13 papers · ranked by Valyu relevance
Broderick Crawford, Álex Paz, Ricardo Soto, Álvaro Peña Fritz + 7 more
Metaheuristics are a fundament pillar of Industry 4.0, as they allow for complex optimization problems to be solved by finding good solutions in a reasonable amount of computational time. One category of important problems in modern industry is that of binary problems, where decision variables can take values of zero…
Abdelmonem M. Ibrahim, Doaa A. Fakhry, Fares Al-Shargie, Jan Cornelis
Feature selection is crucial for high-dimensional sensor and biomedical data because it reduces redundancy, improves generalization, and supports interpretable biomarker discovery. In this study, we propose a Binary Chaos-Enhanced Newton-Raphson-Based Optimizer (BCNRBO) for wrapper-based feature selection. The method…
Broderick Crawford, Hugo Caballero, Gino Astorga, Felipe Cisternas-Caneo + 7 more
The Set Covering Problem is a fundamental NP-hard problem in combinatorial optimization and plays a central role in a wide range of industrial decision-making processes, including logistics planning, scheduling, facility location, network design, and resource allocation. In many real-world contexts, problems of this…
Reham Kamal, Eman Amin, Diaa Salama AbdElminaam, Rasha Ismail
Feature selection is a key step in machine learning-based decision systems, especially in medical and biomedical applications, where datasets often contain a large number of features that can negatively affect both accuracy and interpretability. In this study, we introduce the binary secretary bird optimization…
Łukasz Grodzki, Mateusz Slysz, Grzegorz Waligóra, Zhian Jia + 1 more
Quantum computing offers new possibilities for solving combinatorial optimization problems with rapidly growing search spaces. Among emerging hardware platforms, photonic quantum computers based on boson sampling provide a promising approach for sampling-based optimization methods. In this work, we investigate the…
GyeongTaek Choi, Seungho Jeon
WebAssembly is a low-level binary format originally designed to enable high-performance applications to run in web browsers. As WebAssembly is increasingly being ported to various environments, the security verification of WebAssembly execution environments is becoming more critical. While a wide variety of WebAssembly…
Binhe Chen, Yaodan Chen, Li Cao, Changzu Chen + 3 more
The Crested Porcupine Optimizer (CPO), as a newly emerging swarm intelligence algorithm, demonstrates advantages in balancing global exploration and local exploitation but still suffers from limitations in convergence speed and local exploitation precision. To address these issues, this paper proposes an enhanced…
Kun Qi, Kai Wei, Rong Cheng, Guangmin Liang + 3 more
In the field of industrial fault detection, accurate and timely fault identification is crucial for ensuring production safety and efficiency. Effective feature selection (FS) methods can significantly enhance detection performance in this process. However, the recently proposed Black-winged Kite Algorithm (BKA) tends…
Shengnan Li, Taiju Yin, Heming Jia
As engineering systems grow in complexity, reliable metaheuristic optimizers are increasingly essential. While swarm intelligence algorithms are widely applied, recent approaches like the Cuckoo Catfish Optimizer (CCO) can experience premature convergence due to limited local exploitation and simplistic boundary…
MingXuan Jian, GuoZhen Wu, BangLing Xiao
Metaheuristic optimization algorithms are widely used to tackle complex, high-dimensional, and nonlinear problems by mimicking natural or social behaviors, showing great potential for future development. Among them, the Parrot Optimization (PO) algorithm, inspired by the green-cheeked conure, exhibits strong…
Li Cao, Meng Li, Ken Chen, Yinggao Yue + 3 more
Aiming at the inherent limitations of the basic White Shark Optimizer (WSO), such as insufficient population diversity, unbalanced global and local search mechanisms, and weak convergence in the later stage, this paper proposes an Improved White Shark Optimizer (IWSO). The algorithm is improved from the following three…
Li Lan, Zhang Qi
This paper presents an Enhanced parrot Optimizer (EPO), a novel metaheuristic algorithm that synergistically integrates multiple advanced strategies to address the critical limitations of the original parrot Optimizer (PO)-namely, poor initial population diversity, susceptibility to premature convergence, slow…
Ning Zhao, Tinghua Wang, Yating Zhu, Yongquan Zhou
To address the deficiencies in global search capability and population diversity decline of the black-winged kite algorithm (BKA), this paper proposes an enhanced black-winged kite algorithm integrating opposition-based learning and quasi-Newton strategy (OQBKA). The algorithm introduces a mirror imaging strategy based…