XU Xiaolong,RONG Hanzhong,TROVATI M,et al.CS-PSO:chaotic particle swarm optimization algorithm for solving combinatorial optimization problems[J].Soft Computing,2016,2018(22):1-13.Xu X, Rong H, Trovati M, et al, CS-PSO: Chaotic Particle Swarm Optimization Algorithm for Solving Combinatorial ...
Solving combinatorial optimization problems can often lead to runtime growing exponentially as a function of the input size. But important real-world problems, industrial applications, and academic research challenges, may demand exact optimal solutions. In such situations, parallel processing can reduce ...
Combinatorial Optimization problems occur in different fields of science, including Artificial Intelligence, BioInformatics, Electronic Design Automation, Operations Research, Applied Formal Methods, among others. For example in BioInformatics, and in particular inAJ dos Reis Morgado...
In this paper, we propose Deep Reinforcement Learning Hyperheuristic (DRLH), a general approach to selection hyperheuristic framework (definition in Section 2) for solving combinatorial optimization problems. In DRLH, we replace the adaptive layer of ALNS with a Deep RL agent responsible for select...
A range of quantum-, optical- and spintronic-based approaches have been explored for solving such combinatorial optimization problems, but they remain complicated to build and to scale. Here we report a scalable ring-oscillator-based integrated circuit for optimization problem solving. Our 1,968-...
Solving Combinatorial Optimization Problems in Parallel Methods and Techniques 作者:Alfonso Ferreira/Panos Pardalos/Afonso Ferreira 页数:292 ISBN:9783540610434 豆瓣评分 目前无人评价 写笔记 写书评 加入购书单 分享到 + 加入购书单
In VLSI physical design, many algorithms require the solution of difficult combinatorial optimization problems such as max/min-cut, max-flow problems etc. Due to the vast number of elements typically found in this problem domain, these problems are computationally intractable leading to the use of ...
Quantum annealing is a generic solver for optimization problems that uses fictitious quantum fluctuation. The most groundbreaking progress in the research field of quantum annealing is its hardware implementation, i.e., the so-called quantum annealer, us
optimization problems. In this study, we investigate the use of deep learning (DL) to solve combinatorial optimization problems related to scheduling, packing, loading, and routing. A systematic literature review is run in well-known scientific databases such as Scopus, Web of Science, and IEEE ...
The methods used in optimization vary depending on the type of problem and the variables involved. Optimization problems with discrete variables are known as combinatorial optimization problems. If the variables in the problem are continuous, we can use calculus to solve the problem....