26 papers · ranked by Valyu relevance
Thomas Seiller
This work continues the development of an intensional approach to computability initiated in previous work, in which programs and computations, rather than functions, constitute the primary objects of study. In this setting, models of computation are described as monoid actions on a configuration space, and programs as…
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The Hidden Subgroup Problem (HSP) unifies several landmark quantum algorithms, yet systematic exploration of its variants and modern applications has slowed. This paper revives HSP-based algorithm design by examining new group structures with direct relevance to post-quantum cryptography, lattice problems, and…
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Terminally labeled DNA oligonucleotides have wide applications in modern biology and biotechnological applications. It has been observed that the fluorescent intensity of light released from these fluorescent labels is heavily influenced by the terminal sequence of nucleotides. Recent studies have assayed and published…
Zhimeng Zhou, Yang Nan, Minjie Mou, Yuntao Qian + 16 more
Artificial intelligence (AI) is increasingly permeating the drug development pipeline. Numerous algorithms for accelerating this multi-stage and multi-task process have been constructed, which depends heavily on expert design and labor-intensive task-specific optimization. Given that AI-driven acceleration of drug…
Han Yang, Xingwang Huang, Heming Jia
The rapid development of intelligent systems has introduced increasingly sophisticated optimization problems across diverse domains. While contemporary metaheuristic algorithms, including the recent Artificial Lemming Algorithm (ALA), have shown considerable promise, they frequently encounter difficulties such as…
Yves Matanga, Chunling Du, Etienne van Wyk
Context: The city of Kinshasa faces severe traffic congestion, requiring strategic infrastructure capacity enhancements. Although a comprehensive master plan [[1]] has been proposed, its implementation requires substantial financial investment, which remains constrained in the Democratic Republic of the Congo (DRC), an…
Doaa El-Nagar, Ibrahim Zeidan, Mohamed Issa
The Multi-Objective Sinh-Cosh Optimization Algorithm (MOSCHO) is presented in this article based on the memorized technique. MOSCHO is an extension version of the recently proposed Sinh-Cosh optimizer for multiple objective optimizations. The memorized local optimum is integrated with the global optimal solution to…
Ahsan Sanaullah, Nathaniel K. Brown, Pramesh Shakya, Arun Deegutla + 4 more
Lossless full text indexes are utilized in a myriad of applications in bioinformatics. The continuously decreasing cost of generating biological data has resulted in the need to build full text indexes on biological datasets of increasing size. Many compressed full text indexes have been developed to address this…
Sergio Salazar, Abraham Alejandro Ponce Duarte, Maurício G. C. Resende, J. Manuel Colmenar
Facility location problems (FLPs) are a family of optimisation problems with significant social impact. This class of problems has been the subject of study since the 1960s, with classical approaches including the Weber problem and the p-Median problem. Currently, more complex variations of these problems are being…
Maria Constantin, Adrian Miclăuş, Alexandru Popa, Andrei Popa
Given a finite set of integers $A$, a \emph{unary translocation} produces a new set $A' = A \cup \{u,v\}$, where $u$ and $v$ are nonnegative integers satisfying $x+y=u+v$ for some $x,y\in A$. For an input set $A$ and a target set $B$, the \emph{unary translocation distance} is the minimum number of unary translocations…
Jingyang Zhao, Mingyu Xiao
The multi-vehicle dial-a-ride problem (mDaRP) is a fundamental vehicle routing problem with pickups and deliveries, widely applicable in ride-sharing, economics, and transportation. Given a set of n locations, h vehicles of identical capacity λ located at various depots, and m ride requests each defined by a source and…
Changin Oh, Kathleen P. Wilkie
We present the Toroidal Search Algorithm (TSA), a novel population-based metaheuristic optimization method inspired by the topology of a torus. Conventional metaheuristics frequently suffer from boundary stagnation, a phenomenon that severely degrades performance in bounded and high-dimensional search spaces. TSA…
Mahmudur Rahman Hera, David Koslicki, Conrado Martínez
With the surge in sequencing data generated from an ever-expanding range of biological studies, designing scalable computational techniques has become essential. One effective strategy to enable large-scale computation is to split long DNA or protein sequences into k-mers, and summarize large k-mer sets into compact…
Mingbin Tang, Yejun Zheng, Lianbao Li, Li Cao + 2 more
Complex engineering optimization problems often exhibit high-dimensional, multi-constraint, and nonlinear characteristics. Traditional deterministic optimization methods rely on gradient information and have limited optimization ranges, making it difficult to meet the requirements of efficient and accurate solutions.…
Arseny Shur, Ido Tziony, Yaron Orenstein
Minimizers are sampling schemes which are ubiquitous in almost any high-throughput sequencing analysis. Assuming a fixed alphabet of size σ, a minimizer is defined by two positive integers k, w and a linear order ρ on k-mers. A sequence is processed by a sliding window algorithm that chooses in each window of length w…
Daniel Anker Hermansen
In the Euclidean travelling salesman problem (Euclidean TSP), a salesman must visit $n$ points in Euclidean space, while minimizing the travel distance, according to the Euclidean distance function. In online Euclidean TSP, introduced by Abrahamsen, Bercea, Beretta, Klausen and Kozma [ESA 2024], the points are revealed…
Shuangyuan Shi, Chang Liu, Lvjiang Yin, Hegen Xiong + 3 more
While the static integrated process planning and scheduling (IPPS) problem is theoretically well-established, its practical application is limited in unpredictable manufacturing environments demanding dynamic adaptability. This paper proposes a dynamic IPPS problem considering stochastic rework (IPPS-SR), whose…
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The integration of machine learning methods is transforming many areas of research by, for instance, accelerating molecular dynamics simulations and enabling improved prediction and optimization of chemical reactions. However, despite this progress, the adoption of data-driven approaches in atomic layer deposition…
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Realizing the promise of artificial intelligence (AI) to accelerate scientific progress and deliver technological impact depends on how effectively AI can be integrated into real-world decision- making processes. As Peter Norvig states, “Somewhat remarkably, almost all AI research until very recently has assumed that…
Yu Liu, Maosheng Fu, Chaochuan Jia, Zhengyu Liu + 5 more
Unmanned aerial vehicle path planning faces multiple challenges in terms of effectiveness and safety. Traditional optimization methods are difficult to use to effectively find the best route. An enhanced artificial lemming optimization algorithm (ALAEN) is proposed here, which introduces stochastic differential…
Dinesh Kumar Baghel, Alex Ravsky, Erel Segal-Halevi
Given items of different sizes and a fixed bin capacity, the bin-packing problem is to pack these items into the minimum number of bins such that the sum of the item sizes in each bin does not exceed the capacity. We define a new variant, k-times bin-packing (kBP), in which the goal is to pack the items so that each…
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Finding the most stable adsorption geometry of a flexible molecule on a catalytic surface remains a key challenge due to the high dimensionality and ruggedness of the potential energy surface. We present a Gradient-Enhanced Genetic Algorithm (GE-GA) for the global optimization of adsorbate–surface configurations…
Pankaj Sharma, Rohit Salgotra, Saravanakumar Raju, Szymon Łukasik + 1 more
The identification of unknown parameters for proton exchange memberane fuel cells (PEMFCs) using nature-inspired optimization algorithms has emerged as a significant field of research in recent years. In the present study, a novel approach is presented, namely the hybrid Gray Particle Cuckoo (GPC) algorithm based on…
Niklas Haas, Sören Schmitt, Rob van Stee
We consider the online buffer minimization in multiprocessor systems with conflicts problem (in short, the buffer minimization problem) in the recently introduced flow model. In an online fashion, workloads arrive on some of the n processors and are stored in an input buffer. Processors can run and reduce these…
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Background: Pharmaceutical batch scheduling in multi-reactor configurations presents complex optimization challenges under operational uncertainty, yet limited research addresses how parallel processing capacity affects heuristic performance and predictive modeling. Objectives: This study investigated scheduling…
Stephan Grein, David R. Penas, Daniel Weindl, Polina Lakrisenko + 2 more
Dynamic models are central to the computational life sciences but typically contain unknown parameters that must be inferred from experimental data. High-throughput measurements have made this task increasingly challenging, yielding high-dimensional search spaces and non-convex objectives with many local optima. This…