Article
Computer Science, Artificial Intelligence
Jia Zhao, Dandan Chen, Renbin Xiao, Juan Chen, Jeng-Shyang Pan, Zhihua Cui, Hui Wang
Summary: This paper proposes a multi-objective firefly algorithm with adaptive region division to address the issues of single optimization strategy and poor comprehensive performance of MOFA. By introducing distinct learning strategies and region divisions, the algorithm improves the exploration, development, and balance capabilities, and further enhances the comprehensive optimization performance through a fusion index.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Hu Peng, Wenhui Xiao, Yupeng Han, Aiwen Jiang, Zhenzhen Xu, Mengmeng Li, Zhijian Wu
Summary: This paper proposes a multi-strategy firefly algorithm with selective ensemble (MSEFA) to address the imbalance between exploration and exploitation in complex engineering optimization problems. The algorithm utilizes different search strategies at different stages of the search process and incorporates a selective ensemble method to improve performance. Experimental results demonstrate that MSEFA outperforms other FA variants and improved swarm intelligence algorithms in terms of solving complex engineering optimization problems.
APPLIED SOFT COMPUTING
(2022)
Article
Mathematical & Computational Biology
Chao Wang, Jian Li, Haidi Rao, Aiwen Chen, Jun Jiao, Nengfeng Zou, Lichuan Gu
Summary: This paper introduces a new multi-objective grasshopper optimization algorithm framework that achieves a balance between exploration and exploitation through grouping and co-evolution mechanisms, improving convergence and diversity.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2021)
Article
Food Science & Technology
Rekha Chaturvedi, Abhay Sharma, Anuja Bhargava, Jitendra Rajpurohit, Pushpa Gothwal
Summary: The Internet and its applications have led to a massive amount of data, particularly in the form of images, which has provided researchers with vast opportunities for data analysis. Image processing is crucial for improving the understanding of images, and various image processing steps can enhance images in different application areas. Many applications, such as medical imaging, face recognition, biometric security, fruit quality evaluation, and traffic surveillance, heavily rely on image analysis and segmentation. This paper focuses on multi-level thresholding for accurately segmenting different types of fruits, proposing a modified Firefly Algorithm (FA) that optimizes fuzzy parameters to obtain optimal thresholds. The algorithm utilizes levy flight and local search for improved performance. The proposed method is evaluated quantitatively and qualitatively on apple, banana, mango, and orange images using parameters like peak signal-to-noise ratio (PSNR) and structured similarity index metric (SSIM).
FOOD ANALYTICAL METHODS
(2022)
Article
Computer Science, Artificial Intelligence
Jia Zhao, Dandan Chen, Renbin Xiao, Zhihua Cui, Hui Wang, Ivan Lee
Summary: This paper proposes a multi-strategy ensemble firefly algorithm with equilibrium of convergence and diversity (MEFA-CD) to balance the convergence and diversity in multi-objective optimization. Experimental results show that the proposed algorithm outperforms other algorithms in terms of convergence and diversity.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Information Systems
Hu Peng, Wenhua Zhu, Changshou Deng, Zhijian Wu
Summary: The firefly algorithm (FA) is a nature-inspired heuristic optimization algorithm based on the luminescence and attraction behavior of fireflies. A novel courtship learning (CL) framework is proposed to enhance the performance of the FA by dividing the population into female and male subpopulations. Experimental results confirm that the proposed CL framework significantly enhances the performance of the original FA and advanced FA variants.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Xianchang Wang, Hongjia Ren, Xiaoxin Guo
Summary: This paper presents a novel method that uses a discrete firefly optimization algorithm to learn the structure of a Bayesian network. Experimental results show that the proposed algorithm has better convergence accuracy and higher scores compared to other algorithms, indicating its effectiveness for learning Bayesian network structures.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Information Systems
Djaafar Zouache, Adel Got, Deemah Alarabiat, Laith Abualigah, El-Ghazali Talbi
Summary: Feature selection is important in machine learning for improving classification capabilities and reducing dataset dimensionality. However, there are limited studies on multi-objective feature selection. In this paper, we propose a novel algorithm that combines quantum computing, Firefly Algorithm (FA), and Particle Swarm Optimizer (PSO) to address this problem. Our algorithm outperforms other algorithms in terms of feature subset size and classification accuracy, as demonstrated by our COVID-19 detection system.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Asma M. Altabeeb, Abdulqader M. Mohsen, Laith Abualigah, Abdullatif Ghallab
Summary: The study introduces a cooperative hybrid firefly algorithm to solve the capacitated vehicle routing problem (CVRP), which utilizes multiple firefly algorithm populations to collaborate, hybridizes with local search and genetic operators, and exchanges solutions among populations through communication, the results of experiments demonstrate the algorithm's outstanding performance compared to other methods.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Information Systems
Christy Jackson Joshua, Vijayakumar Varadarajan
Summary: With automobiles becoming the main form of transportation worldwide, the development of useful applications for safety and entertainment through vehicle communication has become essential. Vehicular adhoc networks (VANET) provide efficient communication among vehicles but face challenges due to unstable topology and frequent network disconnections. A new framework proposed aims to optimize network configuration by considering current system conditions and balancing network topology changes with Quality of Service (QoS) needs.
Article
Engineering, Electrical & Electronic
Seyed Ehsan Ahmadi, S. Mahdi Kazemi-Razi, Mousa Marzband, Augustine Ikpehai, Abdullah Abusorrah
Summary: This paper proposes a flexible multi-objective optimization approach to evaluate and deploy vehicle-to-grid and grid-to-vehicle technologies considering techno-economical and environmental factors. The simulations show significant reductions in operating costs and CO2 emissions, and improved voltage profile of the network. By implementing the discharging facility of PEVs, the PEV owners save a considerable amount in operating costs.
ELECTRIC POWER SYSTEMS RESEARCH
(2023)
Article
Spectroscopy
Mohamed B. El-Zeiny, Hossam M. Zawbaa, Ahmed Serag
Summary: This study introduces the grey wolf optimization (GWO) and antlion optimization (ALO) algorithms as variable selection tools in spectroscopic data analysis for the first time, showing that they select fewer variables than genetic algorithm (GA) and particle swarm optimization (PSO) algorithm in most cases while maintaining almost the same performance.
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
(2021)
Article
Computer Science, Information Systems
Tejna Khosla, Om Prakash Verma
Summary: The Bacterial foraging algorithm-firefly algorithm (BFA-FA) is a novel hybrid algorithm that improves the optimization performance in complex landscapes. It provides accurate solutions, avoids local optima, and works well on multimodal and multidimensional landscapes. The proposed algorithm shows statistically significant difference among other algorithms and demonstrates its robustness and applicability on engineering problems.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Thermodynamics
Hossein Akhlaghi Garmejani, Siamak Hossainpour
Summary: This study focuses on the optimization design of an automotive exhaust thermoelectric power generation system, analyzing the system performance through numerical algorithms and calculating key objective functions. It is found that multi-objective optimization methods can achieve good results in reducing investment while improving efficiency.
ENERGY CONVERSION AND MANAGEMENT
(2021)
Article
Multidisciplinary Sciences
Amin Mahdavi-Meymand, Mohammad Zounemat-Kermani, Wojciech Sulisz, Rodolfo Silva
Summary: This study develops machine learning models to predict wave run-up height and enhances the accuracy by employing optimization algorithms. The results indicate that these ML models are more accurate than empirical relations, with the GMDH-FA and GMDH-IWO models being recommended for applications in coastal engineering.
SCIENTIFIC REPORTS
(2022)
