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检索条件"主题词=combinatorial optimization problems"

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A State Aggregation Approach for Solving Knapsack Problem with Deep Reinforcement Learning 12

A State Aggregation Approach for Solving Knapsack Problem wi...

引用

Asian Conference on Machine Learning

作者： Afshar, Reza Refaei Zhang, Yingqian Firat, Murat Kaymak, Uzay Eindhoven Univ Technol POB 513 NL-5600 MB Eindhoven Netherlands

This paper proposes a Deep Reinforcement Learning (DRL) approach for solving knapsack problem. The proposed method consists of a state aggregation step based on tabular reinforcement learning to extract features and construct states. The state aggregation policy is applied to each problem instance of the knapsack problem, which is used with Advantage Actor Critic (A2C) algorithm to train a policy through which the items are sequentially selected at each time step. The method is a constructive solution approach and the process of selecting items is repeated until the final solution is obtained. The experiments show that our approach provides close to optimal solutions for all tested instances, outperforms the greedy algorithm, and is able to handle larger instances and more flexible than an existing DRL approach. In addition, the results demonstrate that the proposed model with the state aggregation strategy not only gives better solutions but also learns in less timesteps, than the one without state aggregation.

关键词： combinatorial optimization problems Knapsack Problem Deep Reinforcement Learning State Aggregation

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Towards Perpetual Sensor Networks via Deploying Multiple Mobile Wireless Chargers 43

Towards Perpetual Sensor Networks via Deploying Multiple Mob...

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43rd Annual International Conference on Parallel Processing (ICPP)

作者： Xu, Wenzheng Liang, Weifa Lin, Xiaola Mao, Guoqiang Ren, Xiaojiang Sun Yat Sen Univ Guangzhou 510006 Guangdong Peoples R China Australian Natl Univ Canberra ACT 0200 Australia Univ Technol Sydney Sydney NSW 2007 Australia

ISBN: (纸本)9781479956180

In this paper, we study the use of multiple mobile charging vehicles to charge sensors in a large-scale wireless sensor network for a given monitoring period, where sensors can be charged by the vehicles with wireless power transfer. Since each sensor may experience multiple charges to avoid its energy expiration for the period, we first consider a charging problem of scheduling the multiple mobile vehicles to collaboratively charge sensors so that none of the sensors will run out of its energy and the sum of traveling distance (referred to as the service cost) of these vehicles can be minimized. Due to NP-hardness of the problem, we then propose a novel approximation algorithm for it, assuming that sensor energy consumption rates do not change over time. Otherwise, we devise a heuristic algorithm through minor modifications to the approximation algorithm. We finally evaluate the performance of the proposed algorithms via simulations. Experimental results show that the proposed algorithms are very promising, which can reduce upto 45% of the service cost in comparison with the service cost delivered by a greedy algorithm.

关键词： Approximation algorithms combinatorial optimization problems Periodic charging cycles Rechargeable sensor networks Traveling salesman problem Wireless power transfer

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Saving the calculating time of the TCNN with nonchaotic simulated annealing strategy

Saving the calculating time of the TCNN with nonchaotic simu...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Wang, Zhenning Lu, Wei Dai, Jun Univ Sci & Tech China Dept Automat Hefei 230027 Peoples R China

ISBN: (纸本)9781424427932

The Transient Chaotic Neural Network (TCNN) and the Noisy Chaotic Neural Network(NCNN) have been proved their searching abilities for solving combinatorial optimization problems(COPs). The chaotic dynamics of the TCNN and the NCNN are believed to be important for their searching abilities. However, in this paper, we propose a strategy which cuts off the rich dynamics such as periodic and chaotic attractors in the TCNN and just utilizes the nonchaotic converge dynamics of the TCNN to save the time needed for computation. The strategy is named as nonchaotic simulated annealing (NCSA). Experiments on the traveling salesman problems exibit the effectiveness of NCSA. The NCSA saves over half of the time needed for the computation while maintaining the searching ability of the TCNN.

关键词： TCNN CSA nonchaotic NCSA combinatorial optimization problems TSP

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Proposing a New Search Template for Modelling Languages

Proposing a New Search Template for Modelling Languages

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1st World Conference on Information Technology (WCIT)

作者： Rafeh, Reza Arak Univ Dept Comp Engn Arak Iran

The major problem in solving combinatorial optimization problems is the huge size of the search space. To explore the search space in a reasonable time, using smart search algorithms is inevitable. One of the main difficulties in implementing search methods is the lack of a uniform, high-level template for all search paradigms. In this paper, we propose a high-level, parametric template suitable for modeling languages which covers both tree search and local search. (C) 2010 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Guest Editor.

关键词： combinatorial optimization problems Search Space Tree Search Local Search Zinc Modelling Language

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Ferroelectric FET based Signed Synapses of Excitatory and Inhibitory Connection for Stochastic Spiking Neural Network based Optimizer 7

Ferroelectric FET based Signed Synapses of Excitatory and In...

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7th IEEE Electron Devices Technology and Manufacturing Conference (EDTM)

作者： Luo, Jin Liu, Tianyi Fu, Zhiyuan Wei, Xinming Huang, Qianqian Huang, Ru Peking Univ Sch Integrated Circuits Beijing 100871 Peoples R China Beijing Adv Innovat Ctr Integrated Circuits Beijing 100871 Peoples R China Chinese Inst Brain Res Beijing 102206 Peoples R China

ISBN: (纸本)9798350332520

For combinatorial optimization problem (CSP) solving of spiking neural networks (SNNs), both excitatory and inhibitory synaptic connections are necessary for mapping of constraints, along with adaptively-stochastic neuron. In this work, for the first time a novel ferroelectric FET (FeFET) based signed synapse with only two transistors is proposed and experimentally demonstrated to achieve excitatory and inhibitory connections, enabling cascade circuit with our previous proposed FeFETbased adaptively-stochastic neuron for all ferroelectric SNN optimizer. Based on the proposed design, a stochastic SNN is implemented for fast solving CSPs with accuracy improvement by 200%, providing a promising ultralow-hardware-cost and energy-efficient solution for optimization.

关键词： Ferroelectric FET (FeFET) synapse combinatorial optimization problems

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Adaptive Distance-Aware Attention Models for Routing problems

Adaptive Distance-Aware Attention Models for Routing Problem...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhang, Chao Xing, Jinbiao Wang, Hui Li, Kuangzheng Jiang, Weihao Pu, Shiliang Hikvis Res Inst Hangzhou 310052 Peoples R China Univ Sci & Technol China Hefei 230026 Peoples R China

ISBN: (纸本)9798350359329;9798350359312

Attention-based models (AMs) with deep reinforcement learning (DRL) training schemes have emerged as a prominent research direction in the field of machine learning for combinatorial optimization (ML4CO). These models have demonstrated great potential for quickly constructing solutions, without depending on expert knowledge or relying on costly supervised labels. However, existing AMs often neglect the use of positional embeddings and fail to exploit the relative distance information between nodes. To address this limitation, this paper proposes a novel adaptive distance-aware (ADA) mechanism for AMs. Drawing inspiration from the positional encoding technology used in Transformers, the ADA mechanism encodes the distance between pairs of nodes to re-scale the raw self-attention weights. Additionally, the proposed mechanism leverages multiple distance-aware structures to provide the decoder with diverse horizons, enabling it to effectively solve routing problems. The effectiveness of the proposed adaptive DA mechanism is validated through numerous experiments. The results demonstrate that integrating the ADA mechanism into existing attention models significantly improves the quality of the constructed solutions and enhances the generalization capability across different problem sizes.

关键词： combinatorial optimization problems deep reinforcement learning attention-based models routing problems distance-aware mechanism

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A Hybrid Ant Colony optimization and Its Application to Vehicle Routing Problem with Time Windows

A Hybrid Ant Colony Optimization and Its Application to Vehi...

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International Conference on Life System Modeling and Simulation / International Conference on Intelligent Computing for Sustainable Energy and Environment (LSMS-ICSEE)

作者： Hu, Xiangpei Ding, Qiulei Wang, Yunzeng Dalian Univ Technol Inst Syst Engn Dalian 116023 Peoples R China Univ Calif Riverside A Gary Anderson Grad Sch Management Riverside CA 92521 USA

ISBN: (纸本)9783642158520

The Ant Colony optimization (ACO) is a recent meta-heuristic algorithm for solving hard combinatorial optimization problems. The algorithm, however, has the weaknesses of premature convergence and low search speed, which greatly hinder its application. In order to improve the performance of the algorithm, a hybrid ant colony optimization (HACO) is presented by adjusting pheromone approach, introducing a disaster operator, and combining the ACO with the saving algorithm and lambda-interchange mechanism. Then, the HACO is applied to solve the vehicle routing problem with time windows. By comparing the computational results with the previous literature, it is concluded that the HACO is an effective way to solve combinatorial optimization problems.

关键词： Ant Colony optimization Vehicle Routing Problem with Time Windows combinatorial optimization problems

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An optimization algorithm inspired by the immune mechanism and its applications

An optimization algorithm inspired by the immune mechanism a...

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IEEE EMBS Asian-Pacific Conference on Biomedical Engineering (APBME2003)

作者： Sun, WD Tang, Z Li, J Tamura, H Zhao, XC Toyama Univ Fac Engn Toyama 9308555 Japan

ISBN: (纸本)0780379438

Immune system protects living body from an extraordinarily large variety of bacteria, viruses, and other pathogenic organisms. Based on immunological principles, new computational techniques are being developed, aiming not only at a better understanding of the system, but also at solving engineering problems. Our overall goal for this paper is twofold: to understand the real immune system from the information processing perspective, and to use idea generated from the immune system to construct new engineering application. As an example of the latter, we propose an optimization algorithm inspired by the immune response mechanism and apply it to Traveling Salesman Problem (TSP). We test the proposed algorithm by the simulations on randomly generated 100 data sets of 10-city problems and two of TSPLIB [1] benchmark problems: ulysses22 (22-city) and eil51 (51-city). The simulation results illustrate that the proposed algorithm can find one hundred percent valid solutions in short computation time, and the solution quality is very good;meanwhile, the shortest path of ulysses22 and eil51 generated by the proposed algorithm are 7013 and 426, respectively, which are the same as the best paths proclaimed at TSPLIB [1].

关键词： immune network immune response combinatorial optimization problems traveling salesman problem aartificial immune system

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Improving Hash Distributed A* for Shared Memory Architectures Using Abstraction 16th

Improving Hash Distributed A* for Shared Memory Architecture...

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16th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Sanz, Victoria De Giusti, Armando Naiouf, Marcelo Natl Univ La Plata Sch Comp Sci III LIDI La Plata Argentina Minist Sci Technol & Product Innovat CONICET Buenos Aires DF Argentina

ISBN: (纸本)9783319495835;9783319495828

The A* algorithm is generally used to solve combinatorial optimization problems, but it requires high computing power and a large amount of memory, hence, efficient parallel A* algorithms are needed. In this sense, Hash Distributed A* (HDA*) parallelizes A* by applying a decentralized strategy and a hash-based node distribution scheme. However, this distribution scheme results in frequent node transfers among processors. In this paper, we present Optimized AHDA*, a version of HDA* for shared memory architectures, that uses an abstraction-based node distribution scheme and a technique to group several nodes before transferring them to the corresponding thread. Both methods reduce the amount of node transfers and mitigate communication and contention. We assess the effect of each technique on algorithm performance. Finally, we evaluate the scalability of the proposed algorithm, when it is run on a multicore machine, using the 15-puzzle as a case study.

关键词： HDA* for shared memory architectures Abstraction-based node distribution scheme Hash-based node distribution scheme Scalability combinatorial optimization problems

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Proposed Architecture for the combinatorial problems Resolution based on BDI Agents in Ubiquitous Environments

Proposed Architecture for the Combinatorial Problems Resolut...

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4th International Conference on Computer Sciences and Convergence Information Technology

作者： Cabrera G, Guillermo Rubio L, Jose Miguel Pontificia Univ Catolica Valparaiso Valparaiso Chile

ISBN: (纸本)9781424452446

The objective of global optimality versus the cost that this implies is an unresolved issue by researchers. Classical problems such as Travel Salesman Problem (TSP) or Vehicle Routing Problem (VRP) and it's derivatives, relate to combinatorial optimization problems that arise on an everyday basis for a group of people that would like to have the solution to such problems as soon as possible. Thus, the idea that such problems purely operational, would be solved by heuristics in devices makes sense, due to the interest of normal use for sellers or car's drivers. Within the framework of what we may call ubiquitous distributed computing, this paper proposes an BDI architecture that supports the resolution of problems in complex combinatorial mobile devices across heuristics or metaheuristics embedded in the devices themselves and working from a set of data from a central server using agents for coordination and collaboration.

关键词： Multi-agent Systems combinatorial optimization problems ubiquitous computing mobile devices metaheuristic

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