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I/F-Race tuned firefly algorithm and particle swarm optimization for K-medoids-based clustering

EVOLUTIONARY INTELLIGENCE 2023年第1期16卷 351-373页

作者： Khennak, Ilyes Drias, Habiba Drias, Yassine Bendakir, Faysal Hamdi, Samy USTHB Lab Res Artificial Intelligence Algiers Algeria Algiers Univ Algiers Algeria

Clustering is still one of the most common unsupervised learning techniques in data mining since it allows the discovery of meaningful and interesting patterns, knowledge, rules and associations from large-scale datasets. K-medoids, a variant of K-means, is a popular clustering method that attempts to find the optimal combination of K medoids from among a set of potential combinations. It has been successfully applied to solve various real-life problems owing to its simplicity and effectiveness. Nevertheless, due to the exponential number of possible combinations of K medoids, it is extremely challenging to produce the optimal one within a reasonable amount of time. Therefore, in this work, we propose to formulate the problem of K-medoids clustering as an optimization problem and then combine two effective and powerful Swarm Intelligence (SI) algorithms, namely firefly algorithm (FA) and Particle Swarm Optimization (PSO), to select the appropriate combination of K medoids. We extensively evaluate the proposed FA-PSO for K-medoids-based clustering, abbreviated as FA-PSO-KMED, using 10 UCI datasets. We first use the Iterated F-Race (I/F-Race) algorithm to determine the optimal parameter settings for FA and PSO. Then, we compare the results of the proposed FA-PSO-KMED with those obtained using the well-known state-of-the-art K-medoids-based clustering algorithms: PAM, CLARA and CLARANS. We also compare the results with 11 popular swarm intelligence algorithms: PSO, ABC, CS, FA, BA, APSO, EHO, HHO, SMA, AO and RSA. Experimental results and statistical analysis show that the proposed FA-PSO-KMED is very promising and demonstrates a significant improvement over the other clustering algorithms.

关键词： Clustering K-medoids Swarm intelligence firefly algorithm Particle swarm optimization Iterated F-Race algorithm

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Multi-criteria- Recommendations using Autoencoder and Deep Neural Networks with Weight Optimization using firefly algorithm

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INTERNATIONAL JOURNAL OF ENGINEERING 2023年第1期36卷 130-138页

作者： Spoorthy, G. Sanjeevi, S. G. NIT Warangal Dept CSE Warangal India

Demand for personalized recommendation systems elevated recently by e-commerce, news portals etc., to grab the customer interest on the sites. Collaborative filtering proves to be powerful technique but it always suffers from data sparsity, cold-start and robustness issues. These issues have been tackled by some approaches resulting in higher accuracy. Few of them take user profiles, item attributes and rating time as the side information along with ratings to give interpretative personalized recommendations. These type of approaches tries to find which factors mainly impacted the user to rate an item. Another approach extends the single-criteria ratings of collaborative filtering to multi-criteria ratings. Our approach exploits non-linear interpretative recommendations by exploring Multi-criteria ratings by combination of Autoencoders with dropout layer and firefly algorithm optimized weights for deep neural networks. Our approach solves data sparsity, scalability issues and fetch accurate recommendations. Experimental evaluations have been done using Yahoo! Movie and MovieLens datasets. Our approach outperforms in robustness and accuracy with respect to previous research works.

关键词： Multi-Criteria Recommendation Systems Autoencoder firefly algorithm Deep Neural Networks Deep Learning

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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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Optimization of Renewable Energy in North Sumatra Using firefly algorithm Method Towards Net Zero Emissions

Optimization of Renewable Energy in North Sumatra Using Fire...

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IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)

作者： Rimbawati Ambarita, Himsar Irwanto, M. Sitorus, Tulus Burhanuddin Abdullah, Ilmi Univ Sumatera Utara Fac Engn Mech Engn Medan Indonesia Univ Muhammadiyah Sumatera Dept Elect Engn Utara Medan Indonesia Univ Sumatera Utara Fac Engn Dept Mech Engn Medan Indonesia Univ Malaysia Perlis Dept Elect Engn Perlis Malaysia

ISBN: (数字)9798350372106

ISBN: (纸本)9798350372113;9798350372106

North Sumatra has quite abundant natural resources, which can be utilized as renewable energy sources, such as hydropower, geothermal, biomass, biogas, and solar. But until now it has not been used optimally. This study aims to optimize all renewable energy plants in North Sumatra using the firefly algorithm (FA) method. The analysis was conducted using descriptive statistics by comparing the actual power capacity of renewable energy against minimum values, maximum values, average values, and standard deviations. Where this test was carried out 5 times with some iterations of 100, 500, 2500, 12,500, and 62,500 using Matlab R2019a software. This study concluded that the best performance when optimized was microhydro power which reached the best object of 2724.1269 MW, followed by solar achieved the best objective of 2652.2975 MW. This shows that the development of renewable energy sources has significant potential to achieve the Net Zero Emissions target in North Sumatra.

关键词： firefly algorithm Hybrid Renewable Energy

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firefly algorithm for Structural Optimization Using ANSYS 2nd

Firefly Algorithm for Structural Optimization Using ANSYS

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2nd International Conference on Design Tools and Methods in Industrial Engineering (ADM)

作者： Marannano, Giuseppe Ricotta, Vito Univ Palermo Engn Dept Viale Sci I-90128 Palermo Italy

ISBN: (纸本)9783030912345;9783030912338

In the mid-1980s, several metaheuristic methods began to be developed for solving a very large class of computational problems with the aim of obtaining more robust and efficient procedures. Among them, many metaheuristic methods use bio-inspired intelligent algorithms. In recent years, these methods are becoming increasingly important and they can be used in various subject areas for solving complex problems. firefly algorithm is a nature-inspired optimization algorithm proposed by Yang to solve multimodal optimization problems. In particular, the method is inspired by the nature of fireflies to emit a light signal to attract other individuals of this species. In this work, a numerical study for solving a structural problem using the firefly algorithm as optimization method is conducted. In particular, the implementation of the firefly algorithm in several input files realized in the ANSYS Parametric Design Language has allowed the definition of the optimal stacking sequence and the laminate thickness of a composite gear housing used to enclose the components of a mechanical reducer.

关键词： firefly algorithm Structural optimization Finite element analysis ANSYS

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Analysis of Constructive Heuristics with Cuckoo Search algorithm, firefly algorithm and Simulated Annealing in Scheduling Problems 32nd

Analysis of Constructive Heuristics with Cuckoo Search Algor...

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32nd International Conference on Flexible Automation and Intelligent Manufacturing

作者： Moreira, Carlota Costa, Catarina Santos, Andre S. Madureira, Ana M. Barbosa, Marta Polytech Inst Porto ISEP IPP Inst Engn Porto Portugal Polytech Inst Porto ISEP IPP Interdisciplinary Studies Res Ctr ISRC Porto Portugal

ISBN: (纸本)9783031381645;9783031381652

Nowadays, decision making is one of the most important and influential aspects of everyday life, and the application of metaheuristics and heuristics facilitates the process. Thus, this paper presents a performance analysis of the combination of constructive heuristics used to generate initial solutions for metaheuristics applied to scheduling problems. Namely, Nawaz, Enscore, and Ham Heuristic (NEH), Palmer Heuristic and Campbell, Dudek, and Smith Heuristic (CDS) with Cuckoo Search, firefly algorithm and Simulated Annealing. The aim is to compare the performance of these combinations to analyse the efficiency, effectiveness and robustness of each. All combinations were analysed in an in-depth computational study and then subjected to a statistical study to support an accurate analysis of the results. The results of the analysis show that the firefly algorithm associated with NEH, despite having a high runtime, performs better than the other combinations. However, the best effectiveness-efficiency ratio corresponds to SA-Palmer and SA-CDS.

关键词： Nawaz, Enscore, and Ham Palmer Campbell, Dudek, and Smith firefly algorithm Cuckoo Search algorithm Simulated Annealing

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Optimization of S-N curve fitting based on neighborhood rough set reduction with improved firefly algorithm

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FRATTURA ED INTEGRITA STRUTTURALE-FRACTURE AND STRUCTURAL INTEGRITY 2023年第64期17卷 250-265页

作者： Li, Yangjinyu Zou, Li Zhu, Zhengjie Dalian Jiaotong Univ Software Technol Inst Dalian Peoples R China Dalian Jiaotong Univ Liaoning Key Lab Welding & Reliabil Rail Transport Dalian Peoples R China Dalian Jiaotong Univ Dalian Key Lab Welded Struct & Its Intelligent Mfg Dalian Peoples R China

In order to reduce the S-N curve dispersion of titanium alloy welded joints and improve the prediction accuracy of fatigue life, a novel optimization method of S-N curve fitting based on neighborhood rough set reduction with improved firefly algorithm (IFANRSR) is proposed. Firstly, we propose an improved firefly algorithm (IFA) by updating the position and step size, combining IFA algorithm and neighborhood rough set into an IFANRSR algorithm for attribute reduction. Then, according to the fatigue data of titanium alloy welded joints, the fatigue decision system of welded joints is established, and the key factors affecting the fatigue life of welded joints are determined. Next, according to the set of key influencing factors obtained based on IFANRSR algorithm, the fatigue characteristics domains are divided, and the S-N curves are fitted on each fatigue characteristics domain, to obtain a group of S-N curves. To demonstrate the effectiveness of IFA algorithm, six benchmark functions are used, then the availability of IFANRSR algorithm is evaluated in comparison with other algorithms on four UCI datasets. Finally, the results of the goodness-of-fit show that the dispersion of fatigue data is reduced, which can effectively improve the prediction accuracy of fatigue life.

关键词： S-N curve Fatigue characteristics domain Neighborhood rough set firefly algorithm

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Maximum Power Point Tracking on Photovoltaic System Based On Fusion firefly algorithm And Fuzzy Logic Controller for Charging Lead Acid Battery 26

Maximum Power Point Tracking on Photovoltaic System Based On...

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26th International Electronics Symposium (IES) on Shaping the Future - Society 5.0 and Beyond

作者： Navi, Rizki Amrullah Prasetyono, Eka Anggriawan, Dimas Okky Suhariningsih Sunarno, Epyk Politekn Elekt Negeri Surabaya Dept Elect Engn Surabaya Indonesia

ISBN: (纸本)9798350392005;9798350391992

This paper discusses Maximum Power Point Tracking (MPPT) on Photovoltaic (PV) systems which is applicate to charging lead acid batteries. MPPT utilized an interleaved boost converter which is that controlled with Fusion firefly algorithm (FFA) and Fuzzy Logic Controller (FLC) for lead acid battery charging. FFA is utilizing for MPPT because it is able to obtain Maximum Power Point (MPP) quicker and accurately than another firefly algorithm (FA). To avoid overcharging due to the charging voltage and current exceeding the maximum value of the battery once MPP is obtained, a FLC is used to limit the output converter value. To optimize the charging process, the charging mode is used a Constant Current - Constant Voltage ( CC-CV) which is controlled using FLC. Based on simulation, FFA performs much better in finding MPP with less oscillation as compared to Simplified firefly algorithm (SFA) and Neighborhood Attraction firefly algorithm (NaFA). MPPT with FFAFLC can find MPP quickly and accurately with 99.9% efficiency and prevent overcharging by keeping the voltage and current from exceeding 3A - 28.8V.

关键词： MPPT Interleaved Boost Converter Lead Acid Charger firefly algorithm

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Physical Discomfort Analysis of Farmers During Plucking Vegetable Using firefly algorithm

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Operations Research Forum 2025年第1期6卷 1-22页

作者： Chauhan, Hullash Satapathy, Suchismita Pandey, Anish Sahoo, Ashok K. School of Mechanical Engineering KIIT Deemed to Be University Patia Bhubaneswar 751024 India

Agricultural workers, during farming, have to perform many activities in hot summer, heavy rain, and in cold winter. The activities performed require a high physical workload. Farming activities are repetitive in nature and are continuously performed for 7 to 8 h. So, in many cases, farmers suffer from discomfort and body pain while performing farming activities. Hence, the rate of hazards, accidents, and MSDs (musculoskeletal disorders) is very high due to discomfort and needs ergonomic analysis. Suzanne Rodgers, an ergonomic tool, has applied after taking out a complete literature survey and studying plucking activity. Further, the firefly algorithm has been used to predict the discomfort levels of farmers during the plucking of vegetables in a static posture in agricultural fields based on body parts such as shoulders, arms, back, hand, legs, and feet, respectively. The output of the prediction shows that discomfort is maximum when plucking vegetables, and the firefly algorithm result shows the best optimal value of 1.4629;it shows that the predicted discomfort level in the model for farmers are highly impacting the body parts and necessary action is essential for their safety. The correlation results of body parts such as shoulders, arms, back, hands, legs, and feet show that the leg has the highest correlation. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Agriculture fields Discomfort Farmers firefly algorithm Suzanne Rodgers

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Application of revised firefly algorithm and grey wolf optimisation on keystroke dynamics

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INTERNATIONAL JOURNAL OF BIOMETRICS 2023年第3-4期15卷 480-504页

作者： Baynath, Purvashi Khan, Maleika Heenaye-Mamode Univ Mauritius Elect & Elect Engn Reduit Mauritius Univ Mauritius Software & Informat Secur Reduit Mauritius

In this digitalised world, to countermeasure computational threats, keystroke dynamics (KD) is one potential biometric feature that is used to enforce security over a network. Feature subset selection (FSS) process further aid for the increase of security by selecting the appropriate features, which make the replication of pattern difficult. For this purpose, two commonly known algorithms namely firefly algorithm (FA) and grey wolf algorithm (GWA) are being enhanced by incorporating chaos and pheromone in the network architecture. The experimental results have shown the robustness of the revised algorithms of firefly and grey wolf where optimum values for false acceptance rate (FAR) are being achieved. Besides, this study has shown that the revised FA fits better as a FSS technique by outperforming previous proposed solutions in terms of recognition rate (RR), where above 95% has been achieved, which shall aid in the reduction of attacks.

关键词： biometric feature subset selection FSS firefly algorithm FA grey wolf optimisation GWO keystroke dynamics KD machine learning ML

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