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FA-Fuzz: A Novel Scheduling Scheme Using firefly algorithm for Mutation-Based Fuzzing

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2024年第1期50卷 1-15页

作者： Gao, Zicong Xiong, Hao Dong, Weiyu Chang, Rui Yang, Rui Zhou, Yajin Jiang, Liehui State Key Lab Math Engn & Adv Comp Zhengzhou 450000 Peoples R China Informat Engn Univ Sch Cyber Sci & Engn Zhengzhou 450000 Peoples R China Zhejiang Univ Hangzhou 310007 Peoples R China Ant Grp Hangzhou 310007 Peoples R China

Mutation-based fuzzing has been widely used in both academia and industry. Recently, researchers observe that the mutation scheduling scheme affects the efficiency of fuzzing. Accordingly, they propose PSO algorithm or machine learning-based technique to optimize the scheduling process. However, these methods fail to consider the fact that the optimal operator distribution of different seeds is different, even for the same program. In this paper, we propose a novel general scheduling scheme, named FA-fuzz, to find the optimal selecting probability distribution of mutation operators, which is based on the observations that the effective mutation operators are different for different seeds. Specifically, our method is based on the firefly algorithm. The positions of fireflies are mapped to the selection probability distribution of different mutation operators. The brightness of fireflies is expressed as the efficiency of discovering unique testcases. We implement prototype systems on multiple state-of-art fuzzers, and perform evaluations on two datasets. Our proposed method improves both the number of unique paths and unique bugs on real-world datasets. In addition, we discover 30 zero-day vulnerabilities in eight real-world programs, which demonstrate the effectiveness of FA-fuzz.

关键词： Mutation-based fuzzing firefly algorithm

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Generalized firefly algorithm for Optimal Transmit Beamforming

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第6期23卷 5863-5877页

作者： Le, Tuan Anh Yang, Xin-She Middlesex Univ Fac Sci & Technol London NW4 4BT England

This paper proposes a generalized firefly algorithm (FA) to solve an optimization framework having objective function and constraints as multivariate functions of independent optimization variables. Four representative examples of how the proposed generalized FA can be adopted to solve downlink beamforming problems are shown for a classic transmit beamforming, cognitive beamforming, reconfigurable-intelligent-surfaces-aided (RIS-aided) transmit beamforming, and RIS-aided wireless power transfer (WPT). Complexity analyzes indicate that in large-antenna regimes the proposed FA approaches require less computational complexity than their corresponding interior point methods (IPMs) do, yet demand a higher complexity than the iterative and the successive convex approximation (SCA) approaches do. Simulation results reveal that the proposed FA attains the same global optimal solution as that of the IPM for an optimization problem in cognitive beamforming. On the other hand, the proposed FA approaches outperform the iterative, IPM and SCA in terms of obtaining better solution for optimization problems, respectively, for a classic transmit beamforming, RIS-aided transmit beamforming and RIS-aided WPT.

关键词： Array signal processing Optimization Signal to noise ratio Iterative methods Interference Brightness Statistics firefly algorithm nature-inspired optimization transmit beamforming reconfigurable intelligent surfaces

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An integrated firefly algorithm for the optimization of constrained engineering design problems

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SOFT COMPUTING 2024年第4期28卷 3207-3250页

作者： Tao, Ran Zhou, Huanlin Meng, Zeng Liu, Zhaotao Hefei Univ Technol Sch Civil Engn Hefei 230009 Anhui Peoples R China

As a nature-inspired metaheuristic method, the firefly algorithm (FA) arises more attentions in academic and engineering fields. However, too much attraction in FA's global attraction model leads to low computational efficiency, and the stochastic model with fixed randomization parameter is hard to balance the exploitation and exploration of the algorithm. Thus, FA still needs improvement to deal with complex engineering problems. An integrated firefly algorithm (IFA) that combines two novel attractive models with a new stochastic model is proposed to improve the standard FA. Firstly, the attractive model and stochastic model of standard FA are investigated through theoretical analysis and numerical experiments. And the factors that affect the computational efficiency and accuracy of FA are revealed. Based on the analysis results, two new fitness-based update formulas for attractiveness parameter are constructed to avoid the invalidation. The proposed virtual attractive model and global best attractive model can reduce the computation complexity and enhance the exploitation ability. Moreover, an adaptive strategy is presented for the stochastic model to achieve a better balance between exploitation and exploration. The nonlinearly decreased model for the update of parameter alpha can adjust the population diversity through the iteration and ensure the convergence. Additionally, an adaptive penalty function method is developed to handle the constraints effectively. Then, the initial parameters are tested, and the best initial parameters corresponding to the optimal performance of IFA are obtained. The proposed algorithm is evaluated by CEC2015 hybrid composition and a set of classical functions. The numerical experimental results show that the proposed techniques can enhance the solution accuracy and accelerate the convergence speed. Finally, IFA and other metaheuristic algorithms are applied to solve five engineering design optimization problems with mixed variables

关键词： firefly algorithm Attractive model Stochastic model Adaptive penalty function Engineering design optimization

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Prediction of tool wear in milling process based on BP neural network optimized by firefly algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART E-JOURNAL OF PROCESS MECHANICAL ENGINEERING 2024年第5期238卷 2387-2403页

作者： Cheng, Yao-Nan Jin, Ying-Bo Gai, Xiao-Yu Guan, Rui Lu, Meng-Da Harbin Univ Sci & Technol Key Lab Adv Mfg & Intelligent Technol Minist Educ Harbin Peoples R China Harbin Univ Sci & Technol Key Lab Adv Mfg & Intelligent Technol Minist Educ Harbin 150080 Peoples R China

Long-time discontinuous contact is easy to cause tool wear during milling. To decrease the impact of severe wear on workpiece quality and processing efficiency, cutting tools should be replaced timely. Therefore, tool wear prediction is an important aspect in improving process efficiency, ensuring machining precision and realizing intelligent manufacturing. To boost the precision of online prediction of tool wear, this paper suggests a novel approach to monitor tool wear by optimizing backpropagation (BP) neural network via firefly algorithm (FA). Specifically, the progressive semi-soft threshold function is applied to the process of cutting force signal noise reduction, which reduces redundant signals and noise interference in the signal. Time-domain analysis, frequency-domain analysis, and wavelet packet decomposition are utilized, cutting force features are extracted, and Pearson correlation coefficient is used to sift out signal features that are highly connected to tool wear. The FA is used to improve the BP neural network's weights and thresholds. Through learning nonlinear mapping, relationship between tool flank wear and signal features is realized. A prediction model of tool wear of FA-BP is constructed. Milling experiments validate the prediction model in the milling process. The experimental outcomes confirm the precision and reliability of the method. In comparison to BP neural network, genetic algorithm optimized BP neural network and particle swarm optimization algorithm optimized BP neural network prediction method, it has a greater prediction accuracy and a stronger training impact, and has superior performance. The research results can give a theoretical foundation and technological assistance for predicting tool wear, which is crucial for improving workpiece quality, processing efficiency, and promoting intelligent development.

关键词： Tool wear prediction wavelet noise reduction sensitive feature extraction firefly algorithm FA-BP model

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Multi-objective firefly algorithm for enhanced balanced exploitation and exploration capabilities

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2024年第7期36卷 e7973-e7973页

作者： Liu, Lei Jiangxi Ind Polytech Coll Sch Elect & Informat Engn Nanchang Peoples R China Jiangxi Ind Polytech Coll Sch Elect & Informat Engn Nanchang 330096 Peoples R China

The multi-objective firefly algorithm has a single strategy for finding the best in the evolutionary process, which is easy to fall into the local optimum and leads to poor distribution and convergence of the population. To address this problem, this article proposes an enhanced multi-objective firefly algorithm with balanced exploitation and exploration capability (MOFA-EBE). The convergence evaluation index is introduced to divide the population into two sub-regions according to the difference of convergence, namely, the development area and exploration area, and each sub-region is assigned its learning strategy to maximize the utilization of population information. Since the individuals in the development region are far from the Pareto front, the Levy flights mechanism is added to expand the search area and make them approach the Pareto front quickly under the guidance of the convergent global optimal particles to improve the convergence of the algorithm;since the individuals in the exploration region already have better convergence, they are assigned the most diverse and convergent global individuals for guidance and the Cauchy The variation mechanism is added to the Pareto frontier for continuous exploration to improve the distributivity of the algorithm. In the experimental part, the algorithm is compared with some multi-objective optimization algorithms on 19 benchmark test functions, and the effectiveness of the added strategy of MOFA-EBE is verified. The results show that MOFA-EBE is significantly superior to several other algorithms in terms of improving population convergence and distributivity.

关键词： Cauchy mutation firefly algorithm Levy flights multi-objective optimization regional division

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firefly algorithm based LCL filtered grid-tied STATCOM design for reactive power compensation in SCIG based Micro-grid

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ENERGY REPORTS 2022年 8卷 723-740页

作者： Saxena, Nitin Kumar Gupta, Atma Ram Mekhilef, Saad Gao, Wenzhong D. Kumar, Ashwani Gupta, Varun Netto, Roberto Silva Kanungo, Abhas KIET Grp Inst Elect & Elect Engn Dept Delhi Ncr Ghaziabad India Natl Inst Technol Elect Engn Dept Kurukshetra Haryana India Swinburne Univ Technol Sch Software & Elect Engn Melbourne Vic Australia Univ Tenaga Nas Coll Engn Dept Elect Engn Kajang Selangor Malaysia Univ Malaya Dept Elect Engn Power Elect & Renewable Energy Res Lab Kuala Lumpur Malaysia Denver Univ Denver CO USA Ctr Univ Facens Sorocaba Brazil KIET Grp Inst Elect & Commun Engn Dept Delhi Ncr Ghaziabad India

Microgrids are facing several operational and control issues while integrating with the grid. To deal with it, STATCOM, as one of the emerging power converter circuits, is connected with such microgrids. STATCOM with microgrid introduces current harmonic, inherent resonance, and active power losses related to high switching frequency. Filter design can help attenuate these effects and maintain the predefined standards as in IEEE 519-1992 and IEEE P1547.2-2003. There are also some more points of concern about filter design. Inappropriate filter types and parameters may cause worse filtering, reactive power surplus production, and low power factor. Therefore, this paper suggests that the reactive power compensation capability, as a local area problem, must be attended through the proper designing of LCL filtered grid-tied STATCOM. The parameters are estimated through transient analysis, power quality, and power balance studies for proposed Micro-grid and the results obtained are compared using nature-inspired algorithms such as GA, PSO, and FA over conventional mathematical formulation. The main contributions of this work are;(i) study of system with the inclusion of voltage and frequency-dependent load, (ii) modified STATCOM model clubbing reactive power control feedback and LCL filter with damping resistance, (iii) real and reactive power tracking system using dynamic compensator capabilities, and (iv) suppression of total harmonic distortions along with real and reactive power tracking using advanced tuning for filter parameters with GA, PSO, and FA based algorithms. (C) 2022 The Author(s). Published by Elsevier Ltd.

关键词： STATCOM Microgrid SCIG LCL filter Genetic algorithm Particle Swarm Optimization firefly algorithm

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Optimal allocation planning of regional water resources with multiple objectives using improved firefly algorithm

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AQUA-WATER INFRASTRUCTURE ECOSYSTEMS AND SOCIETY 2024年第4期73卷 746-770页

作者： Yang, Zhiling Wang, Zhaocai Yao, Zhiyuan Bao, Xiaoguang Minist Water Resources China Inst Water Resources & Hydropower Res Key Lab Sediment Sci & Northern River Training Beijing 100038 Peoples R China Shanghai Ocean Univ Coll Informat Shanghai 201306 Peoples R China

Population growth and economic development, coupled with water pollution and the frequent occurrence of extreme weather, have led to a growing contradiction between water supply and demand in some regions. To address this challenge, rational and optimal allocation of regional water resources has emerged as a crucial approach. This study focuses on creating a comprehensive model for optimizing regional water resource allocation, taking into account social, economic, and ecological factors. In addition, three innovative modifications are introduced to the firefly algorithm (FA), resulting in the development of the improved firefly algorithm (IFA). The effectiveness of IFA is validated through experiments involving nine benchmark functions. The results highlight the improved search efficiency and convergence achieved by IFA compared to other intelligent algorithms. Moreover, the application of IFA in solving the water resource allocation challenge in Shannxi Province, China, for 2020 and 2021 demonstrates a reduction in the overall water shortage rate to 4.69 and 1.72%, at a 75% guarantee rate. This reduction in water shortages contributes to addressing future scarcities. The proposed allocation scheme offers comprehensive benefits and provides crucial technical support for water resource management. Ultimately, this study offers valuable insights and guidance for addressing the issue of water supply-demand disparities.

关键词： firefly algorithm multi-objective optimization optimal allocation of water resources rotation iterative structure step size adaptive reduction

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Single Depot Multiple Travelling Salesman Problem Solved With Preference-Based Stepping Ahead firefly algorithm

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IEEE ACCESS 2024年 12卷 26655-26666页

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

firefly algorithm (FA) mimics the flashing light characteristic of fireflies to solve optimization problems. An area where its utilization is limited is Travelling Salesman Problem (TSP). There are a number of algorithms utilized to solve the problem;however, there is still scope to do better in terms of solution quality. In this study, stepping ahead FA is proposed to solve the single depot Multiple Travelling Salesman Problem (MTSP) with threshold strategy. Rooted in the discrete FA (dFA), dFA-Step introduces a discrete transformation and threshold strategy to enhance optimization. The algorithm combines a unique stepping ahead mechanism with a threshold acceptance preference operator, achieving a balance between exploration and exploitation. The deterministic threshold acceptance approach facilitates the selection of sub-best solutions while the integration of neighborhood operators, like reverse cyclic permutation and swap transformation, enables exploration of solutions superior and closely aligned with the best solutions. The experimental results are compared with selected novel works from the literature where the results show competitive performance of the proposed algorithm in terms of solution quality. The proposed method gives insight in preference operator and stepping ahead mechanism for other researchers to utilize in discrete domains such as robotics and scheduling.

关键词： Optimization methods Genetic algorithms Urban areas Traveling salesman problems Space exploration Behavioral sciences Tuning Combinatorial mathematics Particle swarm optimization Combinatorial problem firefly algorithm multiple travelling salesman problem swarm intelligence

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Parameter identification of Yoshida-Uemori combined hardening model by using a variable step size firefly algorithm

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MATERIALS TESTING 2024年第8期66卷 1292-1300页

作者： Sener, Bora Yildiz Tech Univ Mech Engn TR-34349 Istanbul Turkiye

The material behavior under cyclic loading is more complex than under monotonic loading and the usage of the sophisticated constitutive models is required to accurately define the elastoplastic behaviors of the advanced high-strength steels and aluminum alloys. These models involve the numerous material parameters that are determined from cyclic tests and accurate calibration of the variables has a great influence on the description of the material response. Therefore, the development of a precise and robust identification method is needed to obtain reliable results. In this study, a systematic methodology depending upon the firefly algorithm (FA) with variable step size has been developed and Yoshida-Uemori combined hardening model parameters of a dual-phase steel (DP980) and an aluminum alloy (AA6XXX-T4) are determined. The identified parameters are verified based on comparisons between the finite element simulations of the cyclic uniaxial tension-compression tests and experimental data and also the search performance of the variable FA is evaluated by comparing it with the standard FA. It is seen from these comparisons that variable FA can easily find and rapidly converge to the global optimum solutions.

关键词： Yoshida-Uemori hardening model finite element simulation cyclic loading firefly algorithm step size

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An optimal frequency regulation in interconnected power system through differential evolution and firefly algorithm

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SOFT COMPUTING 2024年第1期28卷 593-606页

作者： Mishra, Dillip K. Mohanty, Asit Ray, Prakash K. Clemson Univ Dept Automot Engn Clemson SC 29607 USA Eastern Acad Sci & Technol Dept Elect Engn Phulnakhara 754001 Odisha India Odisha Univ Technol & Res Dept Elect Engn Bhubaneswar 751003 Odisha India

Automatic generation control is extensively used to regulate power plants in a modern area of the power system network. In this paper, automatic generation and frequency control in interconnected power system is presented. A multisource such as thermal, hydro, and gas-based power plant is considered in this study, which is carried out by incorporating nonlinearity like generation rate constants, HVDC link, and the conventional PID controller design. Further, an optimal setting of the PID controller is performed by employing evolutionary, and metaheuristic algorithm-based approaches such as differential evolution and firefly algorithm, respectively. With these algorithms, the proposed model has been tested with their performance evaluation and comparison characteristics are discoursed. The robustness of the proposed controllers is assessed based on comparative analyses to regulate the interconnected power network's frequency profile under different loading conditions. The stability analysis is performed using the Eigen and Nyquist plots to assess the proposed controllers' efficacy. Besides, the frequency control study is summarized with comparative assessment through various performance indices such as settling time, peak overshoot and undershoots under different operating conditions. Finally, the proposed control scheme, in the interconnected power system, is validated through a real-time digital simulation platform, i.e., OPAL-RT 5142. The comparison of simulation and real-time results demonstrates the effectiveness of the FA-optimized PID controller in comparison with that of the DE-optimized PID controller.

关键词： Automatic generation control Differential evolution firefly algorithm Frequency control PID controller OPAL-RT

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