3,330 research outputs found
Project for the analysis of technology transfer Quarterly report, 1 Oct. - 31 Dec. 1969
Analysis of Tech Brief-Technical Support Package progra
Traveling Salesman Problem
This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem. It brings together researchers with applications in Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm. Hybrid systems, like Fuzzy Maps, Chaotic Maps and Parallelized TSP are also presented. Most importantly, this book presents both theoretical as well as practical applications of TSP, which will be a vital tool for researchers and graduate entry students in the field of applied Mathematics, Computing Science and Engineering
Learning-Based Approaches for Graph Problems: A Survey
Over the years, many graph problems specifically those in NP-complete are
studied by a wide range of researchers. Some famous examples include graph
colouring, travelling salesman problem and subgraph isomorphism. Most of these
problems are typically addressed by exact algorithms, approximate algorithms
and heuristics. There are however some drawback for each of these methods.
Recent studies have employed learning-based frameworks such as machine learning
techniques in solving these problems, given that they are useful in discovering
new patterns in structured data that can be represented using graphs. This
research direction has successfully attracted a considerable amount of
attention. In this survey, we provide a systematic review mainly on classic
graph problems in which learning-based approaches have been proposed in
addressing the problems. We discuss the overview of each framework, and provide
analyses based on the design and performance of the framework. Some potential
research questions are also suggested. Ultimately, this survey gives a clearer
insight and can be used as a stepping stone to the research community in
studying problems in this field.Comment: v1: 41 pages; v2: 40 page
H-TSP: Hierarchically Solving the Large-Scale Travelling Salesman Problem
We propose an end-to-end learning framework based on hierarchical
reinforcement learning, called H-TSP, for addressing the large-scale Travelling
Salesman Problem (TSP). The proposed H-TSP constructs a solution of a TSP
instance starting from the scratch relying on two components: the upper-level
policy chooses a small subset of nodes (up to 200 in our experiment) from all
nodes that are to be traversed, while the lower-level policy takes the chosen
nodes as input and outputs a tour connecting them to the existing partial route
(initially only containing the depot). After jointly training the upper-level
and lower-level policies, our approach can directly generate solutions for the
given TSP instances without relying on any time-consuming search procedures. To
demonstrate effectiveness of the proposed approach, we have conducted extensive
experiments on randomly generated TSP instances with different numbers of
nodes. We show that H-TSP can achieve comparable results (gap 3.42% vs. 7.32%)
as SOTA search-based approaches, and more importantly, we reduce the time
consumption up to two orders of magnitude (3.32s vs. 395.85s). To the best of
our knowledge, H-TSP is the first end-to-end deep reinforcement learning
approach that can scale to TSP instances of up to 10000 nodes. Although there
are still gaps to SOTA results with respect to solution quality, we believe
that H-TSP will be useful for practical applications, particularly those that
are time-sensitive e.g., on-call routing and ride hailing service.Comment: Accepted by AAAI 2023, February 202
A Perturbed Self-organizing Multiobjective Evolutionary Algorithm to solve Multiobjective TSP
Travelling Salesman Problem (TSP) is a very important NP-Hard problem getting focused more on these days. Having improvement on TSP, right now consider the multi-objective TSP (MOTSP), broadened occurrence of travelling salesman problem. Since TSP is NP-hard issue MOTSP is additionally a NP-hard issue. There are a lot of algorithms and methods to solve the MOTSP among which Multiobjective evolutionary algorithm based on decomposition is appropriate to solve it nowadays. This work presents a new algorithm which combines the Data Perturbation, Self-Organizing Map (SOM) and MOEA/D to solve the problem of MOTSP, named Perturbed Self-Organizing multiobjective Evolutionary Algorithm (P-SMEA). In P-SMEA Self-Organizing Map (SOM) is used extract neighborhood relationship information and with MOEA/D subproblems are generated and solved simultaneously to obtain the optimal solution. Data Perturbation is applied to avoid the local optima. So by using the P-SMEA, MOTSP can be handled efficiently. The experimental results show that P-SMEA outperforms MOEA/D and SMEA on a set of test instances
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