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Optimal configuration problem identification of electrical power cable in tidal turbine farm via traveling salesman problem modeling approach

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Journal of Modern Power Systems and Clean Energy 2019年第2期7卷 289-296页

作者： Johanne Tomine VARTDAL Raad Yahya QASSIM Borge MOKLIEV Guttorm UDJUS Eduardo GONZáLEZ-GORBENA Ocean Engineering Department Center of TechnologyBl.Croom 205Cidade Universita′riaCOPPEFederal University of Rio de JaneiroRio de JaneiroBrazil Norwegian University of Science and Technology-NTNU NO-7491Trondheim UniversityTrondheimNorway Center of Marine and Environmental Research University of AlgarveCampus GambelasEd.78005-139 FaroPortugal

Electrical power cables in tidal turbine farms contribute a significant share to capital expenditure(CAPEX). As a result, the routing of electrical power cables connecting turbines to cable collector hubs must be designed so as to obtain the least cost *** is referred to as a tidal cable routing problem. This problem possesses several variants depending on the number of cable collector hubs. In this paper, these variants are modeled by employing the approach of the single depot multiple traveling salesman problem(mTSP) and the multiple depot mTSP of operational research for the single and multiple cable collector variants, respectively. The developed optimization models are computationally implemented using MATLAB. In the triple cable collector cable hub variant, an optimal solution is obtained, while good-quality suboptimal solutions are obtained in the double and single cable collector hub variants. In practice,multiple cable collector hubs are expected to be employed as the multiple-hub configurations tend to be more economic than the single-hub *** has been confirmed by this paper for an optimal tidal turbine layout obtained with OpenTidalFarm. Suggestions are presented for future research studies comprising a number of heuristics.

关键词： Tidal turbine cable routing problem Tidal current energy traveling salesman problem Cable collector hub Optimization

A Puzzle-Based Genetic Algorithm with Block Mining and Recombination Heuristic for the traveling salesman problem

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Journal of Computer Science & Technology 2012年第5期27卷 937-949页

作者：张百栈黄伟修张真真 Department of Information Management Yuan Ze UniversityTaoyuan 32026TaiwanChina Department of Computer Science Xiamen UniversityXiamen 361005China

In this research, we introduce a new heuristic approach using the concept of ant colony optimization （ACO） to extract patterns from the chromosomes generated by previous generations for solving the generalized traveling salesman problem. The proposed heuristic is composed of two phases. In the first phase the ACO technique is adopted to establish an archive consisting of a set of non-overlapping blocks and of a set of remaining cities （nodes） to be visited. The second phase is a block recombination phase where the set of blocks and the rest of cities are combined to form an artificial chromosome. The generated artificial chromosomes （ACs） will then be injected into a standard genetic algorithm （SGA） to speed up the convergence. The proposed method is called ＂Puzzle-Based Genetic Algorithm＂ or ＂p-ACGA＂. We demonstrate that p-ACGA performs very well on all TSPLIB problems, which have been solved to optimality by other researchers. The proposed approach can prevent the early convergence of the genetic algorithm （GA） and lead the algorithm to explore and exploit the search space by taking advantage of the artificial chromosomes.

关键词： artificial chromosome blocks mining block recombination traveling salesman problem

The Quantum Approximate Algorithm for Solving traveling salesman problem

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Computers, Materials & Continua 2020年第6期63卷 1237-1247页

作者： Yue Ruan Samuel Marsh Xilin Xue Zhihao Liu Jingbo Wang School of Computer Science and Technology Anhui University of TechnologyMaanshanChina School of Physics University of Western AustraliaPerthAustralia School of Computer Science and Engineering Southeast UniversityNanjingChina

The Quantum Approximate Optimization Algorithm(QAOA)is an algorithmic framework for finding approximate solutions to combinatorial optimization *** consists of interleaved unitary transformations induced by two operators labelled the mixing and problem *** fit this framework,one needs to transform the original problem into a suitable form and embed it into these two *** this paper,for the well-known NP-hard traveling salesman problem(TSP),we encode its constraints into the mixing Hamiltonian rather than the conventional approach of adding penalty terms to the problem ***,we map edges(routes)connecting each pair of cities to qubits,which decreases the search space significantly in comparison to other *** a result,our method can achieve a higher probability for the shortest round-trip route with only half the number of qubits consumed compared to IBM Q’s *** argue the formalization approach presented in this paper would lead to a generalized framework for finding,in the context of QAOA,high-quality approximate solutions to NP optimization problems.

关键词： Quantum approximate optimization algorithm traveling salesman problem NP optimization problems

A Multi-Agent Approach for Solving traveling salesman problem

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Wuhan University Journal of Natural Sciences 2006年第5期11卷 1104-1108页

作者： ZHOU Tiejun TAN Yihong XING Lining School of Computer and Communication HunanUniversity Changsha 410082 Hunan China Department of Information and Computer Science Changsha University Changsha 410003 Hunan China School of Management National University ofDefense Technology Changsha 410073 Hunan China

The traveling salesman problem （TSP） is a classical optimization problem and it is one of a class of NP- problem. This paper presents a new method named multiagent approach based genetic algorithm and ant colony system to solve the TSP. Three kinds of agents with different function were designed in the multi-agent architecture proposed by this paper. The first kind of agent is ant colony optimization agent and its function is generating the new solution continuously. The second kind of agent is selection agent, crossover agent and mutation agent, their function is optimizing the current solutions group. The third kind of agent is fast local searching agent and its function is optimizing the best solution from the beginning of the trial. At the end of this paper, the experimental results have shown that the proposed hybrid ap proach has good performance with respect to the quality of solution and the speed of computation.

关键词： traveling salesman problem multi-agent approach genetic algorithm ant colony system

Applying the Method for Solving traveling salesman problem Based on Backtracking Algorithm to Order Picking

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Open Journal of Optimization 2016年第2期5卷 84-89页

作者： Jie Zhu Ying Huang Lijuan Xu School of Information Beijing Wuzi University Beijing China

In the distribution center, the way of order picking personnel to pick goods has two kinds: single picking and batch picking. Based on the way of the single picking and assumed warehouse model, in order to reduce the walking path of order picking, the order picking problem is transformed into the traveling salesman problem in this paper. Based on backtracking algorithm, the order picking path gets optimized. Finally verifing the optimization method under the environment of VC++6.0, order picking path in the warehouse model get optimized, and compared with the traditional order picking walking paths. The results show that in small and medium-sized warehouse, the optimization method proposed in this paper can reduce order picking walking path and improve the work efficiency as well as reduce the time cost.

关键词： Single Picking Path Optimization traveling salesman problem Backtracking Algorithm

A Parallel Search System for Dynamic Multi-Objective traveling salesman problem

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Journal of Mathematics and System Science 2014年第5期4卷 295-314页

作者： Weiqi Li University of Michigan - Flint 303 E. Kearsley Street Flint M148502 U.S.A.

This paper introduces a parallel search system for dynamic multi-objective traveling salesman problem. We design a multi-objective TSP in a stochastic dynamic environment. This dynamic setting of the problem is very useful for routing in ad-hoc networks. The proposed search system first uses parallel processors to identify the extreme solutions of the search space for each ofk objectives individually at the same time. These solutions are merged into the so-called hit-frequency matrix E. The solutions in E are then searched by parallel processors and evaluated for dominance relationship. The search system is implemented in two different ways master-worker architecture and pipeline architecture.

关键词： dynamic multi-objective optimization traveling salesman problem parallel search algorithm solution attractor.

On the “Onion Husk” Algorithm for Approximate Solution of the traveling salesman problem

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Journal of Applied Mathematics and Physics 2024年第4期12卷 1557-1570页

作者： Mikhail E. Abramyan Nikolai I. Krainiukov Boris F. Melnikov Faculty of Computational Mathematics and Cybernetics Shenzhen MSU-BIT University Shenzhen China Department of Algebra and Discrete Mathematics Southern Federal University Rostov-on-Don Russian Federation

The paper describes some implementation aspects of an algorithm for approximate solution of the traveling salesman problem based on the construction of convex closed contours on the initial set of points (“cities”) and their subsequent combination into a closed path (the so-called contour algorithm or “onion husk” algorithm). A number of heuristics related to the different stages of the algorithm are considered, and various variants of the algorithm based on these heuristics are analyzed. Sets of randomly generated points of different sizes (from 4 to 90 and from 500 to 10,000) were used to test the algorithms. The numerical results obtained are compared with the results of two well-known combinatorial optimization algorithms, namely the algorithm based on the branch and bound method and the simulated annealing algorithm. .

关键词： Branch and Bound Method Contour Algorithm “Onion Husk” Algorithm Simulated Annealing Method traveling salesman problem

Novel Local Search Method for the traveling salesman problem

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Journal of Southwest Jiaotong University(English Edition) 2005年第1期13卷 1-4页

作者：黄文奇王磊 College of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

A new local search method for the traveling salesman problem based on an original greedy representation of solution space and neighborhood structure is proposed. First, a partial closed route that only consists of three cities is given; then other cities are added to this route by a greedy procedure successively. Implemented on a personal computer, this algorithm finds optimal solutions for 24 out of 27 standard benchmarks, and outperforms the Full Subpath Ejection Algorithm (F-SEC) proposed by Rego in 1998.

关键词： traveling salesman problem Heuristics Local search

Hybrid ant colony algorithm for traveling salesman problem

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Progress in Natural Science:Materials International 2003年第4期 57-61页

作者： HUANG Lan , ZHOU Chunguang and WANG Kangping(College of Computer Science and Technology, Jilin University, Changchun 130012, China)

A hybrid approach based on ant colony algorithm for the traveling salesman problem is proposed, which is an improved algorithm characterized by adding a local search mechanism, a cross-removing strategy and candidate ... 详细信息

关键词： hybrid ant colony algorithm traveling salesman problem combinatorial optimization.