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检索条件"主题词=firefly algorithm"

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Inference of Gene Regulatory Network based on Legendre Neural Network 8

Inference of Gene Regulatory Network based on Legendre Neura...

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8th International Conference on Information Technology in Medicine and Education (ITME)

作者： Yang, Bin Liu, Sanrong Zaozhuang Univ Sch Informat Sci & Engn Zaozhuang Peoples R China

ISBN: (纸本)9781509039067

Inference of gene regulatory network based on gene expression data is one of the biggest challenges in system biology. In this paper, Legendre neural network (LNN) is proposed to infer gene regulatory network (GRN). firefly algorithm (FA) is used to optimize the parameters of LNN. E. coli dataset from DREAM5 challenge is used to test the performance of LNN. The results reveal that our method performs better than popular inferred methods.

关键词： gene regulatory network Legendre neural network firefly algorithm

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Comparison of two optimization algorithms for fuzzy finite element model updating for damage detection in a wind turbine blade 12

Comparison of two optimization algorithms for fuzzy finite e...

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Conference on Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XII

作者： Turnbull, Heather Omenzetter, Piotr Univ Aberdeen Ctr Safety & Reliabil Engn Lloyds Register Fdn Aberdeen Scotland

ISBN: (数字)9781510616950

ISBN: (纸本)9781510616950

Difficulties associated with current health monitoring and inspection practices combined with harsh, often remote, operational environments of wind turbines highlight the requirement for a non-destructive evaluation system capable of remotely monitoring the current structural state of turbine blades. This research adopted a physics based structural health monitoring methodology through calibration of a finite element model using inverse techniques. A 2.36m blade from a 5kW turbine was used as an experimental specimen, with operational modal analysis techniques utilised to realize the modal properties of the system. Modelling the experimental responses as fuzzy numbers using the sub-level technique, uncertainty in the response parameters was propagated back through the model and into the updating parameters. Initially, experimental responses of the blade were obtained, with a numerical model of the blade created and updated. Deterministic updating was carried out through formulation and minimisation of a deterministic objective function using both firefly algorithm and virus optimisation algorithm. Uncertainty in experimental responses were modelled using triangular membership functions, allowing membership functions of updating parameters (Young's modulus and shear modulus) to be obtained. firefly algorithm and virus optimisation algorithm were again utilised, however, this time in the solution of fuzzy objective functions. This enabled uncertainty associated with updating parameters to be quantified. Varying damage location and severity was simulated experimentally through addition of small masses to the structure intended to cause a structural alteration. A damaged model was created, modelling four variable magnitude nonstructural masses at predefined points and updated to provide a deterministic damage prediction and information in relation to the parameters uncertainty via fuzzy updating.

关键词： structural health monitoring damage detection wind turbine blade fuzzy finite element model updating uncertainty quantification virus optimisation algorithm firefly algorithm

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Enriching the Multi-Object Detection using Convolutional Neural Network in Macro-Image

Enriching the Multi-Object Detection using Convolutional Neu...

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11th International Conference of Computer Communication and Informatics (ICCCI)

作者： Kuppusamy, P. Hung, Che-Lun Madanapalle Inst Technol & Sci Dept CSE Madanapalle Andhra Pradesh India Providence Univ Dept CS & Comm Engg Taichung Taiwan

ISBN: (纸本)9781728158754

An object recognition and localization is a primary issue that is harder than a classification of an image even with precise object location and their annotations available at the time of training. The feature is identified for localizing the objects and classification identifies the classes from recognized object regions. This research work is proposed the two approaches i. Support Vector Machine (SVM) is optimized using the firefly algorithm (FA), and Scale Invariant Feature Transform (SIFT) descriptors, ii. Convolutional Neural Network (CNN) with Adam (Adaptive Moment) optimizer. In first method, FA has been used in the searching of optimal parameters through the simulation of the social behavior of the fireflies using the bioluminescent i.e. emission of light intensity. This FA is trained the Lagrangian multiplier and smoothness parameters in the SVM continuously. The second research work Adam based CNN has recognized the objects in multi-object images. The hash directory is proposed to store the highest scored bounding boxes to speed up the process. The experiments have been evaluated with binary as well as multi-object images. The proposed model has been trained and validated using VOC2012 dataset. It consists the kind of general macro photography images for object identification. The Average Precision (AP) is computed, and comparison shows that CNN performs better than FA-SVM method.

关键词： Object localization Scale invariant feature transform Support vector machine firefly algorithm CNN Adam

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Optimization of variable stiffness composites in Automated Fiber Placement Process Using Evolutionary algorithms 1st

Optimization of variable stiffness composites in Automated F...

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1st CIRP Conference on Composite Materials Parts Manufacturing (CIRP CCMPM)

作者： Mejlej, Vahid Ghaffari Falkenberg, Paul Tuerck, Eiko Vietor, Thomas Tech Univ Carolo Wilhelmina Braunschweig Inst Konstruktionstechn Hermann Blenk Str 42 D-38108 Braunschweig Germany

Stiffness variety in composite parts is performed by local improvement in directions which are more favorable to carry loads in Automated Fiber Placement (AFP) technology. An approach to find the optimum position and the best length of layup dropping in AFP technology is introduced. Since, the objectives are minimum weight and maximum stiffness, problem is considered as a multi-objective optimization. Fiber failure, matrix cracking, and onset of delamination take into account as the constraints for objective function. A comparative study is introduced to evaluate the performance of Genetic algorithm and firefly algorithm in finding the global optimum result. (C) 2017 The Authors. Published by Elsevier B.V.

关键词： Optimization Variable stiffness Composite AFP process Genetic algorithm firefly algorithm

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Meta-heuristic approaches to tackle Skill Based Group allocation of Students in Project Based Learning Courses

Meta-heuristic approaches to tackle Skill Based Group alloca...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Nand, Ravneil Sharma, Anuraganand Univ South Pacific Sch Comp Informat & Math Sci Suva Fiji

ISBN: (纸本)9781728121536

In the arena of software engineering, Project Based Learning (PBL) is one of the fundamental components of practical based assessment. PBL involves team formation where necessary skills are needed to execute the project. Traditionally, the teams were randomly allocated based on individual preferences. To cab on this issue, preference based model needs few refinements such as skills needs to be identified by the facilitator while the students provide the necessary skill data. This way, students get assigned based on their skill rather than just random allocation. In a worst case scenario for random allocation, a team can end up with a very strong team having high skills or vice versa where a team has all of its members with limited skill or few skills are missing. The group created by skill preference would allow each group to more or less have the same strength and nearly all skills would be present in a group. In this paper, a method is extended from its original to cater for other state-of-the-art optimization techniques rather than just genetic algorithm to find a method that can suit small or large dataset. The objective function takes into account the differences between the total skill set of each group with the average total skill set needed for each group and the missing skill penalty of each group is added. Missing skill penalty is incurred due to not satisfying all the constraints such as non-presence of all the skills in a group. The skill rating allows better selection of members in a software engineering course. The results discussed in this paper are from 5 courses of one university.

关键词： Project Based Learning particle swarm optimization genetic algorithm skill based invasive weed optimization firefly algorithm

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An AI-Based Hybrid Forecasting Model for Wind Speed Forecasting 24th

An AI-Based Hybrid Forecasting Model for Wind Speed Forecast...

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24th International Conference on Neural Information Processing (ICONIP)

作者： Lu, Haiyan Heng, Jiani Wang, Chen Univ Technol Sydney 15 Broadway Sydney NSW 2007 Australia Dongbei Univ Finance & Econ Dalian Peoples R China Lanzhou Univ Lanzhou Peoples R China

ISBN: (纸本)9783319700939;9783319700922

Forecasting of wind speed plays an important role in wind power prediction for management of wind energy. Due to intermittent nature of wind, accurately forecasting of wind speed has been a long standing research challenge. Artificial neural networks (ANNs) is one of promising approaches to predict wind speed. However, since the results of ANN-based models are strongly dependent on the initial weights and thresholds values which are usually randomly generated, the stability of forecasting results is not always satisfactory. This paper presents a new hybrid model for short term forecasting of wind speed with high accuracy and strong stability by optimizing the parameters in a generalized regression neural network (GRNN) using a multi-objective firefly algorithm (MOFA). To evaluate the effectiveness of this hybrid algorithm, we apply it for short-term forecasting of wind speed from four wind power stations in Penglai, China, along with four typical ANN-based models, which are back propagation neural network (BPNN), radical basis function neural network (RBFNN), wavelet neural network (WNN) and GRNN. The comparison results clearly show that this hybrid model can significantly reduce the impact of randomness of initialization on the forecasting results and achieve good accuracy and stability.

关键词： Wind speed forecasting Artificial neural network firefly algorithm

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Stepping ahead based hybridization of meta-heuristic model for solving Global Optimization Problems

Stepping ahead based hybridization of meta-heuristic model f...

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IEEE Congress on Evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Nand, Ravneil Chaudhary, Kaylash Sharma, Bibhya Univ South Pacific Sch Comp Informat & Math Sci Suva Fiji

ISBN: (纸本)9781728169293

Intelligent optimization algorithms based on swarm principles have been widely researched in recent times. The firefly algorithm (FA) is an intelligent swarm algorithm for global optimization problems. In literature, FA has been seen as one of the efficient and robust optimization algorithm. However, the solution search space used in FA is insufficient, and the strategy for generating candidate solutions results in good exploration ability but poor exploitation performance. Although, there are a lot of modifications and hybridizations of FA with other optimizing algorithms, there is still a room for improvement. Therefore, in this paper, we first propose modification of FA by introducing a stepping ahead parameter. Second, we design a hybrid of modified FA with Covariance Matrix Adaptation Evolution Strategy (CMAES) to improve the exploitation while containing good exploration. Traditionally, hybridization meant to combine two algorithms together in terms of structure only, and preference was not taken into account. To solve this issue, preference in terms of user and problem (time complexity) is taken where CMAES is used within FA's loop to avoid extra computation time. This way, the structure of algorithm together with the strength of the individual solution are used. In this paper, FA is modified first and later combined with CMAES to solve selected global optimization benchmark problems. The effectiveness of the new hybridization is shown with the performance analysis.

关键词： firefly algorithm covariance matrix adaptation evolution strategy optimization

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Design of a Technology Talent Pool Information Query System Based on Big Data Mining Technology 24

Design of a Technology Talent Pool Information Query System ...

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International Conference on Computer and Multimedia Technology (ICCMT)

作者： Chen, Man Hubei Acad Sci & Tech Informat Wuhan 430064 Peoples R China

ISBN: (纸本)9798400718267

In order to improve the comprehensiveness and success rate of information query, a technology talent pool information query system based on big data mining technology is designed. By collecting and storing data, cleaning and preprocessing, provide a user-friendly query interface to help users easily and quickly access information from the technology talent pool, protect users' information security and privacy through the security and privacy module. At the same time, the K-means clustering method is optimized using the firefly algorithm to achieve comprehensive mining of information in the technology talent pool. The system performance test results indicate that the system has a high success rate and recall rate in information query, which can provide support and reference for scientific and technological innovation activities, talent selection, and strategic decision-making of scientific research institutions and enterprises.

关键词： Data mining Technology talent pool Information inquiry K-means clustering firefly algorithm

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Bioinspired algorithm for 2D Packing Problem 7th

Bioinspired Algorithm for 2D Packing Problem

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7th Computer Science On-Line Conference (CSOC)

作者： Kureichik, Vladimir Kureichik, Liliya Kureichik, Vladimir, Jr. Zaruba, Daria Southern Fed Univ Rostov Na Donu Russia

ISBN: (纸本)9783319911892

The paper considers one of the most important problem of resource allocation - packing of units within 2D space. The problem is NP-hard. A problem formulation is made, as well as restrictions and boundary conditions are found out. To solve the considered problem the authors suggest to use firefly optimization algorithm on the basis of which there are developed a bioinspired algorithm. This algorithm allows to obtain sets of quazi-optimal solutions for the 2D packing problem within polynomial time. Also, there are suggested mechanisms for encoding and decoding of alternative solutions. and presented a scheme of firefly algorithm for 2D packing problem. On the basis of the suggested algorithm there are developed software for computational experiments on benchmarks. Experimental investigations were carried out taking into account time and quality of alternative solutions. As a result, experiments shows the effectiveness of the developed algorithm.

关键词： Packing problem Optimization Bioinspired algorithm firefly algorithm

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STUDY ON SWARM DYNAMICS CONVERTED INTO COMPLEX NETWORK 30

STUDY ON SWARM DYNAMICS CONVERTED INTO COMPLEX NETWORK

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30th European Conference on Modelling and Simulation, ECMS 2016

作者： Pluhacek, Michal Senkerik, Roman Janostik, Jakub Viktorin, Adam Zelinka, Ivan Tomas Bata Univ Zlin Fac Appl Informat Nam TG Masaryka 5555 Zlin 76001 Czech Republic VSB TUO Fac Elect Engn & Comp Sci Dept Comp Sci 17 Listopadu 15 Ostrava 70833 Czech Republic

ISBN: (纸本)9780993244025

In this study it is presented a summarization of our research of possible ways of creating of complex networks from the inner dynamics of Swarm Intelligence based algorithms. The particle swarm optimization algorithm and the firefly algorithm are studied in this paper. Several methods of complex network creation are proposed and discussed alongside with possibilities for future research and application.

关键词： Swarm Intelligence Particle Swarm Optimization firefly algorithm Complex Network

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