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2nd International Conference on Computational Intelligence in Data Mining (ICCIDM)

作者： Chiranjeevi, Karri Jena, Uma Ranjan Krishna, B. Murali Kumar, Jeevan VSSUT Dept Elect & Telecommun Engn Burla 768018 Odisha India Sri Sivani Inst Technol Dept Elect & Commun Engn Srikakulam Andhra Pradesh India

ISBN: (纸本)9788132227311;9788132227298

firefly algorithm optimization is based on the attractiveness/brightness of the firefly. In firefly algorithm, a lighter (lesser fitness function) firefly move towards the brighter firefly (higher fitness function) with amplitude proportional to Euclidean distance between the lighter and brighter firefly. If no such brighter firefly is found then it moves randomly is search space. This random move causes chance of decrement in brightness of the brighter firefly depending on the direction in which it is move. We proposed a modified firefly algorithm in which movement of brighter fireflies is towards the direction of brightness instead of random move. If this direction of brightness is not in the process then firefly is in same position. We call this novel algorithm as MFA-LBG. Experimental results shows that modified firefly algorithm reconstructed image quality and fitness function value is better than the standard firefly algorithm (FA-LBG) and LBG algorithms. It is observed that that modified firefly algorithm convergence time is less than the standard firefly algorithm.

关键词： Vector quantization LBG firefly algorithm modified firefly algorithm Attractiveness Fitness function

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Optimal Remediation Design of Unconfined Contaminated Aquifers Based on the Finite Element Method and a modified firefly algorithm

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WATER RESOURCES MANAGEMENT 2015年第8期29卷 2895-2912页

作者： Kazemzadeh-Parsi, Mohammad Javad Daneshmand, Farhang Ahmadfard, Mohammad Amin Adamowski, Jan Islamic Azad Univ Shiraz Branch Dept Mech Engn Shiraz Iran McGill Univ Dept Mech Engn Montreal PQ H3A 0C3 Canada McGill Univ Dept Bioresource Engn Montreal PQ H9X 3V9 Canada Shiraz Univ Sch Mech Engn Shiraz Iran

Remediation of contaminated sites requires an optimal decision making system to develop remediation techniques in a cost-effective and efficient manner. A coupled simulation-optimization solution approach, based on the finite element method (FEM) and a modified firefly algorithm (MFA), is developed in this study for optimal contaminated groundwater remediation design. A new modified firefly optimization algorithm is proposed by modifying the traditional firefly algorithm in three ways: (i) adding memory, (ii) preventing premature convergence to local optima and thus accelerating the optimization process, and (iii) proposing a new updating formula. Modifications performed in the present study improved the applicability and efficiency of the traditional metaheuristic firefly optimization algorithm, and led the MFA to outperform both its predecessor and conventional optimization methods (e.g., genetic algorithm). A hypothetical, unconfined contaminated field is considered and remediated by considering pump and treat and flushing methods. Pumping rates are considered as design variables while the number of pumps and pump locations, as well as the pumping period, are initially assumed. The coupled simulation-optimization model (FEM-MFA) proposed in this study constitutes an effective way to determine an optimal remediation design for a contaminated aquifer. The results of the present investigation will contribute to improve groundwater management in contaminated aquifers.

关键词： Finite-element method modified firefly algorithm Pump and treat strategy Groundwater remediation

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Opposition and dimensional based modified firefly algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 44卷 168-176页

作者： Verma, Om Prakash Aggarwal, Deepti Patodi, Tejna Delhi Technol Univ Dept Informat Technol Delhi India

This paper presents the modified firefly algorithm (FA) originally proposed by Yang. firefly algorithm is based on the idealized behavior of the flashing characteristics of the fireflies. Though firefly is powerful in local search, it does not search well globally due to being trapped in local optimum. Due to this reason, the convergence is generally slow. The FA also doesn't give efficient solution in high dimensional problems. The proposed approach gives more efficient solution with reduced time complexity in comparison to original FA. Two modifications made are: (I) Opposition-based methodology is deployed where initialization of candidate solutions is done using opposition based learning to improve convergence rate of original FA, which includes initializing the opposite number of positions of each firefly. This also ensures efficient searching of the whole search space, ( 2) The dimensional-based approach is employed in which the position of each firefly is updated along different dimensions. This results in more optimal solution. This algorithm works for High Dimensionality problems, especially in terms of accuracy in finding the best optimal solution and in terms of fast convergence speed as well. Several complex multidimensional standard functions are employed for experimental verification. Experimental results include comparison with other Evolutionary algorithms which show that the Opposition and Dimensional based FA (ODFA) gives more accurate optimal solution with high convergence speed than the original FA and those achieved by existing methods. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： firefly algorithm Fitness function modified firefly algorithm Opposition based learning Optimization

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A modified firefly algorithm applying on multi-objective radial-based function for blasting

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NEURAL COMPUTING & APPLICATIONS 2022年第3期34卷 2455-2471页

作者： Abbaszadeh Shahri, Abbas Khorsand Zak, Mohammad Abbaszadeh Shahri, Hossein Islamic Azad Univ Roudehen Branch Dept Civil Engn Tehran Iran Islamic Azad Univ Aligudarz Branch Dept Appl Math Tehran Iran Texas Tech Univ Dept Civil Engn Lubbock TX 79409 USA

Modifying the metaheuristics as a striking alternative of basic algorithms is outstanding and efficient scientific approach in optimization of engineering problems to improve robustness and convergence rate. firefly algorithm (FA) is one of the new metaheuristics inspired by the flashing behavior of fireflies, where the performance of each randomly generated solution on objective function is evaluated by the brightness. In the current paper, a modified firefly algorithm (MFA) was introduced using expectation value and generalized weighted average of a random brightness and then evaluated with different benchmark functions. Since brightness varies with movements of fireflies, the parameter settings can adaptively be tuned for different problems. The capability of the MFA then in hybridizing with a developed automated multi-objective radial-based function network (MORBF) was examined. In blasting engineering, multi-objective models covering the peak particle velocity (PPV) and the vibration frequency (F-vib) due to providing more insight on safety criteria significantly are essential and great of interested. The hybrid MORBF-MFA then was applied on 78 blasting data comprising stemming, burden, spacing, total charge, distance, and charge per delay to provide more accurate predictive model. Detailed executed analyses through different metrics showed 1.01% and 2.43% improvement in hybrid MORBF-MFA corresponding to PPV and F-vib over MORBF-FA. The observed results approved that the introduced MFA as a reliable and feasible tool with accurate enough response can effectively be applied to multi-objective problems. Implemented sensitivity analyses scored the distance and burden as the most and least influences factors on predicted outputs.

关键词： Blasting Vibration frequency Multi-objective model modified firefly algorithm Sensitivity Prediction

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Application of Dynamically Search Space Squeezed modified firefly algorithm to a Novel Short Term Economic Dispatch of Multi-Generation Systems

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IEEE ACCESS 2021年 9卷 1918-1939页

作者： Liaquat, Sheroze Fakhar, Muhammad Salman Kashif, Syed Abdul Rahman Rasool, Akhtar Saleem, Omer Zia, Muhammad Fahad Padmanaban, Sanjeevikumar Natl Univ Comp & Emerging Sci Dept Elect Engn Lahore 54000 Pakistan Univ Engn & Technol Dept Elect Engn Lahore 54000 Pakistan Sharif Coll Engn & Technol Dept Elect Engn Lahore 55150 Pakistan Aalborg Univ Dept Energy Technol DK-6700 Esbjerg Denmark

The absence of the global best component in the update equation of the conventional firefly algorithm degrades its exploration properties. This research proposes multi-update position criteria to enhance the exploration properties of the conventional firefly technique while including the effect of the global best solution on the movement of the fireflies in the search space of the objective function. Moreover, the dynamic search space squeezing is applied to constrict the movement of the fireflies within the certain limits to avoid their oscillatory movement as the solution approaches towards the global best by determining the optimal trajectory for each firefly. The robustness of the suggested firefly algorithm is tested on a hybrid energy system consisting of thermal, hydroelectric, and Photovoltaic (PV) energy source. The intermittent nature of the PV energy source is explained using fractional integral polynomial model and Auto Regressive Integrated Moving Average (ARIMA) model. The main dispatch problem is successfully computed using both the modified firefly and the simple firefly algorithm by determining the optimal power share of each energy source for different scheduling intervals. The suggested operational strategy reduces the overall generation cost of the system while preserving the various system constraints. Due to the stochastic nature of the meta-heuristic techniques, the two suggested algorithms are compared statistically for different test cases using the independent t-test results. The statistical comparison suggests that the performance of the modified firefly is superior to its conventional counterpart as the evaluation parameters of the modified firefly converge to relatively lower value as compared to the parameters of the simple firefly algorithm.

关键词： modified firefly algorithm auto regressive integrated moving average model firefly algorithm hybrid energy systems independent t-test results

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Bouc-Wen hysteresis model identification using modified firefly algorithm

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JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS 2015年 395卷 229-233页

作者： Zaman, Mohammad Asif Sikder, Urmita Stanford Univ Dept Elect Engn Stanford CA 94305 USA Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA

The parameters of Boric Wen hysteresis model are identified using a modified firefly algorithm. The proposed algorithm uses dynamic process control parameters to improve its performance. The algorithm is used to find the model parameter values that results in the least amount elector between a set of given data points and points obtained from the Bouc-Wen model. The performance of the algorithm is compared with the performance of conventional firefly algorithm, Genetic algorithm and Differential Evolution algorithm in terms of convergence rate and accuracy. Compared to the other three optimization algorithms, the proposed algorithm is found to have good convergence rate with high degree of accuracy in identifying Bouc-Wen model parameters. Finally, the proposed method is used to find the Bouc-Wen model parameters from experimental data The obtained model is found to be in good agreement with measured data. (C) 2015 Elsevier B.V. All rights reserved

关键词： Bouc-Wen model Parameter identification modified firefly algorithm

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An efficient joint spectrum and power allocation in cognitive radio networks using a modified firefly algorithm

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INTERNATIONAL JOURNAL OF COMMUNICATION NETWORKS AND DISTRIBUTED SYSTEMS 2017年第2期19卷 214-236页

作者： Tounsi, Abdelkader Babes, Malika Univ Badji Mokhtar Dept Comp Sci Syst & Networks Lab Annaba Algeria Univ Badji Mokhtar Dept Comp Sci Annaba Algeria

In this paper, the applicability and efficiency of the new meta-heuristic algorithm called firefly algorithm to solve the joint power control and channel allocation problem in cognitive radio networks is studied. A modified version of the firefly algorithm (MFA) using the new attractiveness factor is proposed to solve the problem. Theoretical analysis is presented to prove effectiveness and the existence of Nash equilibrium of the proposed strategy. Simulation results of this study show that the proposed power control based on modified firefly algorithm cannot only able to find the optimal transmit power that maximises each secondary's own utility but also improves the fairness among secondary users and satisfies the outage probability constraints. Our results show that the proposed method outperforms previous approaches (including the standard firefly algorithm) in terms of convergence speed.

关键词： cognitive radio network firefly algorithm power control channel allocation outage probability fairness Nash equilibrium game theory modified firefly algorithm attractiveness factor

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A modified firefly algorithm-artificial neural network expert system for predicting compressive and tensile strength of high-performance concrete

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CONSTRUCTION AND BUILDING MATERIALS 2018年 180卷 320-333页

作者： Dac-Khuong Bui Tuan Nguyen Chou, Jui-Sheng Nguyen-Xuan, H. Tuan Duc Ngo Univ Melbourne Dept Infrastruct Engn Melbourne Vic Australia Natl Taiwan Univ Sci & Technol Dept Civil & Construct Engn 43Sec 4Keelung Rd Taipei 106 Taiwan Chi Minh City Univ Technol Hutech Ctr Interdisciplinary Res Technol Ho Chi Minh City Vietnam

The compressive and tensile strength of high-performance concrete (HPC) is a highly nonlinear function of its constituents. The significance of expert frameworks for predicting the compressive and tensile strength of HPC is greatly distinguished in material technology. This study aims to develop an expert system based on the artificial neural network (ANN) model in association with a modified firefly algorithm (MFA). The ANN model is constructed from experimental data while MFA is used to optimize a set of initial weights and biases of ANN to improve the accuracy of this artificial intelligence technique. The accuracy of the proposed expert system is validated by comparing obtained results with those from the literature. The result indicates that the MFA-ANN hybrid system can obtain a better prediction of the high-performance concrete properties. The MFA-ANN is also much faster at solving problems. Therefore, the proposed approach can provide an efficient and accurate tool to predict and design HPC. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： High-performance concrete Data mining Evolutionary optimization Artificial neural network modified firefly algorithm

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modified firefly algorithm for Improved Maximum Power Extraction on Wind Energy Conversion System

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INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH 2018年第3期8卷 1208-1216页

作者： Putri, Ratna Ika Mahmudi, Irwan Pujiantara, Margo Ardyono, P. Taufik, Taufik Hery, Mauridhi P. State Polytech Malang Dept Elect Engn Malang East Java Indonesia Tadulako Univ Dept Elect Engn Tadulako Indonesia Sepuluh Nopember Inst Technol Fac Elect Technol Dept Elect Engn Surabaya Indonesia Calif Polytech State Univ San Luis Obispo Dept Elect Engn San Luis Obispo CA USA

This paper presents a maximum power extraction and control system using modified firefly algorithm (MFA) to achieve high efficiency maximum power extraction from a grid-tied wind turbine using permanent magnet synchronous generator (PMSG). To get maximum power extraction from the PMSG, the MFA adjusts duty cycle of boost converter based on rectifier's output voltage and current. Simulation results show that MFA successfully achieves tracking capability under varying wind speed to acquire the maximum power during constant power coefficient operation of the wind turbine. The MFA also yields higher efficiency, faster response and lower integral of time and absolute error (ITAE) compared to the particle swarm optimization (PSO) and the perturb and observe (P&O) counterparts. The proposed MFA was further tested empirically against P&O method to extract maximum power on a 500W lab-scale prototype of wind energy conversion system (WECS). The hardware test results demonstrate that MFA outperforms P&O method when used as an MPPT controller algorithm and yields greater efficiency. The efficiency of PMSG wind turbine with MFA is 93.63 %, while P&O produce 81.54 %.

关键词： modified firefly algorithm maximum power wind turbine PMSG

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Energy Hub Optimization Using modified firefly algorithm 8

Energy Hub Optimization Using Modified Firefly Algorithm

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8th International Renewable Energy Congress (IREC)

作者： Abdelaziz, Almoataz Y. El-Zonkoly, Amany M. Eladl, Alaa M. Ain Shams Univ Fac Engn Elect Power & Machines Dept Cairo Egypt AAST Coll Engn & Tech Dept Elect & Control Engn Alexandria Egypt

ISBN: (纸本)9781509067510

With the continuous increase of energy demand along with the congested transmission systems, distributed generation (DG) is suggested as a possible solution. In addition, with the limited fossil energy resources, the need for sustainable and environmentally friendly energy sources is increased. Energy hubs as one type of DG, have made the local use of multi energy carrier systems economically attractive. In this paper, a modified firefly based algorithm is proposed to optimally dispatch the energy hub input energy carriers. The objective is to minimize the total energy cost and emission amount of the energy hub while maximizing the cost of electrical and heating energy sold back to the network. The proposed algorithm is applied to a commercial facility (hospital) as a case study. The results show the effectiveness of the proposed algorithm in solving the optimal dispatch of the energy hub components while reducing the overall cost and satisfying the system operational constraints.

关键词： Energy hubs optimal dispatch modified firefly algorithm

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