Article
Computer Science, Artificial Intelligence
Zhi-zhong Zeng, Zhi-peng Lue, Xin-guo Yu, Qing-hua Wu, Yang Wang, Zhou Zhou
Summary: This paper proposes a new learning method called post-flip edge-state learning (PF-ESL) for the max-cut problem. Unlike previous algorithms, PF-ESL focuses on edge-states as the critical information and extracts their statistics for learning. Experimental results show that PF-ESL is competitive and provides value-added learning for both the EDA perturbation operator and the path-relinking operator. The paper also introduces a new perspective on edge-states, which can inspire future research in learning-based algorithms and graph partitioning problems.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
David Rodriguez Rueda, Carlos Cotta, Antonio J. Fernandez-Leiva
Summary: This paper explores and analyzes a wide range of metaheuristics to tackle the template design problem, guided by a number of issues such as problem formulation, solution encoding, and the symmetrical nature of the problem. It proposes a slot-based alternative problem formulation for the TDP, which represents another option other than the classical variation-based formulation.
JOURNAL OF INTELLIGENT MANUFACTURING
(2021)
Article
Computer Science, Artificial Intelligence
Mariana A. Londe, Carlos E. Andrade, Luciana S. Pessoa
Summary: The p-next center problem extends the classical p-center problem by assigning a backup center to welcome users from a suddenly unavailable center. The objective is to minimize user paths, with proposed genetic algorithm-based methods showing outstanding performance in experimental tests.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Tunchan Cura
Summary: This study presents a competitive algorithm combining the mayfly optimization algorithm (MA) with a local search procedure to solve the α-neighbor p-center problem. The proposed MA method is compared to the best alternatives found in the literature, showing similar performance. The proposed method performs well for cases with a low number of facilities.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Vaclav Snasel, Rizk M. Rizk-Allah, Davut Izci, Serdar Ekinci
Summary: This paper proposes a powerful integrated optimization algorithm, INFO-GBB, for determining the optimal parameters of a power system stabilizer (PSS) model used in a single-machine infinite-bus (SMIB) system. By combining INFO optimizer with COBL and GBB strategies, INFO-GBB algorithm enhances the searching capability and solution diversity. The effectiveness of the algorithm is validated on CEC 2020 benchmark suits, and the results show superior performance compared to other algorithms. Therefore, INFO-GBB algorithm can efficiently handle the parameter estimation and function optimization tasks of the PSS model.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Eneko Osaba, Javier Del Ser, Carlos Cotta, Pablo Moscato
Summary: Memetic Algorithms and approaches have been the focus of research activity in Memetic Computing since the late eighties. This hybridization of algorithms with problem-specific knowledge has achieved success in solving challenging real-world optimization problems.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Mathematics
Ibrahim Attiya, Laith Abualigah, Doaa Elsadek, Samia Allaoua Chelloug, Mohamed Abd Elaziz
Summary: This paper investigates intelligent scheduling approaches to optimize the task scheduling of IoT applications in cloud computing. The proposed CHMPAD algorithm addresses the drawbacks of local optima and the basic ChOA algorithm. Experimental results show that CHMPAD significantly improves the average makespan time across different workloads.
Article
Agriculture, Dairy & Animal Science
Maoxuan Miao, Jinran Wu, Fengjing Cai, You-Gan Wang
Summary: In this study, we introduced an improved genetic algorithm that enhances the exploitation capability and dimension reduction in predictor variables by incorporating an improved splicing method. This algorithm accelerates the identification of the minimal best gene subset and achieves better results compared to other algorithms. The experimental evaluation using the body weight dataset of Hu sheep confirms the superiority of our modified algorithm over the genetic algorithm and other considered algorithms.
Article
Mathematics
Broderick Crawford, Ricardo Soto, Jose Lemus-Romani, Marcelo Becerra-Rozas, Jose M. Lanza-Gutierrez, Nuria Caballe, Mauricio Castillo, Diego Tapia, Felipe Cisternas-Caneo, Jose Garcia, Gino Astorga, Carlos Castro, Jose-Miguel Rubio
Summary: The balance between exploration and exploitation is a key issue in metaheuristic optimization, with a Q-learning integration framework being proposed to improve operator selection and showing statistical improvements in the balance and solution quality for multiple recent metaheuristic algorithms tested on the Set Covering Problem.
Article
Computer Science, Artificial Intelligence
Wangduk Seo, Minwoo Park, Dae-Won Kim, Jaesung Lee
Summary: In this paper, an evolutionary multilabel feature selection algorithm is proposed to search for the final feature subset using multiple populations, in order to prevent limiting the synergy of hybridization. Experimental results show that the proposed method can identify better feature subsets than conventional methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Engineering, Mechanical
Yuyue Su, Xiaojun Tong, Miao Zhang, Zhu Wang
Summary: This paper proposes a three-layer optimization method for generating high-performance S-box using a new chaotic map and artificial jellyfish optimization algorithm. The optimization method considers the nonlinearity, differential uniformity, and other criteria of the S-box.
NONLINEAR DYNAMICS
(2023)
Article
Computer Science, Information Systems
Xiaohui Zhang, Yuyan Han, Grzegorz Krolczyk, Marek Rydel, Rafal Stanislawski, Zhixiong Li
Summary: This study explores the dynamic scheduling problem in distributed manufacturing systems and proposes a rescheduling framework to address production disruptions caused by random machine breakdowns. By establishing a mathematical model and adopting an event-driven policy, a two-stage predictive-reactive method is proposed for dynamic scheduling optimization.
Article
Engineering, Multidisciplinary
W. Y. Wang, Z. H. Xu, Y. H. Fan, D. D. Pan, P. Lin, X. T. Wang
Summary: This work proposes a novel adaptive global optimization algorithm called Disturbance Inspired Equilibrium Optimizer, which enhances the exploitation ability of Equilibrium Optimizer and solves the issue of getting trapped in local minima. The algorithm introduces a novel disturbance-based hybrid initialization strategy, a new form of time factor, and a new update rule of particle's position, leading to significantly improved exploration and exploitation ability.
APPLIED MATHEMATICAL MODELLING
(2023)
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.
Review
Computer Science, Interdisciplinary Applications
Raedal Abu Zitar, Mohammed Azmi Al-Betar, Mohammed A. Awadallah, Iyad Abu Doush, Khaled Assaleh
Summary: This review paper provides an in-depth overview of the JAYA algorithm, analyzing its optimization model, convergence characteristics, and various versions. It also discusses the applications and open sources code of the algorithm, highlighting its advantages and limitations in dealing with optimization problems. Finally, the paper suggests possible future enhancements to improve the performance of the JAYA algorithm.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2022)
