19 papers · ranked by Valyu relevance
Husheng Wu, Renbin Xiao
Optimization problems especially in a dynamic environment is a hot research area that has attracted notable attention in the past decades. It is clear from the dynamic optimization literatures that most of the efforts have been devoted to continuous dynamic optimization problems although the majority of the real-life…
Yuta Nakamura, T. Takahashi, Yoshiyuki Kabashima
Knapsack problem (KP) is a representative combinatorial optimization problem that aims to maximize the total profit by selecting a subset of items under given constraints on the total weights. In this study, we analyze a generalized version of KP, which is termed the generalized multidimensional knapsack problem…
Charlie Wusuo Liu
This article details the algorithmics in FLSSS, an R package for solving various subset sum problems. The fundamental algorithm engages the problem via combinatorial space compression adaptive to constraints, relaxations and variations that are often crucial for data analytics in practice. Such adaptation conversely…
Shalin Shah
The 0/1 multidimensional knapsack problem is the 0/1 knapsack problem with m constraints which makes it difficult to solve using traditional methods like dynamic programming or branch and bound algorithms. We present a genetic algorithm for the multidimensional knapsack problem with Java and C++ code that is able to…
Arindam Khan, Eklavya Sharma, Kidambi Sreenivas
We study a generalization of the knapsack problem with geometric and vector constraints. The input is a set of rectangular items, each with an associated profit and d nonnegative weights (d-dimensional vector), and a square knapsack. The goal is to find a non-overlapping axis-parallel packing of a subset of items into…
Ivars Dzalbs, Tatiana Kalganova, Ian Dear
The multidimensional knapsack problem is a well-known constrained optimization problem with many real-world engineering applications. In order to solve this NP-hard problem, a new modified Imperialist Competitive Algorithm with Constrained Assimilation (ICAwICA) is presented. The proposed algorithm introduces the…
Pinggai Zhang, Ling Wang, Jiaojie Du, Zixiang Fei + 3 more
'Minrui Fei' 'Panos M. Pardalos'] Human Learning Optimization (HLO) is an efficient metaheuristic algorithm in which three learning operators, i.e., the random learning operator, the individual learning operator, and the social learning operator, are developed to search for optima by mimicking the learning behaviors of…
Jean P. Martins
problem Authors: ['Jean P. Martins'] The multidimensional knapsack problem (MKP) is an NP-hard combinatorial optimization problem whose solution consists of determining a subset of items of maximum total profit that does not violate capacity constraints. Due to its hardness, large-scale MKP instances are usually a…
Kobe Grobben, Phablo F. S. Moura, Hande Yaman
This paper presents an algorithmic study of a class of covering mixed-integer linear programming problems which encompasses classic cover problems, including multidimensional knapsack, facility location and supplier selection problems. We first show some properties of the vertices of the associated polytope, which are…
Emanuel Vega, José Lemus-Romani, Ricardo Soto, Broderick Crawford + 4 more
Population-based metaheuristics can be seen as a set of agents that smartly explore the space of solutions of a given optimization problem. These agents are commonly governed by movement operators that decide how the exploration is driven. Although metaheuristics have successfully been used for more than 20 years…
Vojtěch Uher, Petr Gajdoš, Michal Radecký, Václav Snášel
The Differential Evolution (DE) is a widely used bioinspired optimization algorithm developed by Storn and Price. It is popular for its simplicity and robustness. This algorithm was primarily designed for real-valued problems and continuous functions, but several modified versions optimizing both integer and…
Juan Pablo Franco, Nitin Yadav, Peter Bossaerts, Carsten Murawski
Life presents us with decisions of varying degrees of difficulty. Many of them are NP-hard, that is, they are computationally intractable. Two important questions arise: which properties of decisions drive extreme computational hardness and what are the effects of these properties on human-decision making? Here, we…
Zhengyuan Wang, Hui Zhang, Yali Li
0-1 Knapsack problem (KP) is NP-hard. Approximate solution is vital for solving KP exactly. In this paper, a fast polynomial time approximate solution (FPTAS) is proposed for KP. FPTAS is a local search algorithm. The best approximate solution to KP can be found in the neighborhood of the solution of upper bound for…
Susanne Albers, Arindam Khan, Leon Ladewig
The knapsack problem is one of the classical problems in combinatorial optimization: Given a set of items, each specified by its size and profit, the goal is to find a maximum profit packing into a knapsack of bounded capacity. In the online setting, items are revealed one by one and the decision, if the current item…
Juan Pablo Franco, Peter Bossaerts, Carsten Murawski
Many everyday tasks require people to solve computationally complex problems. However, little is known about the effects of computational hardness on the neural processes associated with solving such problems. Here, we draw on computational complexity theory to address this issue. We performed an experiment in which…
Rejwana Tasnim Rimi, K. M. Azharul Hasan, Tatsuo Tsuji
Multidimensional query processing is an important access pattern for multidimensional scientific data. We propose an in-memory multidimensional query processing algorithm for dense data using a higher-dimensional array. We developed a new array system namely a Converted two-dimensional Array (C2A) of a multidimensional…
Davide Gheza, Michael C. Freund, Thea R. Zalabak, Wouter Kool
How can humans manage multiple sources of information competing for attention? To approach this question, we adopted a multi-dimensional task-set interference paradigm that requires individuals to handle distractions from three independent dimensions. Behavioral results suggest that people track prior interference from…
Rafael G. Viegas, Ingrid B. S. Martins, Murilo N. Sanches, Antonio B. Oliveira + 3 more
Molecular dynamics (MD) simulations provide a powerful means to explore the dynamic behavior of biomolecular systems at the atomic level. However, analyzing the vast datasets generated by MD simulations poses significant challenges. This manuscript discusses the Energy Landscape Visualization Method (ELViM), a…
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Experimental design plays an important role in efficiently acquiring informative data for system characterization and deriving robust conclusions under resource limitations. Recent advancements in high-throughput experimentation coupled with machine learning have notably improved experimental procedures. While Bayesian…