Article
Mathematics
Nebojsa Bacanin, Ruxandra Stoean, Miodrag Zivkovic, Aleksandar Petrovic, Tarik A. Rashid, Timea Bezdan
Summary: An enhanced version of the firefly algorithm was proposed in this paper, addressing the drawbacks of the original method through an exploration mechanism and local search strategy. This algorithm was validated for selecting the optimal dropout rate for deep neural network regularization and also applied in image processing tasks.
Article
Computer Science, Artificial Intelligence
Luka Jovanovic, Nebojsa Bacanin, Miodrag Zivkovic, Milos Antonijevic, Bojan Jovanovic, Marija Bogicevic Sretenovic, Ivana Strumberger
Summary: The progress of Industrial Revolution 4.0 has been supported by recent advances in multiple domains, particularly the Internet of Things. Smart factories and healthcare have both benefited in terms of improved quality of service and productivity rate. However, security, intrusion, and failure detection pose significant concerns due to high dependence on IoT devices. Artificial intelligence, especially machine learning algorithms, are used to overcome these challenges by providing fault prediction, intrusion detection, and computer-aided diagnostics. However, the efficiency of machine learning models relies heavily on feature selection, predetermined hyper-parameter values, and training.
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)
Review
Computer Science, Artificial Intelligence
Zaid Abdi Alkareem Alyasseri, Osama Ahmad Alomari, Mohammed Azmi Al-Betar, Sharif Naser Makhadmeh, Iyad Abu Doush, Mohammed A. Awadallah, Ammar Kamal Abasi, Ashraf Elnagar
Summary: This paper reviews the research conducted using the bat-inspired algorithm (BA) from 2017 to 2021, summarizing its characteristics, development, and applications. The limitations of BA are also analyzed, and suggestions for future directions and improvements are given.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Mathematics
Dijana Jovanovic, Milos Antonijevic, Milos Stankovic, Miodrag Zivkovic, Marko Tanaskovic, Nebojsa Bacanin
Summary: This study proposes a hybrid machine learning and swarm metaheuristic approach for credit card fraud detection, using an improved firefly algorithm and three different machine learning models. Experimental results show that the method outperforms other competitor approaches in fraud detection.
Article
Computer Science, Artificial Intelligence
Jun Li, Xiaoyu Wei, Bo Li, Zhigao Zeng
Summary: This paper provides a timely review and analysis of the Firefly algorithm and its variants. The applications and case studies in the rapidly evolving domain are also reviewed and summarized in detail. The paper concludes by identifying future research directions and unresolved problems related to firefly algorithms.
Article
Computer Science, Artificial Intelligence
Yiying Zhang, Aining Chi, Seyedali Mirjalili
Summary: Jaya algorithm (JAYA) is a simple and widely used metaheuristic algorithm, but it may suffer from local optima in complex optimization problems. To enhance global search ability, an improved version called enhanced Jaya algorithm (EJAYA) is proposed, which utilizes both local exploitation and global exploration strategies.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Interdisciplinary Applications
Amir Seyyedabbasi, Farzad Kiani
Summary: The study introduces a new metaheuristic algorithm, SCSO, which mimics the behavior of sand cats. The algorithm performs well in finding good solutions and outperforms compared methods in various test functions and engineering design problems.
ENGINEERING WITH COMPUTERS
(2023)
Article
Computer Science, Artificial Intelligence
Changting Zhong, Gang Li, Zeng Meng
Summary: This paper presents a novel swarm-based metaheuristic algorithm called beluga whale optimization (BWO), which is inspired by the behaviors of beluga whales, for solving optimization problems. BWO consists of three phases: exploration, exploitation, and whale fall, corresponding to pair swim, prey, and whale fall behaviors, respectively. The self-adaptive balance factor and probability of whale fall in BWO play significant roles in controlling the exploration and exploitation capabilities. Additionally, Levy flight is introduced to enhance the global convergence in the exploitation phase. The effectiveness of BWO is evaluated using 30 benchmark functions and compared with 15 other metaheuristic algorithms through qualitative, quantitative, and scalability analysis. The results show that BWO is a competitive algorithm for solving unimodal and multimodal optimization problems. Furthermore, BWO achieves the first overall rank in the scalability analysis of benchmark functions among the compared metaheuristic algorithms. Four engineering problems are also solved to demonstrate the merits and potential of BWO in solving complex real-world optimization problems. The source code of BWO is publicly available.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Mathew Mithra Noel, Venkataraman Muthiah-Nakarajan, Geraldine Bessie Amali, Advait Sanjay Trivedi
Summary: The Firebug Swarm Optimization (FSO) algorithm, inspired by the reproductive swarming behavior of Firebugs, outperforms 17 popular state-of-the-art heuristic global optimization algorithms on benchmark tests, demonstrating its effectiveness in finding optimal solutions.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Hongjia Ren, Hongbo Ren, Zhongqi Sun
Summary: This paper proposes an improved Firefly Algorithm (HSFA) by using a hierarchy strategy to separate the firefly population into elite and non-elite groups and apply distinct attractive models for each group, leading to enhanced performance.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Engineering, Multidisciplinary
Yanqiang Tang, Chenghai Li, Song Li, Bo Cao, Chen Chen
Summary: This paper proposes an improved sparrow search algorithm (ISSA) to address the inherent problems of swarm intelligence algorithm. The ISSA introduces flight behavior from bird swarm algorithm and crossover/mutation from genetic algorithm to maintain population diversity and enhance optimization ability. These improvements effectively prevent falling into local optimum and greatly enhance precision.
MATHEMATICAL PROBLEMS IN ENGINEERING
(2021)
Article
Computer Science, Interdisciplinary Applications
Harish Kundra, Wasim Khan, Meenakshi Malik, Kantilal Pitambar Rane, Rahul Neware, Vishal Jain
Summary: The study introduces an integrated quantum-inspired firefly algorithm with cuckoo search (IQFACS) for optimizing solution sets, showing promising results in path planning and optimization problems.
INTERNATIONAL JOURNAL OF MODERN PHYSICS C
(2022)
Article
Computer Science, Artificial Intelligence
Meilin Zhang, Huiling Chen, Ali Asghar Heidari, Zhennao Cai, Nojood O. Aljehane, Romany F. Mansour
Summary: The recently proposed swarm intelligence algorithm, Runge-Kutta Optimization (RUN), is rooted in the fourth-order Runge-Kutta method. Compared with its counterparts, RUN has a more concrete theoretical foundation and more powerful optimization efficacy. However, it suffers from shortcomings in exploration ability and imbalance between exploration and exploitation. An improved version, OCRUN, based on opposition-based learning and cuckoo search, is proposed to overcome these deficiencies. OCRUN exhibits excellent performance in test functions and parameter sensitivity analysis experiments, and it also performs well in feature selection cases.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Yifeng Li, Ying Tan
Summary: In this paper, a theoretical model of fireworks algorithm based on search space partition is proposed, analyzed, and implemented. Experimental results show that the proposed algorithm outperforms previous variants of fireworks algorithm significantly, and achieves competitive results compared with state-of-the-art evolutionary algorithms.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Eneko Osaba, Javier Del Ser, David Camacho, Miren Nekane Bilbao, Xin-She Yang
APPLIED SOFT COMPUTING
(2020)
Article
Computer Science, Artificial Intelligence
Tengyue Li, Simon Fong, Kelvin K. L. Wong, Ying Wu, Xin-she Yang, Xuqi Li
INFORMATION FUSION
(2020)
Article
Computer Science, Artificial Intelligence
Qian Li, San-Yang Liu, Xin-She Yang
APPLIED SOFT COMPUTING
(2020)
Editorial Material
Computer Science, Artificial Intelligence
Suash Deb, Ka-Chun Wong, Xin-She Yang
NEURAL COMPUTING & APPLICATIONS
(2020)
Article
Computer Science, Artificial Intelligence
Gustavo H. de Rosa, Joao P. Papa, Xin-She Yang
APPLIED SOFT COMPUTING
(2020)
Article
Computer Science, Interdisciplinary Applications
Tengyue Li, Simon Fong, Shirley W. Siu, Xin-she Yang, Lian-Sheng Liu, Sabah Mohammed
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2020)
Article
Computer Science, Artificial Intelligence
Panagiotis E. Mergos, Xin-She Yang
Summary: The flower pollination algorithm (FPA) is an efficient optimization algorithm inspired by the pollination process of flowering species. Parameter values of FPA can significantly impact its computational performance and the study found that the optimal parameters depend on the objective functions, problem dimensions, and computational cost. Additionally, minimizing mean prediction errors does not always lead to the most robust predictions. Recommendations are made for setting optimal FPA parameters based on problem dimensions and computational cost.
Article
Computer Science, Hardware & Architecture
Jinyan Li, Yaoyang Wu, Simon Fong, Antonio J. Tallon-Ballesteros, Xin-she Yang, Sabah Mohammed, Feng Wu
Summary: This paper introduces a novel ensemble method that combines the advantages of ensemble learning and under-sampling by using a multi-objective strategy, resulting in significantly improved performance in imbalanced classification while maintaining the integrity of the original dataset. The proposed method outperforms single ensemble methods, state-of-the-art under-sampling methods, and combinations of these methods with the traditional PSO instance selection algorithm according to experimental results.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Zaid Abdi Alkareem Alyasseri, Ahamad Tajudin Khader, Mohammed Azmi Al-Betar, Xin-She Yang, Mazin Abed Mohammed, Karrar Hameed Abdulkareem, Seifedine Kadry, Imran Razzak
Summary: A multi-objective flower pollination algorithm is proposed in this study to solve the EEG signal denoising problem using wavelet transform. The algorithm optimizes the denoising parameters based on two measurement criteria, minimum mean squared error and maximum signal-to-noise ratio. Experimental results show that the proposed method achieves good performance.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Panagiotis E. Mergos, Xin-She Yang
Summary: The Flower Pollination Algorithm (FPA) is an efficient optimization algorithm inspired by the evolution process of flowering plants. In this study, a modified version of FPA called FPAPA is proposed, considering the additional feature of pollinator attraction in flower pollination. Numerical experiments show that FPAPA represents a statistically significant improvement upon the original FPA, outperforming other state-of-the-art optimization algorithms and offering better and more robust optimal solutions.
EVOLUTIONARY INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Qian Li, Sanyang Liu, Yiguang Bai, Xingshi He, Xin-She Yang
Summary: This paper investigates the robustness of complex networks under the assumption that costs are functions of node degrees. A multi-objective, elitism-based evolutionary algorithm is proposed to address the network disintegration problem. Through information retention and an update mechanism, the algorithm achieves improved convergence rate. Experimental results demonstrate that the proposed method outperforms five other state-of-the-art attack strategies.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Hardware & Architecture
Juzhen Wang, Xiaoli Zhang, Xingshi He, Yongqiang Sun
Summary: This article investigates the scenario where multiple UAVs serve as edge computing devices for the Internet of Vehicles (IoV). By optimizing bandwidth allocation and trajectory control, the communication capacity of the system is maximized so that the UAV edge computing network can process more data. The proposed actor-critic mixing network (AC-Mix) and multi-attentive agent deep deterministic policy gradient (MA2DDPG) algorithms improve the performance compared to the benchmark algorithm MADDPG.
IEEE TRANSACTIONS ON RELIABILITY
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Eneko Osaba, Javier Del Ser, Aritz D. Martinez, Jesus L. Lobo, Antonio J. Nebro, Xin-She Yang
Summary: Multiobjective optimization in evolutionary computation has shown remarkable performance, but the perspective of multitasking optimization in solving MOPs remains unexplored. Research into multitasking aims to address multiple optimization problems simultaneously to exploit synergies between the tasks.
2021 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2021)
(2021)
Proceedings Paper
Engineering, Electrical & Electronic
Geili. T. A. El Sanousi, Franz Hirtenfelder, Mohammed. A. H. Abbas, Raed. A. Abd-Alhameed, Xin-She Yang, Tuan Anh Le, Huan X. Nguyen
Summary: This paper introduces a novel concentric circular antenna array design with in band full duplex access and shows the effectiveness of incorporating virtual antenna formations for enhanced performance. The proposed design demonstrates excellent beam-forming abilities and IBFD reception through self-interference cancellation.
2021 28TH INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS (ICT)
(2021)
Article
Computer Science, Artificial Intelligence
J. Senthilnath, Sushant Kulkarni, S. Suresh, X. S. Yang, J. A. Benediktsson
Summary: In this study, a standalone clustering approach based on the Flower Pollination Algorithm (FPA) is proposed and demonstrated to outperform popular clustering algorithms and metaheuristic algorithms.
EVOLUTIONARY INTELLIGENCE
(2021)
