检索结果-内蒙古大学图书馆

12th International Conference on VLSI Design

作者： Jayadeva Indian Inst Technol Dept Elect Engn New Delhi 110016 India

ISBN: (纸本)0769500137

Recent developments have aroused the interest of researchers in the application of chaotic neural networks to combinatorial optimization problems. In this paper, we introduce a new approach, which is termed as Sequential Chaotic Annealing. The approach combines chaotic neural networks and ideas from the theory of nonlinear optimization. The proposed neural networks are adaptive in the sense that the network "learns" the right cost or energy function to optimize. Sequential Chaotic Annealing is applied to multilayer channel routing using the reserved wiring model and restricted doglegging. We show that the proposed approach improves convergence to valid solutions and reduces the sensitivity to the initial states of the neurons.

关键词： chaos simulated annealing network routing neural nets circuit optimisation wiring integrated circuit layout VLSI circuit layout CAD sequential chaotic annealing multilayer channel routing chaotic neural networks combinatorial optimization problems nonlinear optimization energy function reserved wiring model restricted doglegging VLSI

来源：评论

学校读者我要写书评

暂无评论

Computing Bounds on Network Capacity Regions as a Polytope Reconstruction Problem

Computing Bounds on Network Capacity Regions as a Polytope R...

引用

IEEE International Symposium on Information Theory (ISIT)

作者： Kim, Anthony Medard, Muriel Oracle Corp 500 Oracle Pkwy Redwood Shores CA 94065 USA MIT Res Lab Elect Cambridge MA 02139 USA

ISBN: (纸本)9781457705953

We define a notion of network capacity region of networks that generalizes the notion of network capacity defined by Cannons et al. and prove its notable properties such as closedness, boundedness and convexity when the finite field is fixed. We show that the network routing capacity region is a computable rational polytope and provide exact algorithms and approximation heuristics for computing the region. We define the semi-network linear coding capacity region, with respect to a fixed finite field, that inner bounds the corresponding network linear coding capacity region, show that it is a computable rational polytope, and provide exact algorithms and approximation heuristics. We show connections between computing these regions and a polytope reconstruction problem and some combinatorial optimization problems, such as the minimum cost directed Steiner tree problem. We provide an example to illustrate our results. The algorithms are not necessarily polynomial-time.

关键词： Approximation algorithms Approximation methods Encoding Heuristic algorithms Network coding Routing Steiner trees approximation heuristics combinatorial optimization problems computing bounds fixed finite field heuristic programming inner bounds linear codes minimum cost directed Steiner tree problem network coding network routing capacity region optimisation polynomial-time polytope reconstruction problem seminetwork linear coding capacity region telecommunication network routing trees (mathematics)

来源：评论

学校读者我要写书评

暂无评论

Second Order Swarm Intelligence

Second Order Swarm Intelligence

引用

8th International Conference on Hybrid Artificial Intelligent Systems (HAIS)

作者： Ramos, Vitorino Rodrigues, David M. S. Louca, Jorge Tech Univ Lisbon IST ISR LaSEEB Evolutionary Syst & Biomed Eng Lab Av Rovisco Pais1 Torre Norte 6-21 P-1049001 Lisbon Portugal Open Univ Milton Keynes Bucks England Lisbon Univ Inst IUL ISCTE P-1469026 Lisbon Portugal

ISBN: (纸本)9783642408465

An artificial Ant Colony System (ACS) algorithm to solve general-purpose combinatorial optimization problems (COP) that extends previous AC models [21] by the inclusion of a negative pheromone, is here described. Several Traveling Salesman Problem (TSP) were used as benchmark. We show that by using two different sets of pheromones, a second-order coevolved compromise between positive and negative feedbacks achieves better results than single positive feedback systems. The algorithm was tested against known NP-complete combinatorial optimization problems, running on symmetrical TSPs. We show that the new algorithm compares favorably against these benchmarks, accordingly to recent biological findings by Robinson [26,27], and Gruter [28] where "No entry" signals and negative feedback allows a colony to quickly reallocate the majority of its foragers to superior food patches. This is the first time an extended ACS algorithm is implemented with these successful characteristics.

关键词： Self-Organization Stigmergy Co-Evolution Swarm Intelligence Dynamic optimization Foraging Cooperative Learning combinatorial optimization problems Symmetrical Traveling Salesman problems (TSP)

来源：评论

学校读者我要写书评

暂无评论

An Adaptive Ant Colony optimization in Knowledge Graphs 11

An Adaptive Ant Colony Optimization in Knowledge Graphs

引用

11th IEEE International Conference on Knowledge Graph (IEEE ICKG)

作者： Li, Wei Xia, Le Huang, Ying Jiangxi Univ Sci & Technol Sch Informat Engn Ganzhou Peoples R China Gannan Normal Univ Inst Math & Comp Sci Ganzhou Peoples R China

ISBN: (纸本)9781728181561

Knowledge graphs have been widely used in various fields such as question answering systems and recommendation systems. However, there are few researchers on combinatorial optimization problems based on knowledge graphs, which greatly delays the development of knowledge graphs. Also, when solving combinatorial optimization problems only by using knowledge graphs, it is impossible to obtain better results. In order to solve these problems, an ant colony optimization algorithm based on an adaptive strategy (AACO) is proposed, and the algorithm is applied to solve the path optimization model established by the knowledge graph. In the vector space based on knowledge graph embedding, the ant colony optimization algorithm has a good positive feedback mechanism and robustness to find effective paths between entity nodes. Experimental results show that this proposed AACO algorithm can accelerate the convergence speed and obtain better accuracy. At the same time, a global optimal solution can be achieved, which is suitable for solving combinatorial optimization problems.

关键词： knowledge graph ant colony optimization algorithm adaptive strategy combinatorial optimization problems

来源：评论

学校读者我要写书评

暂无评论

Optimizing hyperparameters in Hopfield neural networks using evolutionary search

引用

OPSEARCH 2024年第3期61卷 1245-1273页

作者： Rbihou, Safae Haddouch, Khalid El Moutaouakil, Karim Sidi Mohamed Ben Abdellah Univ Engn Syst & Applicat Fes Morocco Sidi Mohamed Ben Abdellah Univ Fez Engn Sci Fes Morocco

The major problem facing users of Hopfield neural networks is the automatic choice of hyperparameters depending on the optimisation problem. This work introduces an automatic method to overcome this problem based on an original mathematical model minimizing the energy function. This methods ensures the feasibility of optimal solution obtained by decomposing the set of the feasible solutions. We illustrate the proposed model in the context of six well-known NP-hard problems: meeting scheduling problem, Kohonen network problem, portfolio selection problem, traveling salesman problem, task assignment problem, and max-stable problem. To show the effectiveness of our model, we use particle swarm and genetic algorithms to solve several instances of the last three problems. Numerical results show the good performance of the proposed approach compared to random tuning hyperparameters methods. Indeed, our approach permits an improvement of 49.75% for traveling salesman problem, 5.92% for task assignment problem, and 29.41% for max-stable problem.

关键词： Neural networks Hopfield neural networks Hyperparameters selection Genetic algorithms Particle swarm optimization combinatorial optimization problems

来源：评论

学校读者我要写书评

暂无评论

Enhancing PC cluster-based parallel branch-and-bound algorithms for the graph coloring problem

Enhancing PC cluster-based parallel branch-and-bound algorit...

引用

20th Workshop on Circuits and Systems

作者： Taoka, Satoshi Takafuji, Daisuke Watanabe, Toshimasa Hiroshima Univ Grad Sch Engn Higashihiroshima 7398527 Japan

A branch-and-bound algorithm (BB for short) is the most general technique to deal with various combinatorial optimization problems. Even if it is used, computation time is likely to increase exponentially. So we consider its parallelization to reduce it. It has been reported that the computation time of a parallel BB heavily depends upon node-variable selection strategies. And, in case of a parallel BB, it is also necessary to prevent increase in communication time. So, it is important to pay attention to how many and what kind of nodes are to be transferred (called sending-node selection strategy). In this paper, for the graph coloring problem, we propose some sending-node selection strategies for a parallel BB algorithm by adopting MPI for parallelization and experimentally evaluate how these strategies affect computation time of a parallel BB on a PC cluster network.

关键词： parallel branch-and-bound algorithms combinatorial optimization problems MPI optimum solutions

来源：评论

学校读者我要写书评

暂无评论

Parallelization of Bin Packing on Multicore Systems 23

Parallelization of Bin Packing on Multicore Systems

引用

23rd IEEE International Conference on High Performance Computing (HiPC)

作者： Ghosh, Sayan Gebremedhin, Assefaw H. Washington State Univ Sch Elect Engn & Comp Sci Pullman WA 99164 USA

ISBN: (纸本)9781509054114

We study effective parallelization of approximation algorithms for the one-dimensional bin packing problem on a multicore platform. Bin packing is a classic combinatorial optimization problem that aims to pack a given sequence of items into a minimum number of equal-sized bins. The problem potentially serves as a model for a wide variety of applications. Examples include: packing data into chunks in a memory hierarchy in a given system to increase application performance, loading vehicles subject to weight limitations, and packing TV commercials into station breaks. Bin packing has long served as a proving ground for the analysis of approximation algorithms and played a crucial role in the development of much of the theory of approximation algorithms. Its parallelization, however, has received comparatively much less attention. In this work, we develop multiple parallel versions of an effective approximation algorithm (First Fit Decreasing) for the problem and investigate the trade-off between solution quality and execution time. We use OpenMP and Cilk Plus as mechanisms for achieving the parallelization. The new parallel algorithms obtain a speedup of more than 10x (on 32 cores) for moderate to large input sequences without sacrificing much on the quality of solution produced by the sequential algorithm - in particular, we see only about 3 to 30% increase in the number of bins compared to the sequential version. In turn, the solution obtained by the sequential First Fit Decreasing algorithm is provably almost optimal (the approximation ratio is less than 1.3).

关键词： Bin packing First Fit Decreasing OpenMP combinatorial optimization problems

来源：评论

学校读者我要写书评

暂无评论

An Ant Colony Algorithm Assisted by Graph Neural Networks for Solving Vehicle Routing problems

An Ant Colony Algorithm Assisted by Graph Neural Networks fo...

引用

Genetic and Evolutionary Computation Conference (GECCO)

作者： Wang, Xiangyu Jin, Yaochu Bielefeld Univ Bielefeld Germany

ISBN: (纸本)9798400701207

Vehicle routing problems have attracted increasing attention because of the rapid development of transportation. Companies want to reduce the cost by lowering the number of vehicles and the total distances, which can be considered as a combinatorial optimization problem. The ant colony algorithm shows great potential in solving vehicle routing problems. However, it suffers from a low convergence speed due to the randomly initialized pheromone, which may cause a waste of computational resources in the early search process. To address this problem, a graph neural network is pre-trained to provide prior knowledge to initialize the pheromone in the ant colony algorithm, which can boost the convergence process. In addition, some classic local research methods are applied to balance the exploration and exploitation of the evolutionary process.

关键词： pre-trained neural networks graph neural networks ant colony optimization combinatorial optimization problems

来源：评论

学校读者我要写书评

暂无评论

Improved algorithm of transient chaotic neural network with combinatorial optimization 12

Improved algorithm of transient chaotic neural network with ...

引用

IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)

作者： Cong, Shuang Wang, Zhennin Univ Sci & Technol China Dept Automat Hefei 230027 Peoples R China

ISBN: (纸本)9798350321050

Transiently chaotic neural network (TCNN) and its improved versions have been proven to have search abilities for combinatorial optimization problem (COP). However, the TCNN may be able to maintain its solving ability while the chaotic dynamics are cut. In this paper, the mechanism of the continuous-time Hopfield neural network (CHNN) and the TCNN for COP are analyzed qualitatively from the view of the energy function. It is believed that the "annealing" progress, i.e., the dynamic relaxation of the energy function, helps the improvement of the TCNN over the CHNN. Another Hopfield network with a linear activation function and annealing strategy for the COP is proposed in this paper. Simulations on the TSP show that the improved network performs as well as TCNN but is much more efficient. The performance of the TCNN is improved when linear activation functions are used. Compared with the traditional sigmoid activation function, the improved network is more suitable for hardware implementation.

关键词： Hopfield neural network annealing progress improved TCNN linear activation function combinatorial optimization problems TSP

来源：评论

学校读者我要写书评

暂无评论

Solving Real-World Facility Layout problems Using GA with Levy Flights and Multi-Decoding 18

Solving Real-World Facility Layout Problems Using GA with Le...

引用

18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)

作者： Zhao, Dongqing Aranha, Claus Kanoh, Hitoshi Univ Tsukuba Grad Sch Syst & Informat Tsukuba Ibaraki Japan Univ Tsukuba Fac Engn Informat & Syst Tsukuba Ibaraki Japan

ISBN: (纸本)9781509055043

In this paper, we formulate a real-world Facility Layout Problem as a constraint satisfaction problem and propose a new method with a genetic algorithm. The created layout should reflect the desires of the users of the facility such as individuality, intuition, comfortableness, and convenience. We employed a GA using Levy Flights as basis for an improved mutation operator and used an island model GA with different decoding methods for each island. The experimental results show the effectiveness of the proposed method.

关键词： Levy Flight Facility Layout Decoding Island Model GA optimization combinatorial optimization problems

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：