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Modified firefly algorithm based multilevel thresholding for color image segmentation

NEUROCOMPUTING 2017年 240卷 152-174页

作者： He, Lifang Huang, Songwei Kunming Univ Sci & Technol Dept Elect & Commun Engn Kunming 650093 Peoples R China Kunming Univ Sci & Technol Dept Mineral Proc Kunming 650093 Peoples R China

In this paper, a modified firefly algorithm (MFA) is proposed to find the optimal multilevel threshold values for color image. Kapur's entropy, minimum cross entropy and between-class variance method is used as the objective functions. To test and analyze the performance of the MFA algorithm, the presented method are tested on ten test color image and the results are compared with basic firefly algorithm (FA), Brownian search based firefly algorithm (BFA) and Levy search based firefly algorithm (LFA). The experimental results show that the presented MFA algorithm outperforms all the other algorithms in term of the optimal threshold value, objective function, PSNR, SSIM value and convergence. In MFA algorithm, chaotic map is used to the initialization of firefly population, which can enhance the diversification. In addition, global search method of particle swarm optimization (PSO) algorithm is introduced into the movement phase of fireflies. Compared with the other methods, the MFA algorithm is an effective method for multilevel color image thresholding segmentation. (C) 2017 Elsevier B.V. All rights reserved.

关键词： firefly algorithm Multilevel image segmentation Color image Swarm intelligence algorithm

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Improved firefly algorithm approach applied to chiller loading for energy conservation

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ENERGY AND BUILDINGS 2013年 59卷 273-278页

作者： Coelho, Leandro dos Santos Mariani, Viviana Cocco Pontifical Catholic Univ Parana PUCPR Ind & Syst Engn Grad Program PPGEPS BR-80215901 Curitiba Parana Brazil Pontifical Catholic Univ Parana PUCPR Dept Mech Engn PPGEM BR-80215901 Curitiba Parana Brazil Fed Univ Parana UFPR Dept Elect Engn Elect Engn Grad Program PPGEE Polytech Ctr BR-81531970 Curitiba Parana Brazil

Significant energy savings can be achieved by optimizing chiller operation and design in heating, ventilation and cooling (HVAC) systems. In terms of optimization, various metaheuristics have been proposed to the optimal chiller loading problem. New metaheuristics are also emerging recently, between them the firefly algorithm. firefly algorithm is a nature inspired algorithm based on the idealized behavior of the flash pattern and characteristics of fireflies. This study proposes a new improved firefly algorithm (IFA) based on Gaussian distribution function to the optimal chiller loading design. To testify the performance of the proposed method, the paper adopts two case studies comparing the results of the developed model using IFA with those of traditional firefly algorithm and other optimization methods in literature. In this paper, the optimization problem is minimize energy consumption of multi-chiller systems, where the objective function is energy consumption and the optimum parameter is the partial loading ratio of each chiller. The results of both case studies show that the proposed IFA outperform several optimization methods of the literature in terms of minimum energy consumption solution of the optimal chiller loading problem. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Optimal chiller loading Optimization firefly algorithm

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A novel systematic and evolved approach based on XGBoost-firefly algorithm to predict Young's modulus and unconfined compressive strength of rock

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ENGINEERING WITH COMPUTERS 2022年第SUPPL 5期38卷 3829-3845页

作者： Cao, Jing Gao, Juncheng Rad, Hima Nikafshan Mohammed, Ahmed Salih Hasanipanah, Mahdi Zhou, Jian Longfor Grp Holdings Ltd Beijing 100000 Peoples R China Xian Univ Technol State Key Lab Ecohydraul Northwest Arid Reg China Xian 710000 Peoples R China China Vanke Co Ltd Shenzhen 518000 Peoples R China Tabari Univ Babol Coll Comp Sci Babol Iran Univ Sulaimani Coll Engn Civil Engn Dept Kurdistan Region Sulaymaniyah Iraq Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Cent South Univ Sch Resources & Safety Engn Changsha 410083 Peoples R China

To design the tunnel excavations, the most important parameters are the engineering properties of rock, e.g., Young's modulus (E) and unconfined compressive strength (UCS). Numerous researchers have attempted to propose methods to estimate E and UCS indirectly. This task is complex due to the difficulty of preparing and carrying out such experiments in a laboratory. The main aim of the present study is to propose a new and efficient machine learning model to predict E and UCS. The proposed model combines the extreme gradient boosting machine (XGBoost) with the firefly algorithm (FA), called the XGBoost-FA model. To verify the feasibility of the XGBoost-FA model, a support vector machine (SVM), classical XGBoost, and radial basis function neural network (RBFN) were also employed. Forty-five granite sample sets, collected from the Pahang-Selangor tunnel, Malaysia, were used in the modeling. Several statistical functions, such as coefficient of determination (R-2), mean absolute percentage error (MAPE) and root mean square error (RMSE) were calculated to check the acceptability of the methods mentioned above. A review of the results of the proposed models revealed that the XGBoost-FA was more feasible than the others in predicting both E and UCS and could generalize.

关键词： Rock properties XGBoost Machine learning firefly algorithm

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A modified firefly algorithm to maximize heat dissipation of a rectangular porous fin in heat exchangers exposed to both convective and radiative environment

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART E-JOURNAL OF PROCESS MECHANICAL ENGINEERING 2019年第6期233卷 1203-1216页

作者： Deshamukhya, Tuhin Nath, Ratnadeep Hazarika, Saheera Azmi Bhanja, Dipankar Nath, Sujit Natl Inst Technol Silchar Dept Mech Engn Silchar Assam India Indian Inst Technol Roorkee Dept Mech Engn Roorkee Uttarakhand India

The current study deals with the optimization of significant parameters of aluminium and copper rectangular porous fins using firefly algorithm with reflective boundary condition. The study has been done considering convective heat transfer, in the first case, as well as combined convective and radiative modes of heat transfer, in the second case. To solve the non-linear governing equation, a semi analytical technique, differential transformation method is adopted. The results obtained by differential transformation method are validated by the numerical solution obtained by the finite difference method. The performance of firefly algorithm is evaluated by comparing with the results obtained by particle swarm optimization where it is seen that for the current set of equations, firefly algorithm took lesser number of iterations and computational time to converge than particle swarm optimization for all the cases. The analysis has been done for three different fin volumes and the effect of important variables which directly influence the heat transfer rate through porous fins has been discussed.

关键词： Particle swarm optimization firefly algorithm porous fin differential transformation method radiation heat transfer

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A combined adaptive neuro-fuzzy inference system-firefly algorithm model for predicting the roller length of a hydraulic jump on a rough channel bed

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NEURAL COMPUTING & APPLICATIONS 2018年第6期29卷 249-258页

作者： Azimi, Hamed Bonakdari, Hossein Ebtehaj, Isa Michelson, David G. Razi Univ Dept Civil Engn Kermanshah Iran Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada

Hydraulic jumps can occur downstream of hydraulic structures, such as normal weirs, gates and ogee spillways. The roller length is one of the most important parameters of hydraulic jumps in open channels. In this study, the roller length of a hydraulic jump on a rough bed is predicted using a hybrid of adaptive neuro-fuzzy inference systems and the firefly algorithm (ANFIS-FA). First, the effect of parameters including the Froude number (Fr), sequent depth (h (2)/h (1)) and relative roughness (ks/h (1)) upstream of a hydraulic jump is studied. Following the modeling result analysis, ANFIS-FA is introduced as the superior model for estimating the roller length of a hydraulic jump on a rough bed according to Fr, h (2)/h (1) and ks/h (1). The calculated MAPE, RMSE and correlation coefficient values for the superior model are 7.606, 1.771 and 0.970, respectively. ANFIS-FA predicted approximately 40 % of the results with less than 5 % error, and only 36 % of data had more than 10 % error.

关键词： Supercritical Roller length Jump ANFIS firefly algorithm

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Mixed variable structural optimization using firefly algorithm

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COMPUTERS & STRUCTURES 2011年第23-24期89卷 2325-2336页

作者： Gandomi, Amir Hossein Yang, Xin-She Alavi, Amir Hossein Univ Akron Dept Civil Engn Akron OH 44325 USA Univ Cambridge Dept Engn Cambridge CB2 1PZ England Iran Univ Sci & Technol Sch Civil Engn Tehran Iran

In this study, a recently developed metaheuristic optimization algorithm, the firefly algorithm (FA), is used for solving mixed continuous/discrete structural optimization problems. FA mimics the social behavior of fireflies based on their flashing characteristics. The results of a trade study carried out on six classical structural optimization problems taken from literature confirm the validity of the proposed algorithm. The unique search features implemented in FA are analyzed, and their implications for future research work are discussed in detail in the paper. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Metaheuristic optimization firefly algorithm Mixed continuous-discrete variable problems Structural optimization

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Optimal reactive power dispatch using hybrid Nelder-Mead simplex based firefly algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2015年 66卷 9-24页

作者： Rajan, Abhishek Malakar, T. Natl Inst Technol Silchar Dept Elect Engn Silchar Assam India

This paper presents a novel hybrid algorithm combining firefly algorithm (FA) and Nelder Mead (NM) simplex method for solving power system Optimal Reactive Power Dispatch (ORPD) problems. The ORPD is a very important aspect of power system operation and is a highly nonlinear, non-convex optimization problem, consisting of both continuous and discrete control variables. Like many other general purpose optimization methods, the original FA often traps into local optima and in order to overcome the shortcoming, in this paper, an efficient local search method called NM simplex subroutine is introduced in the internal architecture of the original FA algorithm. The proposed Hybrid firefly algorithm (HFA) avoids premature convergence of original FA by exploration with FA and exploitation with NM simplex. The proposed method is applied to determine optimal settings of generator voltages, tap positions of tap changing transformers and VAR output of shunt capacitors to optimize two different objective functions;such as minimization of real power loss and voltage deviations. The program is developed in Matlab and the proposed hybrid algorithm is examined on two standard IEEE test systems for solving the ORPD problems. For validation purpose, the results obtained with the proposed approach are compared with those obtained by other methods. It is observed that the proposed method has better convergence characteristics and robustness compared to the original version of FA and other existing methods. It is revealed that the proposed hybrid method is able to provide better solutions. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Optimal reactive power dispatch Nelder-Mead simplex search method firefly algorithm Hybrid algorithm

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Structural shape optimization of free-form surface shell and property of solution search using firefly algorithm

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2015年第4期29卷 1449-1455页

作者： Tanaka, Natsuki Honma, Toshio Yokosuka, Yohei Kagoshima Univ Dept Architecture & Architectural Engn Kagoshima 8900065 Japan

The computation procedure for structural shape optimization is based on the heuristic optimization procedures, such as swarm intelligence (SI), that use patterns found in self-organizing phenomena observed in nature. Most SI techniques including particle swarm optimization (PSO) and artificial bee colony (ABC), attain a global optimal solution. The firefly algorithm (FA) can attain both a global optimal and local optimal solutions by setting suitable computational parameters. However, the method for setting these parameters is comparatively difficult. In order to simplify the setting of these parameters, we implement a computational scheme where the distance between two fireflies in the design variable space is dimensionless. The effectiveness and the validity of this FA are clarified by showing the diversified solution for a global optimal solution and local optimal solutions using a local search in the structural shape optimization of a free-form surface shell.

关键词： Computational parameters firefly algorithm Local search Global optimal solution and local optimal solutions Single-objective optimization

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Classifier ensemble reduction using a modified firefly algorithm: An empirical evaluation

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EXPERT SYSTEMS WITH APPLICATIONS 2018年 93卷 395-422页

作者： Zhang, Li Srisukkham, Worawut Neoh, Siew Chin Lim, Chee Peng Pandit, Diptangshu Univ Northumbria Fac Engn & Environm Dept Comp Sci & Digital Technol Computat Intelligence Res Grp Newcastle Upon Tyne NE1 8ST Tyne & Wear England UCSI Univ Fac Engn Technol & Built Environm Kuala Lumpur 56000 Malaysia Deakin Univ Inst Intelligent Syst Res & Innovat Waurn Ponds Vic 3216 Australia

In this research, we propose a variant of the firefly algorithm (FA) for classifier ensemble reduction. It incorporates both accelerated attractiveness and evading strategies to overcome the premature convergence problem cif the original FA model. The attractiveness strategy takes not only the neighboring but also global best solutions into account, in order to guide the firefly swarm to reach the optimal regions with fast convergence while the evading action employs both neighboring and global worst solutions to drive the search out of gloomy regions. The proposed algorithm is subsequently used to conduct discriminant base classifier selection for generating optimized ensemble classifiers without compromising classification accuracy. Evaluated with standard, shifted, and composite test functions, as well as the Black-Box Optimization Benchmarking test suite and several high dimensional UCI data sets, the empirical results indicate that, based on statistical tests, the proposed FA model outperforms other state-of-the-art FA variants and classical metaheuristic search methods in solving diverse complex unimodal and multimodal optimization and ensemble reduction problems. Moreover, the resulting ensemble classifiers show superior performance in comparison with those of the original, full-sized ensemble models. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Ensemble reduction Classification firefly algorithm

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Improved density peaks clustering based on firefly algorithm

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2020年第1期15卷 24-42页

作者： Zhao, Jia Tang, Jingjing Shi, Aiye Fan, Tanghuai Xu, Lizhong Hohai Univ Coll Comp & Informat Nanjing 211100 Peoples R China Nanchang Inst Technol Sch Informat Engn Nanchang 330099 Jiangxi Peoples R China

The cut-off distance of the density peaks clustering (DPC) algorithm need to be set manually;the two local densities of the algorithm have a large difference in the clustering effect on the same dataset. To address the issue, the paper proposes an improved DPC based on firefly algorithm. It combines the cut-off kernel and the Gaussian kernel defined by the DPC algorithm, and balances the effects of the two kernels by the weighting factor. Meanwhile, a cluster-like centre evaluation criterion based on local density and relative distance of preference coefficient is constructed. In order to determine the parameters of the cut-off distance, weighting factor and preference coefficient, the three parameters are optimised by the firefly algorithm with the Rand index as the objective function. The experiment results show that the performance of the proposed method on synthetic datasets and real datasets is better than DPC and its variants.

关键词： density peaks clustering DPC firefly algorithm cut-off distance weighting factor preference coefficient

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