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Dual-population firefly algorithm Based on Gender Differences for Detecting Protein Complexes

ENGINEERING LETTERS 2024年第5期32卷 1062-1072页

作者： Zhang, Qiwen Guo, Xinxin Lanzhou Univ Technol Lanzhou Gansu Peoples R China Lanzhou Univ Technol Elect Informat Lanzhou Gansu Peoples R China

address the problem of high false positive/negative rate and low accuracy in protein complex detection, we propose the Dual-population firefly algorithm Based on Gender Differences (DFAGD) based on the unique core-attachment structure of protein complexes and the biological properties of fireflies. This method divides the detection of protein complexes into two phases. Firstly, a global search of the male population is used to detect the core proteins, and then a local search of the female population is used to detect the attachment proteins, which improves the accuracy of detection. In the male population strategy, the population diversity is redefined, and when the diversity falls below the threshold, a spring model is introduced to bring the population into the repulsion phase so that it does not fall into a local optimum. The female population selects elite and excellent individuals from the detection results of the male population to perform guided neighborhood searches, which can effectively improve detection accuracy. Finally, the effectiveness of the protein complex detection method is tested by comparing it to eight classical detection methods using four datasets of Saccharomyces cerevisiae proteins.

关键词： protein complex firefly algorithm dual-population core-attachment structure spring model

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firefly algorithm for Beamforming Design in RIS-aided Communication Systems 97

Firefly Algorithm for Beamforming Design in RIS-aided Commun...

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97th IEEE Vehicular Technology Conference (VTC-Spring)

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

ISBN: (纸本)9798350311143

This paper studies a non-convex power minimization problem for reconfigurable-intelligent-surfaces-aided communication systems whose constraints are multivariate functions of two independent optimization variables, i.e., active and passive beamforming vectors. A widely adopted alternative optimization (AO) approach approximates the originally non-convex problem by two convex sub-optimization problems where each suboptimization problem deals with one variable considering the other variable as a constant. The solution for the original problem is obtained by iteratively solving these sub-optimization problems. Although the AO approach converts the original NP-hard optimization problem to two convex sub-problems, the solutions attained by this method may not be the global optimal solution due to the approximation process as well as the inherent non-convexity of the original problem. To overcome the issue, this paper adopts a nature-inspired optimization approach and introduces a novel firefly algorithm (FA) to simultaneously solve for two independent optimization variables of the originally nonconvex optimization problem. Computational complexity analyses are provided for the proposed FA and the AO approaches. Simulation results reveal that the proposed FA approach prevails its AO counterpart in obtaining a better solution for the under-studied optimization problem with the same order of computational complexity.

关键词： firefly algorithm nature inspired optimization transmit beamforming reconfigurable intelligent surfaces

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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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Quantum-inspired firefly algorithm with ant miner plus for fake news detection

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INTERNATIONAL JOURNAL OF MODERN PHYSICS C 2024年第1期36卷

作者： Sharma, Kanta Prasad Manideep, A. Sai Kulkarni, Shailesh Gowrishankar, J. Choudhary, Binod Kumar Kaur, Jatinder Gehlot, Anita GLA Univ Dept Comp Engn & Applicat Mathura 281406 Uttar Pradesh India Vignans Fdn Sci Technol & Res Dept Management Studies Vadlamudi Andhra Pradesh India Vishwakarma Inst Informat Technol Dept Elect & Telecommun Engn Pune India JAIN Deemed Univ Sch Engn & Technol Dept Comp Sci Engn Bangalore Karnataka India ARKA Jain Univ Dept Elect & Elect Engn Ranchi Jharkhand India Vivekananda Global Univ Dept Elect Engn Jaipur 303012 Rajasthan India Uttaranchal Univ Uttaranchal Inst Technol Dept Elect & Commun Engn Dehra Dun 248007 India

Nowadays, technology has shifted the way individuals access news from conventional media sources to social media platforms. The active engagement of people with social media platforms leads them to consume news without confirming its source or legitimacy. This facilitated the dissemination of more manipulated and false information in the form of rumors and fake news. Fake news can affect public opinion and create chaos and panic among the population. Thus, it is essential to employ an advanced methodology to identify fake news with high precision. This research work has proposed the concept of the quantum-inspired firefly algorithm with the ant miner plus algorithm (QFAMP) for more effective fake news detection. The proposed QFAMP algorithm utilizes the attributes of quantum computing (QC), the firefly algorithm (FA), and the ant miner plus algorithm (AMP). Here, the QFA approach ensures the effective exploitation of the firefly agents until the agents are able to search for the brighter firefly. Further, the AMP algorithm utilizes the best ants with higher pheromone concentrations for global exploration, which also avoids the premature convergence of the QFA agents. In addition, the AMP algorithm serves as an efficient data mining variant that is effective for the classification of fake news. The efficacy of the proposed QFAMP algorithm is evaluated for the dataset of FakeNewsNet, which is composed of two sub-categories: BuzzFeed and PolitiFact. The experimental evaluations indicate the effective performance of the proposed algorithm compared to the other techniques.

关键词： Quantum computing metaheuristic algorithms firefly algorithm quantum firefly algorithm ant colony optimization ant miner plus fake news detection optimization

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Optimization DC-DC boost converter of BLDC motor drive by solar panel using PID and firefly algorithm

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RESULTS IN ENGINEERING 2024年 21卷

作者： Anshory, Izza Jamaaluddin, Jamaaluddin Wisaksono, Arief Sulistiyowati, Indah Hindarto Rintyarna, Bagus Setya Fudholi, Ahmad Rahman, Yuli Asmi Sopian, Kamaruzzaman Univ Muhammadiyah Sidoarjo Fac Sci & Technol Dept Elect Engn Sidoarjo Indonesia Natl Res & Innovat Agcy BRIN Res Ctr Energy Convers & Conservat Jakarta Indonesia Univ Muhammadiyah Sidoarjo Fac Sci & Technol Dept Informat Engn Sidoarjo Indonesia Univ Muhammadiyah Jember Dept Informat Engn Jember Regency Indonesia Univ Kebangsaan Malaysia Solar Energy Res Inst Bangi Selangor Malaysia Univ Tadulako Palu Indonesia Univ Teknol PETRONAS Dept Mech Engn Seri Iskandar 32610 Perak Darul Rid Malaysia

The use of solar photovoltaic panels as source of power for Brushless Direct Current (BLDC) motors requires a DCDC Converter circuit. One application of solar energy is as a power source for Brushless Direct Current (BLDC) motors. The main problem is the voltage fluctuation and low DC voltage generated by the solar panel. This research aims to improve the performance of the DC-DC Boost Converter circuit and minimize voltage fluctuations. The methodology encompasses mathematical modeling of the circuit in the form of transfer functions and optimizing the DC-DC Boost Converter circuit using the Proportional Integral Derivative (PID) controller and the firefly algorithm. Simulation testing results indicate an improvement in transient response performance of the DC-DC Converter circuit as a driver for the BLDC motor. This is evidenced by an increase in rise time from 499 s to 820 s, a decrease in settling time from 3.33 e+03 s to 2.07e+03 s, and a reduction in overshoot to 0 % from previously 11.4 %. The utilization of the firefly algorithm in optimization significantly enhances system efficiency, as demonstrated by faster achievement of stability without excessive oscillation and a reduction in the time required for the system to settle. Overall, this study shows that the firefly algorithm is effective in developing DC-DC Boost Converter circuits, improving system efficiency by reducing settling time and eliminating overshoot. These findings provide empirical evidence of the effectiveness of using artificial intelligence algorithms in enhancing the operational efficiency of energy conversion systems.

关键词： BLDC motor Solar panel DC-DC boost converter PID controller firefly algorithm

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IFA-EO: An improved firefly algorithm hybridized with extremal optimization for continuous unconstrained optimization problems

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SOFT COMPUTING 2023年第6期27卷 2943-2964页

作者： Chen, Min-Rong Yang, Liu-Qing Zeng, Guo-Qiang Lu, Kang-Di Huang, Yi-Yuan South China Normal Univ Sch Comp Sci Guangzhou 510631 Peoples R China Guangzhou Coll Commerce Sch Informat Technol & Engn Guangzhou 511363 Peoples R China Wenzhou Univ Natl Local Joint Engn Lab Digitalize Elect Design Wenzhou 325035 Peoples R China Jinan Univ Coll Cyber Secur Guangzhou 510632 Peoples R China Zhejiang Univ Inst Cyber Syst & Control Hangzhou 310018 Peoples R China

As one of the evolutionary algorithms, firefly algorithm (FA) has been widely used to solve various complex optimization problems. However, FA has significant drawbacks in slow convergence rate and is easily trapped into local optimum. To tackle these defects, this paper proposes an improved FA combined with extremal optimization (EO), named IFA-EO, where three strategies are incorporated. First, to balance the tradeoff between exploration ability and exploitation ability, we adopt a new attraction model for FA operation, which combines the full attraction model and the single attraction model through the probability choice strategy. In the single attraction model, small probability accepts the worse solution to improve the diversity of the offspring. Second, the adaptive step size is proposed based on the number of iterations to dynamically adjust the attention to the exploration model or exploitation model. Third, we combine an EO algorithm with powerful ability in local-search into FA. Experiments are tested on two group popular benchmarks including complex unimodal and multimodal functions. Our experimental results demonstrate that the proposed IFA-EO algorithm can deal with various complex optimization problems and has similar or better performance than the other eight FA variants, three EO-based algorithms, and one advanced differential evolution variant in terms of accuracy and statistical results.

关键词： firefly algorithm extremal optimization Probability choice strategy Adaptive step size Continuous optimization problems

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Automatic EEG channel selection for multiclass brain-computer interface classification using multiobjective improved firefly algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第4期82卷 5405-5433页

作者： Tiwari, Anurag Chaturvedi, Amrita IIT BHU Dept Comp Sci & Engn Varanasi Uttar Pradesh India

Multichannel Electroencephalography-based Brain-Computer Interface (BCI) systems facilitate a communicating medium between the human brain and the outside world. BCI systems aim to translate human intentions into computer-based control commands by decoding the respective brain patterns. Moreover, Electroencephalography (EEG) analysis involves high-dimensional features that increase the computational burden of applied signal processing approaches. To minimize this overload caused by a large set of channels, we propose an automatic EEG channel selection method for multiclass Motor-Imagery (MI) classification. In this study, we developed a hybrid channel ranking procedure using Fisher information and the objective firefly algorithm (FA). Firstly, the preprocessed neural signals are used to extract spatial-temporal features using the Regularized Common Spatial Pattern with Aggregation (RCSPA) method. Then, objective FA with two input variables (Spectral Entropy and Lyapunov exponent) is used to compute a weighted score for each channel in the neighborhood of a candidate solution. Finally, a novel Channel Set Relevance Index (CSRI) is developed to rank channels using their respective weighted score and Fisher information. The RCSPA features of highly ranked channels are employed to discriminate different MI-tasks using the Regularized Support Vector Machine (RSVM) classifier. The proposed approach is cross-validated on three publicly available BCI competition datasets (BCI Competition IV- 2008 - IIA, BCI Competition IV- dataset 1, BCI competition III - dataset IVa) with varying numbers of channels. The validation results show that the proposed method achieved a superior classification accuracy (83.97% on dataset 1, 80.85% on dataset 2, and 84.19% on dataset 3) with fewer channels than other baseline methods. In addition, our method significantly reduced the BCI preparation time, making it effective to conduct multiple experimental sessions for a large pool of subjects.

关键词： Brain-computer Interface firefly algorithm Spectral entropy Lyapunov exponent Regularized common spatial pattern with aggregation Fisher information Regularized support vector machine

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An improved multi-objective firefly algorithm for energy-efficient hybrid flowshop rescheduling problem

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JOURNAL OF CLEANER PRODUCTION 2023年第1期385卷

作者： Wang, Ziyue Shen, Liangshan Li, Xinyu Gao, Liang Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

Hybrid flowshop scheduling problem is a hot research topic, and is widely applied for production shop or line in chemical industry, metallurgical industry, semiconductor manufacturing and other industries. However, on the one hand, the uncertain events are inevitable in actual production, which will disrupt the production plan. On the other hand, nowadays the energy problem becomes more and more serious, and attracts much attention in the manufacturing industry. Therefore, an energy-efficient hybrid flowshop rescheduling problem under the machine breakdown is addressed in this paper. Firstly, the mathematical model for the problem is established, and an energy saving strategy based on problem model is designed, which can ensure the reduction of energy consumption without affecting the production efficiency. Then, an improved multi-objective firefly algorithm is proposed to optimize the production efficiency, energy consumption and production stability. To express the problem characteristics, a two-level encoding mechanism is used to describe the individual, and a corresponding decoding mechanism is presented to generate the scheduling scheme. By simulating the location updating law of the fireflies, the population updating rule is designed, in which the variable neighborhood search is employed to avoid the local optimal. To ensure the quality of the solution set, the fast non-dominated sorting method and elite individual reserving strategy are introduced to the population evolution. Finally, the numerical experimental results indicate that the designed energy saving strategy is effective, and the proposed algorithm obtains better Pareto frontier and performs the better convergence and diversity comparing with MOEA/D and NSGA-II, the common algorithms to solve complex multi-objective optimization problem.

关键词： Hybrid flowshop scheduling problem Hybrid flowshop rescheduling problem Energy -efficient scheduling Multi -objective optimization firefly algorithm

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Data mining-based firefly algorithm for green vehicle routing problem with heterogeneous fleet and refueling constraint

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ARTIFICIAL INTELLIGENCE REVIEW 2023年第7期56卷 6557-6589页

作者： Behnamian, J. Ghadimi, M. Farajiamiri, M. Ali Sina Univ Dept Ind Engn Hamadan Iran Rhein Westfal TH Aachen Dept Operat Management Aachen Germany

One of the major reasons for the importance of green vehicle routing is that current distribution systems are not sustainable in the long run. Because currently, only conventional economic costs are considered and environmental impacts of logistics strategies are not considered to be an important issue. Green vehicle routing emphasizes the use of clean fuels as an important factor in reducing the environmental pollution. However, due to the shortage of fuel stations providing these types of fuels, careful planning is needed considering the location and time of refueling of vehicles. In this regard, here, it is assumed that vehicles have limited fuel and should go to refueling stations and so fuel stations have also been added to the nodes of the classic vehicle routing problem. In this paper, a mathematical formulation is presented for a green heterogeneous vehicle routing problem and a firefly algorithm is developed to solve it in large-size instances. Two approaches have been used in applying the firefly algorithm. The first approach is the basic firefly algorithm and the second one is the firefly algorithm based on data mining in which the decision tree method reduces solution space and makes smarter fireflies movement. Finally, we have shown that using data mining has a significant effect on improving the performance of the firefly algorithm and by using a decision tree, the efficiency of the algorithm is significantly improved.

关键词： Green vehicle routing problem Heterogeneous fleet of vehicles Fueling station firefly algorithm Data mining

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A novel multilevel color image segmentation technique based on an improved firefly algorithm and energy curve

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EVOLVING SYSTEMS 2023年第4期14卷 685-733页

作者： Guo, Qiuping Peng, Hao Yunnan Normal Univ Pan Asia Business Sch Kunming 650092 Yunnan Peoples R China

Color image multilevel thresholding segmentation based on metaheuristic optimization algorithm and histogram has been widely used in many fields, but histogram neglects the relationships between the neighborhood pixels and any correlation among gray levels of the image when selecting the optimum threshold values. Therefore, in this paper, a novel and efficient color image multilevel thresholding method is proposed which uses an energy function to generate the energy curve of an image by considering spatial contextual information of the image. The presented color image segmentation technique based on energy curve takes the between class variance, Tsallis entropy and Kapur's entropy as objective functions. In order to further enhance the segmentation performance, an improved firefly algorithm (IFA) is presented in this paper. The IFA algorithm based on energy function is used for color image multilevel thresholding problem and compared with modified firefly algorithm (MFA), cuckoo search (CS), grasshopper optimization algorithm (GOA), Harris hawks optimization (HHO), emperor penguin optimization (EPO) algorithms. The experimental results are presented in terms of optimal threshold value, optimal objective function values, peak signal to noise (PSNR), structural similarity index (SSIM), standard deviation of the objective values and statistical results. The experimental results show that the presented method outperforms the other algorithms and Kapur's entropy based on energy curve is better than between class variance and Tsallis entropy for color image multilevel thresholding segmentation.

关键词： Image segmentation Color image multilevel thresholding Energy curve firefly algorithm

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