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Opposition and dimensional based modified firefly algorithm

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 New modified firefly algorithm for Function Optimization

A New Modified Firefly Algorithm for Function Optimization

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International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)

作者： Sarangi, Shubhendu Kumar Panda, Rutuparna Priyadarshini, Sabnam Sarangi, Archana SOA Univ Dept Elect & Instrumentat Engn ITER Bhubaneswar Orissa India VSSUT Dept Elect & Telecommun Engn Burla India SOA Univ Dept Elect & Commun Engn ITER Bhubaneswar Orissa India

ISBN: (纸本)9781467399395

This paper intends to provide a modified firefly algorithm based on firefly algorithm and improved particle swarm optimization. This firefly algorithm is a category of nature-enthused algorithm of swarm intelligence, i. e. depends on the response of a firefly to the light of other fireflies and also perform well on various numerical optimization problems. The modified algorithm uses the improved velocity concept of particle swarm optimization to enhance the searching behavior of standard algorithm. A comparison of the firefly algorithm with that of modified firefly algorithm is performed for some standard benchmark functions through simulations. The algorithms are also checked in various standard dimensions for providing effective output. The simulated results prove the superiority of modified firefly algorithm as compared to the traditional firefly algorithm in standard benchmark functions and in all dimensions. The results give an idea that the proposed modified algorithm enriches performance of the standard firefly algorithm and converges more quickly with less time to produce optimum solution.

关键词： Particle swarm optimization firefly algorithm modified firefly algorithm Benchmark functions Dimensions

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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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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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MFO-TL: modified firefly optimal transfer learning based motion correction of fetal brain and placenta MRI for thyroid prediction

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EVOLVING SYSTEMS 2024年第2期15卷 361-374页

作者： Keerthi, G. Abirami, M. S. SRM Inst Sci & Technol Dept Computat Intelligence Chennai India

Thyroid classification is required in the medical domain to better assist doctors in deciding diagnostic treatments. Although many researchers conducted experiments to detect abnormal conditions of fetal brains in an earlier stage, there exist several limitations like over-fitting problems and imbalance data problems. To deal with these problems, this paper proposes 'Transfer learning- Bidirectional Long Short Term Memory (TL-BiLSTM) which is an efficient thyroid classification model. This paper focuses on identifying the defects in fetal brains in a primary stage by investigating the thyroid range of the mother during the 19th week of pregnancy. In this research, TL is applied with Bi-LSTM for the improvement of Thyroid classification performance. The Transfer learning method selects the optimal batch size for the Bi-LSTM model to eliminate the overfitting problem. The bi-LSTM model learns the sequence in forward and reverses mode to store the useful features for the long term and discard the irrelevant features. The most significant features in the dataset are selected by applying a modified firefly algorithm (MFA). The modified firefly algorithm has the advantages of easy escape from local optima and a good convergence rate. For evaluation purposes, the thyroid dataset is used as input for investigating the proposed classifier's effectiveness. The evaluation results display that the proposed novel approach successfully identifies and classifies thyroid problems using fetal brain magnetic resonance imaging (MRI) images of various Gestational weeks.

关键词： Bidirectional long short-term memory modified firefly algorithm Transfer learning Fetal brain defects Thyroid MRI images

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Enhancing grid resilience and efficiency with distributed generators through fuzzified firefly optimization

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ELECTRICAL ENGINEERING 2024年第6期106卷 7803-7818页

作者： Anbuchandran, S. Christa, S. T. Jaya Suresh, S. Bhuvanesh, A. JCT Coll Engn & Technol Dept Elect & Elect Engn Coimbatore Tamil Nadu India Mepco Schlenk Engn Coll Dept Elect & Elect Engn Sivakasi Tamil Nadu India Sri Eshwar Coll Engn Dept Elect & Commun Engn Coimbatore Tamil Nadu India PSN Coll Engn & Technol Dept Elect & Elect Engn Tirunelveli Tamil Nadu India

In a conventional power system, distribution systems tend to incur higher power losses. However, integrating decentralized sources into power networks not only reduces these losses but also alleviates the strain on primary sources. Since centralized power sources are often situated at a distance, incorporating distributed generations (DGs) closer to load centers offers more efficient system operation. However, the lack of proper sizing alignment among secondary sources could result in operational, financial, and ecological complexities. Selecting optimal DG locations and sizes necessitates intricate computational processes, escalating workload demands. Fortunately, advancements in optimization software have simplified this task. The primary focus of this paper lies in optimizing DG allocation through the application of a fuzzified firefly algorithm (FFA). The strategic placement of diverse DG types aims to reduce both real and reactive power losses within the distribution system, followed by an evaluation of system performance indices. The study examines how different types of DGs incorporated into an IEEE-33 bus system elucidate the impact of varying power system loads, spanning from 50 to 150% of their rated value.

关键词： Curve fitting technique Distributed generators IEEE-33 bus modified firefly algorithm Pollutant emission Power loss minimization

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Scouting firefly algorithm and its Performance on Global Optimization Problems

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第3期14卷 445-451页

作者： Villaruz, Jolitte A. Gerardo, Bobby D. Gamao, Ariel O. Medina, Ruji P. Technol Inst Philippines Grad Programs Quezon City Philippines Northern Iloilo State Univ Coll Informat Comp Studies Iloilo Philippines Davao Norte State Coll Inst Comp Panabo City Philippines

effective optimization, metaheuristics should maintain the proper balance between exploration and exploitation. However, the standard firefly algorithm (FA) posted some limitations in its exploration process that can eventually lead to premature convergence, affecting its performance and adding uncertainty to the optimization results. To address these constraints, this study introduces an additional novel search mechanism for the standard FA inspired by the behavior of the scout bee in the artificial bee colony (ABC) algorithm, termed the "Scouting FA". Specifically, fireflies stuck in the local optima will take directed extra random walks to escape toward the region of the optimum solution, thus improving convergence accuracy. Empirical findings on the five standard benchmark functions have validated the effects of this modification and revealed that Scouting FA is superior to its original version.

关键词： -Metaheuristics firefly algorithm modified firefly algorithm global optimization scout bee exploitation and exploration

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Investigation on load harmonic reduction through solar-power utilization in intermittent SSFI using particle swarm, genetic, and modified firefly optimization algorithms

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第4期42卷 4117-4133页

作者： Albert, Johny Renoald Sharma, Aditi Rajani, B. Mishra, Ashish Saxena, Ankur Nandagopal, C. Mewada, Shivlal Vivekanandha Coll Engn Women Dept EEE Namakkal Tamil Nadu India ITM Vocat Univ Dept CSE Vadodara India Aditya Coll Engn & Technol Dept EEE Surampalem Andhra Pradesh India Gyan Ganga Inst Technol & Sci Dept CSE Jabalpur Madhya Pradesh India Indus Univ Indus Inst Informat & Commun Technol Dept CSE Ahmadabad Gujarat India M Kumarasamy Coll Engn Autonomous Dept ECE Karur Tamil Nadu India Goverment Holkar Sci Coll Dept Comp Sci Indore India

A new Symmetric Solar Fed Inverter (SSFI) proposed with a reduced number of components compared to the classical, modified, conventional type of Multilevel Inverter (MLI). The objective of this architecture is to design fifteen-level SSFI, this circuit uses a single switch with minimizing harmonics, and Modulation Index (MI) values. Power Quality (PQ) is developed by using the optimization algorithms like as Particle Swarm Optimization (PSO), Genetic algorithm (GA), modified firefly algorithm (MFA). It's determined to generate the gating pulse and finding optimum firing angle values calculate as per the input of MPP intelligent controller schemes. The proposed circuit is solar fed inverter used for optimization techniques governed by switching controller approach delivers a major task. The comparison is made for different optimization algorithm has significantly reduced the harmonic content by varying the modulation index and switching angle values. SSFI generates low distortion output uses through without any additional filter component through utilizing MATLAB Simulink software (2020a). The SSFI circuit assist Xilinx Spartan 3-AN Filed Program Gate Array (FPGA) tuned by optimization techniques are presented for the effectiveness of the proposed model.

关键词： Symmetric solar fed inverter particle swarm optimization genetic algorithm modified firefly algorithm power quality

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A novel evolving framework for energy management in wind-gas reinforced unit commitment with demand response

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IET RENEWABLE POWER GENERATION 2022年第16期16卷 3473-3484页

作者： Safipour, Hamed Abdollahi, Amir Hajmohammadi, Mohammad Sadegh Alizadeh, Mohammad Iman Islamic Azad Univ Dept Elect Engn Sirjan Branch Sirjan Iran Shahid Bahonar Univ Dept Elect Engn Kerman Iran Islamic Azad Univ Sirjan Branch Dept Comp Engn Sirjan Iran Tarbiat Modares Univ Elect & Comp Engn Fac Tehran Iran

This research work tries to investigate the provision of flexibility on both sides of production and demand security constraint unit commitment (SCUC) before the schedule is considered. To this end, a powerful heuristic technique based on modified firefly algorithm (FA) is devised to achieve the optimal solution. Moreover, fast-starting gas units (FGUs) are considered as providers of lateral flexibility as a result of their quickness in starting work and high ramp capability. Considering the fact that the normal operation of units is subject to the supply of reliable gas through connected gas transmission lines, the uncertainty of gas supply in the suggested model is considered to imitate the effects of deviation in the volume of gas over pipelines. The application of the suggested model has been tested on IEEE-118 bus.

关键词： energy management powerful heuristic technique gas supply wind-gas reinforced unit commitment power generation dispatch power generation economics lateral flexibility demand response gas units high ramp capability modified firefly algorithm optimal solution reliable gas demand side management demand security constraint unit commitment novel evolving framework power generation scheduling pipelines power system security connected gas transmission lines

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A hybrid modified FA-ANFIS-P&O approach for MPPT in photovoltaic systems under PSCs

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INTERNATIONAL JOURNAL OF ELECTRONICS 2020年第5期107卷 703-718页

作者： Farzaneh, Javad Semnan Univ Dept Elect Engn Semnan Iran

A modified firefly algorithm (MFO)-based adaptive neuro-fuzzy inference system (ANFIS) combined with the perturbation and observation (P&O) is used in this paper to track the maximum power point (MPP) in photovoltaic systems (PVs). The proposed method identifies and tracks the MPP in two stages. First, according to the irradiance on the solar panels, the ANFIS approximately identifies the MPP. In the second stage, the P&O method starts to act in the tracking cycle and initiates an accurate searching process from that point. The suggested hybrid method covers the problems of commonly-used methods, such as inability in detecting the global MPP under partial shading conditions (PSCs) and trapping in the local optima. Furthermore, the method provides significantly higher speed for the MPP tracking under various irradiance patterns. To prove the above-mentioned claims, the given approach is compared with the P&O method as a common method in the MPPT and particle swarm optimisation (PSO) which operates based on swarm intelligence. Simulation results obtained from MATLAB/Simulink environment show that the proposed method identifies and tracks the MPP under uniform irradiance and PSCs in a very short time of roughly 0.2?s.

关键词： PV system modified firefly algorithm adaptive neuro fuzzy inference system perturbation and observation solar energy

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