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Higher-Order Multicuts for Geometric Model Fitting and Motion Segmentation

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第1期45卷 608-622页

作者： Levinkov, Evgeny Kardoost, Amirhossein Andres, Bjoern Keuper, Margret Bosch Ctr Artificial Intelligence D-71272 Renningen Baden Wurttembe Germany Univ Mannheim Data & Web Sci Grp D-68131 Mannheim Baden Wurttembe Germany Tech Univ Dresden D-01069 Dresden Saxony Germany Ctr Syst Biol Dresden CSBD D-01307 Dresden Saxony Germany Univ Siegen D-57076 Siegen Germany Max Planck Inst Informat Saarland Informat Campus D-66123 Saarbrucken Saarland Germany

The minimum cost lifted multicut problem is a generalization of the multicut problem (also known as correlation clustering) and is a means to optimizing a decomposition of a graph w.r.t. both positive and negative edge costs. It has been shown to be useful in a large variety of applications in computer vision thanks to the fact that multicut-based formulations do not require the number of components given a priori;instead, it is deduced from the solution. However, the standard multicut cost function is limited to pairwise relationships between nodes, while several important applications either require or can benefit from a higher-order cost function, i.e., hyper-edges. In this paper, we propose a pseudo-boolean formulation for a multiple model fitting problem. It is based on a formulation of any-order minimum cost lifted multicuts, which allows to partition an undirected graph with pairwise connectivity such as to minimize costs defined over any set of hyper-edges. As the proposed formulation is np-hard and the branch-and-bound algorithm (as well as obtaining lower bounds) is too slow in practice, we propose an efficient local search algorithm for inference into resulting problems. We demonstrate versatility and effectiveness of our approach in several applications: 1) We define a geometric multiple model fitting, more specifically, a line fitting problem on all triplets of points and group points, that belong to the same line, together. 2) We formulate homography and motion estimation as a geometric model fitting problem where the task is to find groups of points that can be explained by the same geometrical transformation. 3) In motion segmentation our model allows to go from modeling translational motion to euclidean or affine transformations, which improves the segmentation quality in terms of F-measure.

关键词： Motion segmentation Computer vision Geometric modeling Costs Task analysis Data models Computational modeling Combinatorial optimization local search algorithm higher-order multicut motion segmentation geometric model fitting

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Correction Method of Reference Flux for Maximum Torque per Ampere Control in Direct-Torque-Controlled IPMSM Drives

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IEEJ JOURNAL OF INDUSTRY APPLICATIONS 2017年第1期6卷 12-18页

作者： Shinohara, Atsushi Inoue, Yukinori Morimoto, Shigeo Sanada, Masayuki Osaka Prefecture Univ Naka Ku 1-1 Gakuen Cho Sakai Osaka 5998531 Japan

In this paper, a maximum torque per ampere (MTPA) control strategy for direct-torque-controlled interior permanent magnet synchronous motor (IPMSM) drives is proposed. For current-controlled IPMSM drives, many methods have been proposed, in which the periodic signals are injected into reference current vectors. On the other hand, the direct-torque-controlled IPMSM drives require the torque and stator flux linkage magnitude as their references. The proposed method injects signals into the reference flux in a manner similar to that used for the signal injections in current-controlled IPMSM drives. The main feature of the proposed method is the ability to search the optimal current directly and successively using a local search algorithm. Therefore, variations in the parameters can be considered. The simulation and experimental results validate the proposed method.

关键词： permanent magnet synchronous motors direct torque control maximum torque per ampere control local search algorithm

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Piecewise Convex Maximization Problems: Piece Adding Technique

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 2011年第3期148卷 471-487页

作者： Fortin, Dominique Tseveendorj, Ider Univ Versailles St Quentin Yvelines Lab PRiSM F-78035 Versailles France INRIA F-78153 Le Chesnay France

In this article, we provide a global search algorithm for maximizing a piecewise convex function F over a compact D. We propose to iteratively refine the function F at local solution y by a virtual cutting function p (y) (a <...) and to solve max {min {F(x)-F(y),p (y) (x)}a xaD} pound instead. We call this function either a patch, when it avoids returning back to the same local solutions, or a pseudo patch, when it possibly yields a better point. It is virtual in the sense that the role of cutting constraints is played by additional convex pieces in the objective function. We report some computational results, that represent an improvement on previous linearization based techniques.

关键词： Global search algorithm local search algorithm Nonconvex optimization Convex maximization Piecewise convex maximization

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Instance scale, numerical properties and design of metaheuristics: A study for the facility location problem

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IFAC-PapersOnLine 2019年第13期52卷 2219-2224页

作者： Chalupa, David Nielsen, Peter Operations Research Group Department of Materials and Production Aalborg University Fibigerstræde 16 Aalborg9220 Denmark

We present an in-depth computational study of two local search metaheuristics for the classical uncapacitated facility location problem. We investigate four problem instance models, studied for the same problem size, for which the two metaheuristics exhibit intriguing and contrasting behaviours. The metaheuristics explored include a local search (LS) algorithm that chooses the best moves in the current neighbourhood, while a randomised local search (RLS) algorithm chooses the first move that does not lead to a worsening. The experimental results indicate that the right choice between these two algorithms depends heavily on the distribution of coefficients within the problem instance. This is also put further into context by finding optimal or near-optimal solutions using a mixed-integer linear programming problem solver. © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Integer programming Combinatorial optimization Heuristic algorithms local search (optimization) Location algorithm efficiency Facility location problem Integer Linear Programming local search local search algorithm local search metaheuristics Mixed integer linear programming problems Uncapacitated facility locations

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Multi-objective mapping of full-mission simulators on heterogeneous distributed multi-processor systems

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JOURNAL OF DEFENSE MODELING AND SIMULATION-APPLICATIONS METHODOLOGY TECHNOLOGY-JDMS 2018年第4期15卷 449-460页

作者： Ayari, Rabeh Hafnaoui, Imane Aguiar, Alexandra Gilbert, Patricia Galibois, Michel Rousseau, Jean-Pierre Beltrame, Giovanni Nicolescu, Gabriela Ecole Polytech Montreal 2900 Blvd Eduard Monpetit Montreal PQ H3T 1J4 Canada CAE Global Dev Engn Dept Montreal PQ Canada CAE Montreal PQ Canada

Full-mission simulators (FMSs) are considered the most critical simulation tool belonging to the flight simulator family. FMSs include a faithful reproduction of fighter aircraft. They are used by armed forces for design, training, and investigation purposes. Due to the criticality of their timing constraints and the high computation cost of the whole simulation, FMSs need to run in a high-performance computing system. Heterogeneous distributed systems are among the leading computing platforms and can guarantee a significant increase in performance by providing a large number of parallel powerful execution resources. One of the most persistent challenges raised by these platforms is the difficulty of finding an optimal mapping of n tasks on m processing elements. The mapping problem is considered a variant of the quadratic assignment problem, in which an exhaustive search cannot be performed. The mapping problem is an NP-hard problem and solving it requires the use of meta-heuristics, and it becomes more challenging when one has to optimize more than one objective with respect to the timing constraints. Multi-objective evolutionary algorithms have proven their efficiency when tackling this problem. Most of the existent works deal with the task mapping by considering either a single objective or homogeneous architectures. Therefore, the main contribution of this paper is a framework based on the model-driven design paradigm allowing us to map a set of intercommunicating real-time tasks making up the FMS model onto the heterogeneous distributed multi-processor system model. We propose a multi-objective approach based on the well-known optimization algorithm Non-dominated Sorting Genetic algorithm-II satisfying the tight timing constraints of the simulation and minimizing makespan, communication cost, and memory consumption simultaneously.

关键词： Full-mission simulators real-time systems heterogeneous computing architectures mapping problem schedulability analysis multi-objective optimization local search algorithm meta-heuristics

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Modelling and solving for ready-mixed concrete scheduling problems with time dependence

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INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2013年第2期4卷 163-175页

作者： Zhang, Guo-Chen Zeng, Jian-Chao Lanzhou Univ Technol Coll Elect & Informat Engn Lanzhou 730050 Gansu Peoples R China Taiyuan Univ Sci & Technol Complex Syst & Computat Intelligence Lab Taiyuan 030024 Shanxi Peoples R China

In this paper, we present a formulation for the ready-mixed concrete scheduling problems with time-dependent travel time. Traffic congestion is a major problem for the business of delivering RMC. It may lead to late deliveries of RMC to construction sites, and because RMC is perishable, late deliveries also mean large additional costs for both batch plants and the construction companies. Additional costs caused by traffic congestion can be reduced by taking predictable traffic congestion into account in the process of scheduling. For this purpose we develop a model with consideration of traffic congestion of RMC which uses a heuristic approach based on local search algorithm to solve. A detailed case study based on industrial data is used to illustrate the potential of the proposed approach.

关键词： ready-mixed concrete RMC traffic congestion local search algorithm heuristic approach

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Individual haplotype assembly of Apis mellifera (honeybee) using a practical branch and bound algorithm

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JOURNAL OF ASIA-PACIFIC ENTOMOLOGY 2012年第3期15卷 375-381页

作者： Lim, Hyeong-Seok Jeong, In-Seon Kang, Seung-Ho KT Daeduk 2 Res Ctr Natl Inst Math Sci Div Fus Convergence Math Sci Taejon 305811 South Korea Chonnam Natl Univ Dept Comp Sci Kwangju 500757 South Korea Natl Acad Agr Sci Rural Dev Adm Dept Agr Bioresources Suwon 441855 South Korea

A haplotype is a single nucleotide polymorphism (SNP) sequence and a representative genetic marker describing the diversity of biological organs. Haplotypes have a wide range of applications such as pharmacology and medical applications. In particular, as a highly social species, haplotypes of the Apis mellifera (honeybee) benefit human health and medicine in diverse areas, including venom toxicology, infectious disease, and allergic disease. For this reason, assembling a pair of haplotypes from individual SNP fragments drives research and generates various computational models for this problem. The minimum error correction (MEC) model is an important computational model for an individual haplotype assembly problem. However, the MEC model has been proved to be NP-hard;therefore, no efficient algorithm is available to address this problem. In this study, we propose an improved version of a branch and bound algorithm that can assemble a pair of haplotypes with an optimal solution from SNP fragments of a honeybee specimen in practical time bound. First, we designed a local search algorithm to calculate the good initial upper bound of feasible solutions for enhancing the efficiency of the branch and bound algorithm. Furthermore, to accelerate the speed of the algorithm, we made use of the recursive property of the bounding function together with a lookup table. After conducting extensive experiments over honeybee SNP data released by the Human Genome Sequencing Center, we showed that our method is highly accurate and efficient for assembling haplotypes. (C) Korean Society of Applied Entomology, Taiwan Entomological Society and Malaysian Plant Protection Society, 2012. Published by Elsevier B.V. All rights reserved,

关键词： Apis mellifera (honeybee) Single nucleotide polymorphism (SNP) Haplotype assembly local search algorithm Branch and bound algorithm

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A new integral filter algorithm for unconstrained global optimization

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NUMERICAL algorithmS 2013年第3期63卷 419-430页

作者： Yue, Lina Yang, Yongjian Shanghai Univ Dept Math Shanghai 200444 Peoples R China

In this paper, making use a exponential integral filter, a new algorithm for unconstrained global optimization is proposed. Compared with Yang's absolute value type integral filter method (Yang et al., Appl Math Comput 18:173-180, 2007), this algorithm is more effective and more sensitive. Numerical results for some typical examples show that in most cases, this algorithm works effectively and reliably.

关键词： Global optimization Integral filter Branch and bound local search algorithm

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Optimizing train stopping patterns for congestion management

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PUBLIC TRANSPORT 2023年第1期15卷 1-29页

作者： Yamauchi, Tatsuki Takamatsu, Mizuyo Imahori, Shinji Internet Initiat Japan Inc Tokyo 1020071 Japan Chuo Univ Dept Informat & Syst Engn Tokyo 1128551 Japan

In this paper, we optimize train stopping patterns during the morning rush hour in Japan. Since trains are extremely crowded, we need to determine stopping patterns based not only on travel time but also on congestion rates of trains. We exploit a Wardrop equilibrium model to compute passenger flows subject to congestion phenomena and present an efficient local search algorithm to optimize stopping patterns which iteratively computes a Wardrop equilibrium. The framework of the proposed algorithm is extended to solve the problem of optimizing the number of services for each train type. We apply our algorithms to railway lines in Tokyo including the Keio Line with six types of trains and demonstrate that we succeed in relaxing congestion.

关键词： Train stopping pattern Wardrop equilibrium local search algorithm Event-activity network

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Development, application, and comparison of hybrid meta-heuristics for urban land-use allocation optimization: Tabu search, genetic, GRASP, and simulated annealing algorithms

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COMPUTERS ENVIRONMENT AND URBAN SYSTEMS 2016年第0期60卷 23-36页

作者： Mohammadi, Mahmoud Nastaran, Mahin Sahebgharani, Alireza Art Univ Isfahan Fac Architecture & Urban Planning Dept Urban Planning Esfahan Iran

Land-use optimization problem (LUOP) that seeks to allocate different land types to land units involves various dimensions and deals with numerous conflicting objectives and a large set of data and variables. Single meta heuristics are broadly developed and applied for solving LUOP. Despite the acceptable solutions derived from these algorithms, researchers in both academic and practical areas face the interesting question: can we develop an algorithm with better efficiency and solution quality? In the literature of operation research, hybridization, a combination of meta-heuristics, was introduced as a way of generating better algorithms. Therefore, this paper aims at developing novel algorithms through hybridizing Tabu search (TS), genetic algorithm (GA), GRASP, and simulated annealing (SA) and examining their quality and efficiency in practice. Accordingly, low-level teamwork GRASP-GA-TS (LLTGRGATS), high-level relay Greedy-GA-TS, and high-level teamwork SA were developed. Firstly, these algorithms were applied for solving small- and large-size single-row facility layout problem to evaluate their performance and functionality and to select the satisfactory algorithm in comparison with the other developed hybrids. Secondly, the selected algorithm, LLTGRGATS, and SVNS, a recent hybrid algorithm proposed for solving LUOP, were performed on a study area to solve a LUOP with two constraints and seven nonlinear objective functions. The outputs showed that the quality and efficiency of LLTGRGATS were slightly better than those of SVNS and it can be considered as a favorable tool for land-use planning process. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Land-use allocation Land-use optimization Hybrid meta-heuristic Population-based algorithm local search algorithm

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