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Navigability analysis of magnetic map with projecting pursuit-based selection method by using firefly algorithm

NEUROCOMPUTING 2015年第1期159卷 288-297页

作者： Ma, Yan Zhao, Yuxin Wu, Ligang He, Yongxu Yang, Xin-She Harbin Engn Univ Coll Automat Harbin 150001 Peoples R China Harbin Engn Univ Space Control & Inertial Technol Res Ctr Harbin 150001 Peoples R China Middlesex Univ Sch Sci & Technol London NW4 4BT England

The performance of geomagnetic aided navigation is closely related to the selection of geomagnetic matching area. This paper mainly studies the selection method of multi-parameter geomagnetic matching area based on the projection pursuit model, and then adopts the firefly algorithm, particle swarm optimization algorithm and differential evolution algorithm to obtain the optimal projection direction. After that, we compare the optimizing results comprehensively and give the evaluation of navigation performance in geomagnetic matching area to provide the basis for the selection of matching area. The implementation results show that when there are large differences between the characteristic parameters, the computation efficiency of firefly algorithm performs significantly better than the other two optimization algorithms. And under the same experimental conditions, the optimal geomagnetic matching area obtained by the method presented in this paper has the minimum matching position error and optimal navigation performance. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Navigability analysis Geomagnetic aided navigation Projecting pursuit-based selection method firefly algorithm

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Investigation of robustness of hybrid artificial neural network with artificial bee colony and firefly algorithm in predicting COVID-19 new cases: case study of Iran

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2022年第9期36卷 2461-2476页

作者： Shaibani, Mohammad Javad Emamgholipour, Sara Moazeni, Samira Sadate Univ Tehran Med Sci Sch Publ Hlth Dept Hlth Management & Econ Tehran Iran Zahedan Univ Med Sci Sch Nursing & Midwifery Med Surg Nursing Dept Zahedan Iran

As an ongoing public health menace, the novel coronavirus pandemic has challenged the world. With several mutations and a high transmission rate, the virus is able to infect individuals in an exponential manner. At the same time, Iran is confronted with multiple wave peaks and the health care system is facing a major challenge. In consequence, developing a robust forecasting methodology can assist health authorities for effective planning. In that regard, with the help of Artificial Neural Network-Artificial Bee Colony (ANN-ABC) and Artificial Neural Network- firefly algorithm (ANN-FA) as two robust hybrid artificial intelligence-based models, the current study intends to select the optimal model with the maximum accuracy rate. To do so, first a sample of COVID-19 confirmed cases in Iran ranging from 19 February 2020 to 25 July 2021 is compiled. 75% (25%) of total observation is randomly allocated as training (testing) data. Afterwards, an ANN model is trained with Levenberg-Marquardt algorithm. Accordingly, based on R-squared and root-mean-square error criteria, the optimal number of hidden neurons is computed as 17. The proposed ANN model is employed to develop ANN-ABC and ANN-FA models for achieving the maximum accuracy rate. According to ANN-ABC, the R- squared values of the optimal model are 0.9884 and 0.9885 at train and test stages. In respect to ANN-FA, the R-squared ranged from 0.9954 to 0.9940 at the train and test phases, which indicates the outperformance of ANN-FA for predicting COVID-19 new cases in Iran. Finally, the proposed ANN-ABC and ANN-FA are applied for simulating the COVID-19 new cases data in different countries. The results revealed that both models can be used as a robust predictor of COVID-19 data and in a majority of cases ANN-FA outperforms the ANN-ABC.

关键词： COVID-19 Artificial neural network Artificial bee colony firefly algorithm Hybrid model

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Optimal sitting and sizing of shunt capacitor for real power loss reduction on radial distribution system using firefly algorithm: A case study of Nigerian system

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2023年第2期45卷 5776-5788页

作者： Olabode, E. O. Ajewole, T. O. Okakwu, I. K. Ade-Ikuesan, O. O. Ladoke Akintola Univ Technol Dept Elect & Elect Engn Ogbomosho Nigeria Osun State Univ Dept Elect & Elect Engn Osogbo Nigeria Univ Benin Dept Elect & Elect Engn Benin Nigeria Olabisi Onabanjo Univ Dept Comp & Elect Engn Ago Iwoye Nigeria

Geometrical increase in power demand and high load density at the distribution ends of modern power systems have key consequential problems of high power loss and poor voltage profile, as a result of which the integrity of radial distribution networks to faithfully account for the energy received from transmission subsystems has been seriously undermined. This challenge is, however, being overcome by placement of shunt capacitors to supply the reactive power required for compensation;hence, optimal sitting and sizing of compensators has been intensively researched. As efficient as the use of meta-heuristic algorithms for joint optimal placement and sizing of the shunt capacitors are, employment of the approach on the Nigerian radial distribution system (RDS) is not yet popular as most of the earlier works reported rather used analytical and numerical programming approaches. In this study, therefore, the use of firefly algorithm (FA) on a Nigerian 11-kV feeder is presented as an approach to optimally site and size shunt capacitor for real power loss reduction on such network. Backward-forward sweep load flow technique, with voltage stability index (VSI), is employed to find the candidate buses where the shunt capacitors would be installed, then FA is employed to determine the optimal size required. This approach is implemented on a 34-bus 11-kV feeder and it is found out that the system?s real power loss reduced from 762.6419 to 597.7486?kW, while the minimum bus voltage magnitude was raised from 0.8295 to 0.8456 p.u. and the minimum system VSI was improved from 0.4741 to 0.5121 p.u. Based on these results, the proposed approach is, therefore, considered a promising technique for sitting and sizing shunt capacitor optimally in real practical RDS.

关键词： Backward-forward sweep firefly algorithm loss minimization voltage profile voltage stability index

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A multi-objective parameter optimization and decision-making method for multi-pass end milling with firefly algorithm and Markov clustering

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2024年第4期46卷 1-22页

作者： Cai, Xu-Lin Yang, Wen-An Yang, Xue-Feng You, You-Peng Nanjing Univ Aeronaut & Astronaut Sch Mech & Elect Engn Natl Key Lab Sci & Technol Helicopter Transmiss Nanjing 210016 Peoples R China

Machining parameter optimization holds profound significance within the domain of milling operations, as the selection of these parameters can exert a substantial influence on both production efficiency and the quality of produced components. Notably, the integration of the chatter stability constraint into the optimization model remains underrepresented in the existing literature, despite its pivotal role in ensuring tool safety and machining quality. Furthermore, while several multi-objective optimization (MOO) algorithms have been devised, their effectiveness is often compromised due to the complexity of the milling parameter optimization model. In response to these challenges, this study developed an updated full discretization method (UFDM)-based three-dimensional (3-D) stability prediction model for multi-pass end milling. A milling parameter optimization model is meticulously formulated herein, which simultaneously optimizes the number of passes, feed rate, spindle speed, axial cutting depth, and radial cutting depth in multi-pass end milling while complying with constraints including machine tool performance, tool life, workpiece characteristics, and 3-D stability, to minimize production time and surface roughness. Moreover, a novel MOO and decision-making system for milling parameters is developed to solve the constructed model and assist decision-making, which includes a novel Markov clustering (MCL)-enabled MOO firefly algorithm (MEMOFA), a firefly algorithm-enabled MCL and a pseudo-weight-coefficient-vector-based decision-making method. The empirical findings encompassing six benchmark functions unequivocally attest to the superior performance of the elaborated MEMOFA in matters of convergence, diversity, and spread. Furthermore, an extensive optimization for the milling parameters model is also carried out to verify the superiority of the developed MOO and decision-making system in solving milling parameters optimization.

关键词： Markov clustering firefly algorithm Multi-objective optimization Multi-pass end milling

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An effective software project effort estimation system using optimal firefly algorithm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第5-Sup期22卷 11329-11338页

作者： Resmi, V. Vijayalakshmi, S. Chandrabose, R. Subash Sun Coll Engn & Technol Dept Comp Applicat Erachakulam India Thiagarajar Coll Engn Dept Comp Applicat Madurai Tamil Nadu India Sun Coll Engn & Technol Nagercoil India

The software effort estimation is one of the active presentations in the software project administration. Accordingly, it is not frequently possible to antedate the exact guesses in the estimation of software development effort. There are many techniques used for effort estimation. But we cannot confirm that one particular method alone gives good accuracy in estimates. In this expose, a hybrid process is gracefully boosted for the estimation of the effort of software project. The innovative process is unknown;but consolidation of the fuzzy analogy by the side of the firefly and the Expectation-Maximization (EM) process that is envisaged for estimation of the software project lead to the enhancement of accuracy in prediction. Furthermore, an EM is employed to group large amount of data. The significant production is set as an input to the fuzzy analogy in parallel to the firefly algorithm (FA). Consecutively, the FA is competently familiar in enhancing the optimal solutions and thereby improves estimation accuracy. The fuzzy analogy reliably helps the presentation of assessing the effort of the software project. The epoch-making process is proficient in java platform and its task is competently estimated.

关键词： Expectation maximization Fuzzy analogy firefly algorithm Software effort estimation

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A robust firefly algorithm with backpropagation neural networks for solving hydrogeneration prediction

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ELECTRICAL ENGINEERING 2018年第4期100卷 2617-2633页

作者： Hammid, Ali Thaeer Bin Sulaiman, Mohd Herwan Awad, Omar I. Univ Malaysia Pahang Fac Elect & Elect Engn Pekan 26600 Malaysia Al Yarmouk Univ Coll Dept Comp Engn Tech Baaqubah 32001 Diyala Iraq Univ Malaysia Pahang Fac Mech Engn Pekan 26600 Malaysia

Hydrogeneration prediction typically has composite structures such as nonlinearity, non-stationarity, and fluctuation, which converts its predicting to be very tough. The applications of backpropagation neural network (BPNN) are very various and saturated. The linear threshold part of the BPNN produces rapid learning with bounded abilities, also the procedure of BPNN causes the slow speed of training. The objective of this study, first, a firefly algorithm (FA) based on the k-fold cross-validation of BPNN has been suggested to predict data for keeping rapid learning and prevents the exponential increase in operating parts. Second, it is to construct on this method to improve an efficient process for prediction problems that can discover efficient solutions at a high speed of convergence. For this purpose, the suggested approach that makes a hybridizing the FA with the robust algorithm (RA), where RA is used to control the steps of randomness for the FA while optimizing the weights of the standard BPNN model. The algorithms were verified on an original dataset of the Himreen Lake Dam. The results display that the regression coefficient, root-mean-square error, mean absolute error, and mean bias error values of the suggested model are 99.86%, 1.87%, 0.91%, and 0.31%, respectively. Furthermore, the performance of the suggested robust firefly algorithm model is better than previously mentioned models in terms of speed and accuracy of prediction.

关键词： firefly algorithm Backpropagation neural networks Prediction problems Cross-validation

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New memetic self-adaptive firefly algorithm for continuous optimisation

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2016年第5期8卷 300-317页

作者： Galvez, Akemi Iglesias, Andres Univ Cantabria ETSI Caminos Canales & Puertos Dept Appl Math & Computat Sci Avda Castros S-N Santander 39005 Spain Toho Univ Fac Sci Dept Informat Sci 2-2-1 Miyama Funabashi Chiba 2748510 Japan

The firefly algorithm is a recent nature-inspired algorithm that is receiving increasing attention from the scientific community during the last few years. One of its most promising variants is given by the memetic self-adaptive firefly algorithm (MSA-FFA), recently introduced to solve combinatorial problems. In this paper we propose a modification of the original MSA-FFA for continuous optimisation problems. The most important features of our method are: the problem-dependent selection of control parameters for self-adaptation, a simple population model providing an adequate trade-off between exploration and exploitation, and the use of an adaptive-size Luus-Jaakola random local search. This new method is applied to solve a very difficult real-world continuous optimisation problem arising in geometric modelling and manufacturing. The paper also provides the first reliable, standardised benchmark for this optimisation problem. This benchmark is used for a comparative analysis of our method with respect to some of the most popular nature-inspired algorithms. Our results show that the proposed method outperforms previous approaches (including the standard firefly algorithm) for most of the instances in the benchmark.

关键词： firefly algorithm FFA continuous optimisation nature-inspired metaheuristic memetic approach breakpoint location problem

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Performance-based optimum seismic design of steel structures by a modified firefly algorithm and a new neural network

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ADVANCES IN ENGINEERING SOFTWARE 2015年第C期81卷 50-65页

作者： Gholizadeh, Saeed Urmia Univ Dept Civil Engn Orumiyeh Iran

Structural optimization for performance-based seismic design (PBSD) in earthquake engineering aims at finding optimum design variables corresponding to a minimum objective function with constraints on performance requirements. In this study, an efficient methodology, consisting of two computational strategies, is presented for performance-based optimum seismic design (PBOSD) of steel moment frames. In the first strategy, a modified firefly algorithm (MFA) is proposed to efficiently find PBOSD at the performance levels. Because that for computing the structural responses at the performance levels a nonlinear static pushover analysis must be conducted, the overall computational time of optimization process is extremely large. In the second strategy, to reduce the computational burden, a new neural network model termed as wavelet cascade-forward back-propagation (WCFBP) is proposed to effectively predict the results of nonlinear pushover analysis during the optimization process. To illustrate the effectiveness of the proposed methodology, 3, 6 and 12 storey planar steel moment resisting frames are optimized for various performance levels. The results demonstrate the effectiveness of the proposed soft computing-based methodology for PBOSD of steel structures spending low computational cost. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Structural optimization firefly algorithm Neural network Performance-based design Wavelet Earthquake

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Preference-Based Stepping Ahead firefly algorithm for Solving Real-World Uncapacitated Examination Timetabling Problem

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IEEE ACCESS 2024年 12卷 24685-24699页

作者： Nand, Ravneil Reddy, Emmenual Chaudhary, Kaylash Sharma, Bibhya Univ South Pacific Sch Informat Technol Engn Math & Phys Suva Fiji

Swarm-based intelligent optimization algorithms that employ the principles of collective behavior have been gaining traction as viable solutions in optimization research. One area of optimization is the Examination Timetabling Problem (ETP), which presents a significant challenge for many Higher Education Institutes (HEIs). This study proposes a novel approach for solving the Uncapacitated Examination Timetabling Problem (UETP), where a stepping-ahead mechanism is utilized with threshold acceptance in the firefly algorithm (FA). The proposed method improves exploration with the use of the stepping-ahead mechanism, while threshold acceptance allows for better exploitation of the search space. Initially, a neighborhood search mechanism is employed as the discretization of FA to improve solution quality, known as Kempe Chain-based neighborhoods. The proposed method is tested on seven UETP problems, with the results showing comparative performance to the best solutions available in the literature for the Toronto exam timetabling dataset. The selection of seven problems is made with exams totaling less than 400, this allows to create a manageable yet representative benchmark. The study further extends the experiment to a real-world dataset collected from an HEI. The use of a real-world dataset allows us to see the potential of the algorithm and at the same time evaluate its performance under realistic conditions and resource constraints. The proposed stepping-ahead mechanism has the potential for use in other domains, such as robotics and engineering. Overall, this paper presents a new methodology for solving the UETP that has the potential to offer superior results when compared to existing approaches.

关键词： Optimization methods Particle swarm optimization Benchmark testing Task analysis Schedules Education Behavioral sciences Time factors Mathematical models Scheduling Testing Educational institutions Performance analysis firefly algorithm optimization swarm intelligence uncapacitated examination timetabling problem

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Calculation of the inverse kinematics solution of the 7-DOF redundant robot manipulator by the firefly algorithm and statistical analysis of the results in terms of speed and accuracy

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INVERSE PROBLEMS IN SCIENCE AND ENGINEERING 2020年第5期28卷 601-613页

作者： Dereli, Serkan Koker, Rasit Sakarya Univ Sakarya Vocat High Sch Comp Technol Dept Sakarya Turkey Sakarya Univ Fac Technol Elect & Elect Engn Sakarya Turkey

In this study, the inverse kinematics solution of a 7-DOF redundant robot manipulator was performed by using firefly algorithm that is a swarm optimization technique. In order to show the power of this technique, a redundant robotic arm which is inadequate inverse kinematic solution by conventional methods has been chosen. Both speed and accuracy are two important factors in robotic studies. For this reason, the comparison of the method used in this study in terms of speed and accuracy has been carried out in depth. The scenario used is as follows: Firstly, the position equations of this manipulator are derived with the DH parameters. Afterward, the position of the end effector is obtained in the work space according to the forward kinematic calculation. Finally, the joint angles that will be directed to the calculated position values with the least error are obtained by the firefly algorithm and the obtained result is compared with other swarm algorithms such as particle swarm optimization and artificial bee colony.

关键词： firefly algorithm swarm intelligence inverse kinematics redundant robot manipulator 7-dof

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