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HR-kESP: A heuristic algorithm for Robustness-Oriented k Edge Server Placement 1

23rd International Conference on algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Hu, Haiquan Chen, Jifu Mao, Chengying Jiangxi Univ Finance & Econ Sch Software & IoT Engn Nanchang 330013 Jiangxi Peoples R China

ISBN: (数字)9789819708628

ISBN: (纸本)9789819708611;9789819708628

Mobile edge computing develops a new paradigm to deliver low-latency services to mobile users by deploying edge servers at base stations or access points close to the users. From the perspective of service providers, in this paper, we consider how to deploy k edge servers on suitable base stations to maximize network robustness and user coverage. Accordingly, the above two metrics are focused on constructing an optimization model for deploying a given number (i.e., k) of edge servers in the edge computing network. In order to solve the model efficiently, a heuristic algorithm for Robustness-oriented k Edge Server Placement (HR-kESP) is designed. In the proposed algorithm, the initial base station is first selected in a greedy manner. Then, a heuristic policy by considering the increment of network robustness is designed to gradually expand the deployment solution until the server budget constraint is satisfied. The comparison experiments have been conducted on a public dataset, showing that our HR-kESP algorithm achieves a better optimization effect than the other four benchmark algorithms in most cases.

关键词： Mobile Edge Computing Edge Server Placement Robustness heuristic algorithm

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A heuristic algorithm for Robust Adaptive Beamforming Design With Nonconvex Uncertainty Sets 43

A Heuristic Algorithm for Robust Adaptive Beamforming Design...

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43rd Chinese Control Conference (CCC)

作者： Xu, Wenjing Liu, Jinming Mou, Jiao Wang, Tingting Song, Enbin Sichuan Univ Coll Math Chengdu 610065 Peoples R China China Aerosp Sci & Technol Corp Sichuan Aerosp Res Inst Elect Equipment Chengdu 610100 Peoples R China

ISBN: (纸本)9798350366907;9789887581581

This paper considers a robust adaptive beamforming (RAB) design problem based on the worst-case signal-to-interference-plus-noise ratio (SINR) maximization, where the steering vector has a nonconvex uncertainty set consisting of a similarity constraint and a double-sided ball constraint. This problem can be turned into a quadratic matrix inequality problem, and a linear matrix inequality relaxation is explored. Two sufficient conditions are given to ensure the validity of strong duality and existence of a rank-one solution to the relaxation problem, respectively. Combining with the perturbation method, we propose a heuristic algorithm to find an optimal/suboptimal solution to the original RAB problem. Finally, simulations demonstrate that the proposed method can improve the array output SINR.

关键词： Worst-case SINR maximization robust adaptive beamforming heuristic algorithm

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Efficient heuristic algorithm for Precedence-Constrained Multi-Robot Task Assignment 43

Efficient Heuristic Algorithm for Precedence-Constrained Mul...

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43rd Chinese Control Conference (CCC)

作者： Bai, Xiaoshan Li, Baode Liu, Fuqiang Qiu, Li Cao, Guangzhong Shenzhen Univ Coll Mech & Control Engn Shenzhen 518060 Peoples R China Shenzhen Univ Guangdong Key Lab Elect Control & Intelligent Rob Shenzhen 518060 Peoples R China Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen 518060 Peoples R China Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China

ISBN: (纸本)9798350366907;9789887581581

Due to the widespread application of multi-robot systems, the efficient task assignment for a team of robots has become particularly critical for performing complex tasks. This paper studies the task assignment problem for multiple robots to visit a group of target locations with precedence constraints, which determine the order/sequence in which certain target locations need to be visited before others. A marginal-cost-based heuristic algorithm is developed to minimize the time for the robots to visit the last target location. The algorithm first uses a timestamp strategy to update the anticipated visiting time of each assigned target and the corresponding earliest time that each successor target can be visited under the precedence constraints. Then, it utilizes the topological sorting technique and the marginal-cost-based mechanism to insert the feasible target that induces the minimum marginal cost into the robots' current routes. Numerical simulations demonstrate the effectiveness of the proposed algorithm compared with the popular greedy algorithm and the existing simple iterative auction algorithm.

关键词： Multi-robot task assignment Precedence constraints heuristic algorithm Marginal cost

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A Branch-and-Price heuristic algorithm for Vehicle Routing Problem with Soft Time Windows 20th

A Branch-and-Price Heuristic Algorithm for Vehicle Routing P...

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20th International Conference on Intelligent Computing (ICIC)

作者： Li, Han Qian, Bin Yu, Nai-kang Hu, Rong Li, Zuo-cheng Kunming Univ Sci & Technol Sch Informat Engn & Automat Kunming 650500 Yunnan Peoples R China

ISBN: (纸本)9789819755776;9789819755783

A branch-and-price based heuristic algorithm (B&P_HA) is presented to solve the vehicle routing problem with soft time window (VRPSTW), whose objective is to minimize the total cost. In the B&P_HA, the linear problem (LP) relaxation of the set partitioning reformulation of the VRPSTW is solved by column generation. Specifically, a heuristic algorithm with greedy rules is firstly developed to construct initial columns, and then feasible columns are generated by using the special designed label correcting algorithm to solve the pricing subproblem (i.e., the single vehicle pricing subproblem). Furthermore, the branching scheme introduced is implemented when the LP's solution is fractional. Experimental results show that the B&P_HA algorithm outperforms the traditional branch-and-price algorithm.

关键词： Soft time window Branch-and-price Column generation heuristic algorithm

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A heuristic algorithm for Mathematical Markup Encoding Based on the Relative Positions of Characters 24

A Heuristic Algorithm for Mathematical Markup Encoding Based...

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24th Symposium on Document Engineering (DocEng)

作者： Lin, Chun-Min Lin, Jason Lin, Shin-Hung Liao, Jo-Kai Natl Chung Hsing Univ Taichung Taiwan Natl Taiwan Univ Taipei Taiwan Natl Yang Ming Chiao Tung Univ Hsinchu Taiwan

ISBN: (纸本)9798400711695

Mathematical expressions (MEs) are the most crucial technical content in scientific documents, yet their presentations are not easy to describe. However, LaTeX, one of the primary markup languages in mathematics, enables MEs to be easily understood by computer software. This paper presents a heuristic algorithm based on character relative positioning for converting any MEs in a PDF document into LaTeX markup strings. Unlike OCR-based approaches, our method takes a different approach, as it does not require processing the page images of the document. Instead, it utilizes embedded information within the PDF as input. By using precompiled PDF parsers that provide bounding boxes for each character and analyzing the relative positions of characters, we can effectively decode the mathematical encoding of MEs in LaTeX. To evaluate the performance of our proposed method compared to other related works, we utilized the publicly available dataset IM2LATEX-100K, which includes LaTeX mathematical formulas from high-energy physics and theoretical domains. The dataset was chosen due to its diverse mathematical structures and symbols and its sufficient size for validating the algorithm's performance and robustness. Experimental results demonstrate that our algorithm exhibits strong recognition capabilities, outperforming the state-of-the-art methods with a 4% improvement in image edit distance. The performance of our algorithm significantly surpasses existing methods, affirming the effectiveness of our proposed approach.

关键词： Mathematical expression content recognition heuristic algorithm PDF-to-LaTeX conversion

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Interruptible charge scheduling of plug-in electric vehicle to minimize charging cost using heuristic algorithm

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ELECTRICAL ENGINEERING 2022年第3期104卷 1425-1440页

作者： Mohammed, S. Sheik Ahamed, T. P. Imthias Aleem, Shady H. E. Abdel Omar, Ahmed, I TKM Coll Engn Elect & Elect Engn Dept Kollam India Sci Valley Acad Valley High Inst Engn & Technol Dept Elect Engn Qalyubia 44971 Egypt Higher Inst Engn El Shorouk City Elect Power & Machines Engn El Shorouk City Egypt

In recent times, transportation electrification has been recognized as one of the key solutions to accelerate global GHG emission reductions. As the electric vehicle industry grows faster, plug-in electric vehicles (PEV) are expected to be the most dominant load in the utility sector in less than a decade. Regular charging of the battery energy storage system (BESS) is a mandate for the continued operation of the vehicle, and the PEVs are connected to the utility to charge. Since PEVs are mobility loads, predicting the interconnection of these mobility loads in the utility network for recharging is a major challenge. The intermittent connection of mobility loads to the grid for charging leads to an unpredictable increase in electricity demand and other grid-related issues. Optimal scheduling of PEV charging would conquer the grid-related issues and provide financial benefits to the users. In this paper, an intelligent charge scheduling technique of PEV charging for both residential and commercial charging stations using the heuristic algorithm is proposed and discussed. The primary objective of the algorithm is to achieve the minimization of PEV charging costs by implementing an interrupted charging schedule. The proposed algorithm is tested by conducting exhaustive simulation studies under several conditions for PEVs with different power ratings for residential and commercial charging scenarios. The time-of-use pricing (ToUP) system is adopted as a tariff system in this paper. A detailed comparison of the unscheduled algorithm, the modified placement algorithm (MPA) and proposed heuristic technique-based charge scheduling is carried out through simulation studies. A detailed cost analysis for charging the PEVs with the selected charge scheduling techniques for various conditions is conducted and cost minimization by implementing the proposed charging scheme is validated.

关键词： Plug-in electric vehicle (PEV) Optimization Charge scheduling heuristic algorithm Interrupted scheduling Time-of-use pricing (ToUP) Modified placement algorithm (MPA)

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An Embedded Hamiltonian Graph-Guided heuristic algorithm for Two-Echelon Vehicle Routing Problem

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第7期52卷 5695-5707页

作者： Huang, Han Yang, Shuling Li, Xueqiang Hao, Zhifeng South China Univ Technol Sch Software Engn Guangzhou 510642 Peoples R China Dongguan Univ Technol Sch Comp Sci & Technol Dongguan 523000 Peoples R China Shantou Univ Coll Sci Shantou 515063 Guangdong Peoples R China

Two-echelon vehicle routing problem (2E-VRP) is an NP-hard combinatorial optimization problem and a basic mathematical model of modern city logistics. While it is difficult to obtain the optimal solution of 2E-VRP, this study finds a breakthrough that the structure of the optimal route planning for 2E-VRP is usually an embedded Hamiltonian graph. In the graph, routes can be drawn in a planar graph as Hamiltonian circuits without intersections. Based on this finding, an embedded Hamiltonian graph-guided heuristic algorithm is proposed to solve 2E-VRP. As a crucial part of the algorithm, an initialization scheme is designed to search for the farthest vertices from each route and insert the rest of the vertices. In the satellite-adjustment process, a dynamic adjustment for satellites scheme is proposed to adjust the state of satellites. The two schemes aim to construct Hamiltonian circuits with few intersections. Experiments have been conducted on 207 instances to demonstrate the effect of the proposed algorithm on solving 2E-VRP. Experimental results show that the proposed algorithm can obtain more solutions of 2E-VRP with significantly smaller objective-function values. Furthermore, the number of intersections in routes generated by the proposed algorithm is much less than those obtained by the compared algorithms. With the use of the two schemes, the embedded Hamiltonian graph-guided heuristic algorithm significantly outperforms the compared algorithms for 2E-VRP.

关键词： Satellites heuristic algorithms Vehicle routing Planning Mathematical models Costs Urban areas Combinatorial optimization dynamic adjustment for satellites (DAS) embedded Hamiltonian graph heuristic algorithm two-echelon vehicle routing problem (2E-VRP)

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An improved heuristic algorithm for the maximum benefit Chinese postman problem

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RAIRO-OPERATIONS RESEARCH 2022年第3期56卷 1283-1291页

作者： Matsuura, Shiori Takazawa, Kenjiro Hosei Univ Fac Sci & Engn Dept Ind & Syst Engn Koganei Tokyo Japan

The maximum benefit Chinese postman problem (MBCPP) is a practical generalization of the Chinese postman problem. A distinctive feature of MBCPP is that the postman can traverse each edge arbitrary times and obtains a benefit for each traversal of an edge, which depends on the number of times that the edge has been traversed. (Pearn and Wang, Omega 31 (2003) 269-273) discussed MBCPP under the assumption that the benefits of each edge is a non-increasing function of the number of traversals. They showed that MBCPP under this assumption is NP-hard, and proposed a heuristic algorithm which applies the minimum spanning tree and the minimal-cost T-join algorithms. (Corberan, Plana, Rodriguez-Chia and Sanchis, Math. Program. 141 (2013) 21-48) presented an integer programming formulation and a branch-and-cut algorithm for MBCPP without the assumption on the benefits. This is based on the idea of integrating the benefits of each edge into two benefits, each representing that the edge is traversed an odd or even number of times. In this paper, by applying the idea of Corberan et al., we improve the heuristic algorithm of Pearn and Wang. Our algorithm applies to the general case with no assumption on the benefits, and can perform better even if the benefits are non-increasing. We then analyze the efficiency of our heuristic algorithm in theory and in practice, and prove that it finds the optimal solution when the benefits satisfy a certain property.

关键词： Combinatorial optimization maximum benefit Chinese postman problem heuristic algorithm spanning tree T-join

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A New Direct Coefficient-Based heuristic algorithm for Set Covering Problems

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2022年第2期24卷 1131-1147页

作者： Hashemi, Ahmad Gholami, Hamed Venkatadri, Uday Karganroudi, Sasan Sattarpanah Khouri, Samer Wojciechowski, Adam Streimikiene, Dalia Univ Zanjan Dept Management & Accounting Univ Blvd Zanjan *** Iran Univ Teknol Malaysia UTM Dept Mfg & Ind Engn Johor Baharu 81310 Malaysia Dalhousie Univ Dept Ind Engn 6299 South St Halifax NS B3H 4R2 Canada Univ Quebec Trois Rivieres Dept Mech Engn Trois Rivieres PQ Canada Tech Univ Kosice Fac Min Ecol Proc Control & Geotechnol Letna 9 Kosice 04200 Slovakia Lodz Univ Technol Inst Informat Technol Wolczanska 215 PL-90924 Lodz Poland Vilnius Univ Kaunas Fac Muitines 8 LT-44280 Kaunas Lithuania

The set covering problem is a fundamental model which comprises a wide range of important applications such as crew scheduling problems that need to cover a set of trips. It is one of the most common issues in the facility location problem, which requires further investigations, particularly in emergency and service facilities. As such, the objective of this study is to propose a new coefficient-based heuristic algorithm for the set covering problems. This paper has accordingly presented the algorithm that evaluates the qualification of subsets by directly applying a fitness function. This fitness function is formulated based on sets and subsets coefficients in a way that the subsets of selected sets have the lowest probability to be selected in the next iteration. The algorithm is not only capable of constructing an answer within polynomial time, but can solve complex set covering problems without conventional restrictions. The performance of this algorithm is evaluated on benchmark instances including a set of reproduced and selected OR-library problems within different sizes. Computational results indicate that the proposed heuristic algorithm produces better solutions, especially in large-scale problems comparing simulated annealing in terms of quality and time.

关键词： Set covering problem heuristic algorithm Coefficient Optimization

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Path Planning for Cellular-connected UAV using heuristic algorithm and Reinforcement Learning 25

Path Planning for Cellular-connected UAV using Heuristic Alg...

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25th International Conference on Advanced Communication Technology (ICACT) - New Cyber Security Risks for Enterprise Amidst COVID-19 Pandemic

作者： Bao, Junqi Yang, Yunchu Wang, Yapeng Yang, Xu Du, Zhenyu Macao Polytechn Univ Fac Sci Appl Macau Peoples R China

ISBN: (纸本)9791188428106

With the development of Unmanned Aerial Vehicle (UAV), a novel technology called cellular-connected UAV has been proposed to make UAV complete its mission more efficiently. We consider a scenario where UAV must take off from a random start point, travel over some specific points (e.g. collecting data from sparce sensors in large area) and reach a random end point while keep connected to the Ground Base Station. One of the major challenges is to plan the flying path of UAV while satisfies all constraints. We abstract the path planning problem into Travel Salesman Problem (TSP) and use A* combine with Genetic algorithm, Simulated Annealing algorithm and Reinforcement Learning Model to solve TSP to get the best path for cellular-connected UAV. In addition, we did experiments and recorded the results to analyze the advantages and disadvantages of these algorithms.

关键词： UAV Cellular-connected UAV path planning heuristic algorithm Reinforcement Learning Travel Salesman Problem

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