dynamic step factor strategy is proposed. In NFA, the step factor is not fixed and it is dynamically updated during the search process. Three models in different forms (linear, exponential and hybrid) are developed based on the structure of social and economic conditions. Water demand in Nanchang city from 2003 to 2015 is considered as a case study. The data from 2003 to 2012 is used for finding the optimal weights, and the rest data (2013-2015) is for testing the models. Simulation results show that three FA variants can achieve promising performance. Our proposed NFA outperforms the standard FA and memetic FA (MFA), and the prediction accuracy is up to 97.91%.

关键词： firefly algorithm Swarm intelligence Water demand estimation Water demand forecasting Optimization

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firefly algorithm to Solve a Project Scheduling Problem 5th

Firefly Algorithm to Solve a Project Scheduling Problem

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5th Computer Science On-line Conference (CSOC)

作者： Crawford, Broderick Soto, Ricardo Johnson, Franklin Valencia, Carlos Paredes, Fernando Pontificia Univ Catolica Valparaiso Valparaiso Chile Univ Cient Sur Lima Peru Univ San Sebastian Santiago Chile Univ Autonoma Chile Santiago Chile Univ Cent Chile Santiago Chile Univ Playa Ancha Valparaiso Chile Univ Diego Portales Escuela Ingn Ind Santiago Chile

ISBN: (纸本)9783319336251;9783319336237

This paper describes the Software Project Scheduling Problem (SPSP) as a combinatorial optimization problem. In this problem raises the need for a process to assign a set of resources to tasks for a project in a given time, trying to decrease the duration and cost. The workers are the main resource in the project. We present the design of the resolution model to solve the SPSP using an algorithm of fireflies (firefly algorithm, FA). We illustrate the experimental results in order to demonstrate the viability and soundness of our approach.

关键词： firefly algorithm Metaheuristic Software project scheduling problem Project management

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firefly algorithm Optimized Particle Filter for Relative Navigation of Non-cooperative Target 8th

Firefly Algorithm Optimized Particle Filter for Relative Nav...

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8th International Conference on Swarm Intelligence (ICSI)

作者： Zhang, Dali Zhong, Chao Wang, Changhong Guan, Haowei Xia, Hongwei Harbin Inst Technol Harbin 150001 Heilongjiang Peoples R China Shanghai Inst Spaceflight Control Technol Shanghai 201109 Peoples R China Shanghai Inst Satellite Engn Shanghai 201109 Peoples R China

ISBN: (纸本)9783319618241;9783319618234

Particle filter (PF) has been proved to be an effective tool in solving relative navigation problems. However, the sample impoverishment problem caused by resampling is the main disadvantage of PF, which strongly affect the accuracy of navigation. To solve this problem, an improved PF based on firefly algorithm (FA) is proposed. Combine with the operation mechanism of PF, the optimization mode of FA is revised, and a new update formula of attractiveness is designed. By means of firefly group's mechanism of survival of the fittest and individual firefly's attraction and movement behaviors, this algorithm enables the particles to move toward the high likelihood region. Thus, the number of meaningful particles can be increased, and the particles can approximate the true state of the target more accurately. Simulation results show that the improved algorithm improves the navigation accuracy and reduces the quantity of the particles required by the prediction of state value.

关键词： firefly algorithm Particle filter Relative navigation

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firefly algorithm for Feature Selection in Sentiment Analysis 3rd

引用

3rd International Conference on Computational Intelligence in Data Mining (ICCIDM)

作者： Kumar, Akshi Khorwal, Renu Delhi Technol Univ Dept Comp Sci & Engn Delhi India

ISBN: (纸本)9789811038747;9789811038730

Selecting and extracting feature is a vital step in sentiment analysis. The statistical techniques of feature selection like document frequency thresholding produce sub-optimal feature subset because of the non-polynomial (NP)-hard character of the problem. Swarm intelligence algorithms are used extensively in optimization problems. Swarm optimization renders feature subset selection by improving the classification accuracy and reducing the computational complexity and feature set size. In this work, we propose firefly algorithm for feature subset selection optimization. SVM classifier is used for the classification task. Four different datasets are used for the classification of which two are in Hindi and two in English. The proposed method is compared with feature selection using genetic algorithm. This method, therefore, is successful in optimizing the feature set and improving the performance of the system in terms of accuracy.

关键词： Sentiment analysis Feature selection Swarm intelligence firefly algorithm Genetic algorithm Support vector machine (SVM)

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firefly algorithm based-Pitch Angle Control of a Tidal Stream Generator for Power Limitation Mode 13

Firefly Algorithm based-Pitch Angle Control of a Tidal Strea...

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International Conference on Advanced Systems and Electric Technologies (IC_ASET)

作者： Ghefiri, Khaoula Bouallegue, Soufiene Haggege, Joseph Garrido, Izaskun Garrido, Aitor J. Univ Tunis El Manar Natl Engn Sch Tunis ENIT Lab Res Automat Control LARA BP 37 Tunis 1002 Tunisia Univ Basque Country UPV EHU Dept Automat Control & Syst Engn ACG Bilbao 48012 Spain

ISBN: (纸本)9781538644492

This paper deals with the modeling and control of a Tidal Stream Generator (TSG) for marine renewable energy. The Power Take Off system consists of a Tidal Stream Turbine coupled to a Doubly Fed Induction Generator with a 1.5 MW power. At any tidal site, there is a variation in the tidal current speed with the occurring of maximum velocities. This means that the TSG system needs to be controlled in order to limit the generated power and shedding mechanical load at high current speeds. For this purpose, a Proportional Integral (PI) controller for the blade pitch angle is investigated. A firefly algorithm based-metaheuristic theory is used to tune the gains of the PI. Simulation results show the performance of the implemented control strategy by optimizing the generated power in case of strong flow speeds.

关键词： Doubly Fed Induction Generator Energy harvesting firefly algorithm Marine energy Pitch angle control Tidal Stream Generator

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