12 papers · ranked by Valyu relevance
Zihao Cheng, Li Cao, Yang Qiu, Yinggao Yue + 1 more
Aiming at the problems of uneven population initialization distribution, easy trapping in local optima, unbalanced exploration and exploitation capabilities, insufficient optimization accuracy and convergence speed of the original Greater Cane Rat Algorithm (GCRA), this paper proposes a Chaos-Integrated…
Chaochuan Jia, Yaqi Yang, Yujie Cheng, Maosheng Fu + 4 more
To tackle the drawbacks inherent in the Chinese Pangolin Optimization (CPO) algorithm, such as uneven population initialization distribution and a tendency to fall into local optimal solutions, this paper proposes an ACDCPO algorithm that integrates boundary-adaptive contraction initialization, Cauchy inverse…
Mohamed Elhosseny, Mahmoud Abdel-Salam, Anand Nayyar, Emre Çelik + 3 more
The Dung Beetle Optimization (DBO) algorithm is a relatively recent metaheuristic known for its simplicity, versatility, and low parameter dependence, making it a valuable tool for solving complex optimization problems. Despite its potential, DBO suffers from limitations such as slow convergence and premature…
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…
Yacine Bouali, Basem Alamri
Accurate identification of photovoltaic (PV) cell and module parameters is essential for reliable electrical modeling, performance assessment, and long-term energy yield prediction. This task is commonly formulated as an optimization problem, where the root mean square error (RMSE) between measured and estimated…
Zhixin Han, Ying Qiao, Hongxin Fu, Yuelin Gao + 1 more
With the increasing complexity of optimization problems, existing methods are often inadequate for addressing these challenges, creating a pressing need for more versatile and robust approaches capable of solving a wide range of optimization problems. Meta-heuristic algorithms have become powerful tools in this regard…
Yarong Li, Chuandong Qin, Heming Jia
Inspired by the hovering, diving, and cooperative hunting behaviors of the pied kingfisher, the Pied Kingfisher Optimizer (PKO) has demonstrated competitive performance in optimization tasks. However, it exhibits several phase-specific limitations, including uneven population distribution caused by random…
Albert Jiménez-Blanco, Lorién López-Villellas, Juan Carlos Moure, Miquel Moreto + 1 more
Sequence-to-graph alignment is a central problem in bioinformatics, with applications in multiple sequence alignment (MSA) and pangenome analysis, among others. However, current algorithms for optimal affine-gap alignment impose high memory and computational requirements, limiting their scalability to aligning long…
Ruya Fan, Yan Chen, Bo Shan, Erfan Babaee Tirkolaee
Aiming at the problem of matching scarce resources among donors, recipients, and medical institutions in organ transplantation, a stable three-sided matching method is proposed. Firstly, in view of the preference structure characteristics of the problems in the context of organ transplantation, a mixed preference…
João Sartori, Eduardo Krempser, Ana Carolina Ramos Guimarães, Lucas de Almeida Machado
The optimization of protein sequences for enhanced binding and stability remains a formidable challenge in bioengineering due to the vastness of sequence space. Existing state-of-the-art methods, including traditional structure-based design and protein language models, use fitness estimators as objective functions to…
Ryo Tsuchihashi, Misaki Kinoshita
Cyclic peptides have emerged as a pivotal modality for next-generation therapeutics, due to their superior biocompatibility, high selectivity, and structural stability. While AI-driven peptide design has advanced rapidly, conventional optimization algorithms are often constrained by initialization biases, which impede…
Yukun Yang, Wolfgang Maass
Most current methods for goal-directed action selection in the face of changing goals and contingencies require DNNs or LLMs. Therefore they are less suited for implementation in edge devices, where low energy-consumption is imperative. The brain shows that similar functionality can be produced with just 20W, even with…