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A two-phase algorithm for point-feature cartographic label placement

EARTH SCIENCE INFORMATICS 2018年第2期11卷 183-203页

作者： Ding, Yuan Jiang, Nan Wu, Changbin Zhou, Xinxin Nanjing Normal Univ Minist Educ Key Lab Virtual Geog Environm Nanjing 210023 Jiangsu Peoples R China Nanjing Normal Univ Coll Geog Sci Nanjing 210023 Jiangsu Peoples R China Jiangsu Ctr Collaborat Innovat Geog Informat Reso Nanjing 210023 Jiangsu Peoples R China

Point-feature cartographic label placement (PFCLP) involves placing labels adjacent to their corresponding point features on a map. A widely accepted goal of PFCLP is to maximize the number of conflict-free labels. This paper presents an algorithm for PFCLP based on the four-slider (4S) model. The algorithm is composed of two phases: an initialization phase during which an initial solution is constructed by an exact algorithm and a heuristic method to maximize the probability of conflict-free labels. The initialization phase is followed by an improvement phase that adopts a backtracking greedy search. The exact algorithm can find a portion of the conflict-free labels in an optimal solution and an extension of the exact algorithm is provided that can find additional conflict-free labels. Computational tests were performed for instances based on standard sets. The two-phase algorithm generated better solutions relative to all methods previously reported in the literature. It also executes at a reasonable speed and is more stable than most other methods.

关键词： two-phase algorithm Backtracking greedy search Map labeling Sliding labels

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A simplified two-phase differential decoding algorithm for high precision grating encoder 8

A simplified two-phase differential decoding algorithm for h...

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Conference on Optical Metrology and Inspection for Industrial Applications VIII

作者： Zhu, Junhao Yu, Kangning Xue, Gaopeng Shi, Ningning Zhou, Qian Wang, Xiaohao Li, Xinghui Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Shenzhen 518055 Peoples R China

ISBN: (数字)9781510646483

ISBN: (纸本)9781510646483;9781510646476

The signal processing of the grating encoder has a great impact on its accuracy and resolution. We proposed a new type of signal processing method for a grating encoder using a two-phase differential algorithm based on the two-phase physical structure. The interference signal could be divided into two phases with 90 degrees phase delay, capable of effectively reducing the number of optical devices and the space occupied by the reading head. Owing to the rapid elimination of the DC component in the measurement signal, the measurement displacement was solved swiftly by the two-phase signal using the algorithm. In the experiment, a 660 nm laser and a 1 mu m-period grating were used, and the scale grating was actuated at a speed of 1 mu m/s by a linear stage. With a sampling rate of 20 kHz, the system resolution of the grating encoder was enabled to reach 50 pm. Simultaneously, there was a measurement error of +/- 1 mu m at a stroke of 4 mm, and the error within a single cycle was 2 nm. Compared with the four-phase algorithm, our proposed two-phase differential algorithm exhibits a compact physical structure and fast solution without reducing the accuracy and resolution, which will be of great significance to the real-time measurement and miniaturization of grating encoders.

关键词： grating encoder two-phase algorithm differential algorithm displacement sensor signal processing

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A two-phase adaptive variable neighborhood approach for nurse rostering

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COMPUTERS & OPERATIONS RESEARCH 2015年 60卷 150-169页

作者： Tassopoulos, Ioannis X. Solos, Ioannis P. Beligiannis, Grigorios N. Univ Patras Dept Business Adm Food & Agr Enterprises GR-30100 Agrinion Greece

This contribution presents a two-phase variable neighborhood search algorithm for solving nurse rostering problems. In order to demonstrate the efficiency of the proposed algorithm, it is firstly applied to all nurse rostering problem instances as proposed in the First International Nurse Rostering Competition (INRC-2010). Computational results assessed on all three sets of sixty competition instances demonstrate that the proposed algorithm improves the best known results for two instances, inside the time limits of the competition, while achieving the best known bounds for forty eight other instances. The proposed algorithm was also applied to seven other nurse rostering instances reported in the respective literature and managed to achieve the best known result in six of them while improving the best known result in one instance. The proposed algorithm, as well as its differences from existing approaches are presented, described and discussed in detail. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Nurse rostering Hospital personnel scheduling Variable neighborhood two-phase algorithm

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Α two-phase adaptive variable neighborhood approach for nurse rostering

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Computers & Operations Research 2015年 60卷 150-169页

作者： Ioannis X. Tassopoulos Ioannis P. Solos Grigorios N. Beligiannis Department of Business Administration of Food and Agricultural Enterprises University of Patras G. Seferi 2 GR-30100 Agrinio Greece

This contribution presents a two-phase variable neighborhood search algorithm for solving nurse rostering problems. In order to demonstrate the efficiency of the proposed algorithm, it is firstly applied to all nurse rostering problem instances as proposed in the First International Nurse Rostering Competition ( INRC-2010 ). Computational results assessed on all three sets of sixty competition instances demonstrate that the proposed algorithm improves the best known results for two instances, inside the time limits of the competition, while achieving the best known bounds for forty eight other instances. The proposed algorithm was also applied to seven other nurse rostering instances reported in the respective literature and managed to achieve the best known result in six of them while improving the best known result in one instance. The proposed algorithm, as well as its differences from existing approaches are presented, described and discussed in detail.

关键词： Nurse rostering Hospital personnel scheduling Variable neighborhood two-phase algorithm

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A Generic two-phase Stochastic Variable Neighborhood Approach for Effectively Solving the Nurse Rostering Problem

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algorithmS 2013年第2期6卷 278-308页

作者： Solos, Ioannis P. Tassopoulos, Ioannis X. Beligiannis, Grigorios N. Univ Western Greece Dept Business Adm Food & Agr Enterprises G Seferi 2 Agrinion 30100 Greece

In this contribution, a generic two-phase stochastic variable neighborhood approach is applied to nurse rostering problems. The proposed algorithm is used for creating feasible and efficient nurse rosters for many different nurse rostering cases. In order to demonstrate the efficiency and generic applicability of the proposed approach, experiments with real-world input data coming from many different nurse rostering cases have been conducted. The nurse rostering instances used have significant differences in nature, structure, philosophy and the type of hard and soft constraints. Computational results show that the proposed algorithm performs better than six different existing approaches applied to the same nurse rostering input instances using the same evaluation criteria. In addition, in all cases, it manages to reach the best-known fitness achieved in the literature, and in one case, it manages to beat the best-known fitness achieved till now.

关键词： nurse rostering hospital personnel scheduling stochastic variable neighborhood two-phase algorithm mutation element swap selective mutation reduce rosters' cost

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Using hybrid multiobjective machine learning to optimise sonobuoy placement patterns

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IET RADAR SONAR AND NAVIGATION 2023年第3期17卷 374-387页

作者： Taylor, Christopher M. Maskell, Simon Ralph, Jason F. Univ Liverpool Dept Elect & Elect Engn Brownlow Hill Liverpool L69 3GJ Merseyside England

This paper presents a new approach to finding optimal patterns for the placement of fields of sonobuoys in a complex undersea environment. We model the problem as a biobjective one, where the aim is to minimise both sensor placement time and uncertainty over target localisation. Both objectives may be important in time-critical localisation scenarios and our approach allows an operator to choose between different optimal solutions, favouring lower placement time or lower localisation uncertainty as operational circumstances require. We develop a two-phase algorithm, where an offline multiobjective evolutionary phase finds initial Pareto-non-dominated solutions to a static problem and then an online multiobjective reinforcement learning phase finds improved solutions using updated information. We find that the evolutionary algorithm improves significantly on standard grid patterns and that the reinforcement learning algorithm improves further on the evolutionary phase. The number of sonobuoys required may also be reduced.

关键词： optimal patterns Optimisation techniques static problem standard grid patterns Pareto optimisation sensor placement time evolutionary computation offline multiobjective evolutionary phase telecommunication computing initial Pareto-nondominated solutions operational circumstances complex undersea environment placement time localisation uncertainty reinforcement learning algorithm sonobuoy placement patterns evolutionary algorithm online multiobjective reinforcement learning phase two-phase algorithm Reinforcement learning target localisation time-critical localisation scenarios hybrid multiobjective machine Communications computing reinforcement learning sensor placement Wireless sensor networks

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An incremental mining algorithm for high utility itemsets

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第8期39卷 7173-7180页

作者： Lin, Chun-Wei Lan, Guo-Cheng Hong, Tzung-Pei Natl Univ Kaohsiung Dept Comp Sci & Informat Engn Kaohsiung 811 Taiwan Natl Univ Kaohsiung Gen Educ Ctr Kaohsiung 811 Taiwan Natl Cheng Kung Univ Dept Comp Sci & Informat Engn Tainan 701 Taiwan Natl Sun Yat Sen Univ Dept Comp Sci & Engn Kaohsiung 804 Taiwan

Association-rule mining, which is based on frequency values of items, is the most common topic in data mining. In real-world applications, customers may, however, buy many copies of products and each product may have different factors, such as profits and prices. Only mining frequent itemsets in binary databases is thus not suitable for some applications. Utility mining is thus presented to consider additional measures, such as profits or costs according to user preference. In the past, a two-phase mining algorithm was designed for fast discovering high utility itemsets from databases. When data come intermittently, the approach needs to process all the transactions in a batch way. In this paper, an incremental mining algorithm for efficiently mining high utility itemsets is proposed to handle the above situation. It is based on the concept of the fast-update (FUP) approach, which was originally designed for association mining. The proposed approach first partitions itemsets into four parts according to whether they are high transaction-weighted utilization itemsets in the original database and in the newly inserted transactions. Each part is then executed by its own procedure. Experimental results also show that the proposed algorithm executes faster than the two-phase batch mining algorithm in the intermittent data environment (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Utility mining High utility itemset Incremental mining FUP concept two-phase algorithm

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Mining high utility itemsets for transaction deletion in a dynamic database

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INTELLIGENT DATA ANALYSIS 2015年第1期19卷 43-55页

作者： Lin, Chun-Wei Lan, Guo-Cheng Hong, Tzung-Pei Harbin Inst Technol Shenzhen Grad Sch Sch Comp Sci & Technol Innovat Informat Ind Res Ctr Shenzhen Guangdong Peoples R China Harbin Inst Technol Shenzhen Grad Sch Sch Comp Sci & Technol Shenzhen Key Lab Internet Informat Collaborat Shenzhen Guangdong Peoples R China Ind Technol Res Inst Computat Intelligence Technol Ctr Hsinchu Taiwan Natl Univ Kaohsiung Dept Comp Sci & Informat Engn Kaohsiung Taiwan Natl Sun Yat Sen Univ Dept Comp Sci & Engn Kaohsiung 80424 Taiwan

Association-rule mining is used to mine the relationships among the occurrences itemsets in a transactional database. An item is treated as a binary variable whose value is one if it appears in a transaction and zero otherwise. In real-world applications, several products may be purchased at the same time, with each product having an associated profit, quantity, and price. Association-rule mining from a binary database is thus not sufficient in some applications. Utility mining was thus proposed as an extension of frequent-itemset mining for considering various factors from the user. Most utility mining approaches can only process static databases and use batch processing. In real-world applications, transactions are dynamically inserted into or deleted from databases. The Fast UPdated (FUP) algorithm and the FUP2 algorithm were respectively proposed to handle transaction insertion and deletion in dynamic databases. In this paper, a fast-updated high-utility itemsets for transaction deletion (FUP-HUI-DEL) algorithm is proposed to handle transaction deletion for efficiently updating discovered high utility itemsets in decremental mining. The two-phase approach in high utility mining is applied to the proposed FUP-HUI-DEL algorithm for preserving the downward closure property to reduce the number of candidates. The FUP2 algorithm for handling transaction deletion in association-rule mining is adopted in the proposed FUP-HUI-DEL algorithm to reduce the number of scans of the original database in high utility mining. Experiments show that the proposed FUP-HUI-DEL algorithm outperforms the batch two-phase approach.

关键词： High utility mining decremental mining transaction deletion two-phase algorithm dynamic database

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A Multiobjective Approach for Sector Duration Optimization in Stereotactic Radiosurgery Treatment Planning

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INFORMS JOURNAL ON COMPUTING 2023年第1期35卷 248-264页

作者： Seker, Oylum Cevik, Mucahit Bodur, Merve Lee, Young Ruschin, Mark Univ Toronto Dept Mech & Ind Engn Toronto ON M5S 3G8 Canada Ryerson Univ Dept Mech & Ind Engn Toronto ON M5B 2K3 Canada Elekta Oncol Syst Crawley RH10 9BL West Sussex England Univ Toronto Sunnybrook Hlth Sci Ctr Dept Radiat Oncol Toronto ON M4N 3M5 Canada

Sector duration optimization (SDO) is a problem arising in treatment planning for stereotactic radiosurgery on Gamma Knife. Given a set of isocenter locations, SDO aims to select collimator size configurations and irradiation times thereof such that target tissues receive prescribed doses in a reasonable amount of treatment time and healthy tissues nearby are spared. We present a multiobjective linear programming model for SDO to gener-ate a diverse collection of solutions so that clinicians can select the most appropriate treat-ment. We develop a generic two-phase solution strategy based on the epsilon-constraint method for solving multiobjective optimization models, 2PHAS epsilon, which aims to systematically increase the number of high-quality solutions obtained, instead of conducting a traditional uniform search. To improve solution quality further and to accelerate the procedure, we incorporate some general and problem-specific enhancements. Moreover, we propose an alternative version of 2PHAS epsilon, which makes use of machine learning tools to reduce the com-putational effort. In our computational study on eight previously treated real test cases, a significant portion of 2PHAS epsilon solutions outperformed clinical results and those from a single -objective model from the literature. In addition to significant benefits of the algorithmic enhancements, our experiments illustrate the usefulness of machine learning strategies to reduce the overall run times nearly by half while maintaining or besting the clinical practice.

关键词： multiobjective optimization sector duration optimization stereotactic radiosurgery ?-constraint method two-phase algorithm

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Chameleon: Hierarchical clustering using dynamic modeling

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COMPUTER 1999年第8期32卷 68-+页

作者： Karypis, G Han, EH Kumar, V Univ Minnesota Dept Comp Sci & Engn Minneapolis MN 55455 USA

Many advanced algorithms have difficulty dealing with highly variable clusters that do not follow a preconceived model. By basing its selections on both interconnectivity and closeness, the Chameleon algorithm yields ... 详细信息

关键词： CURE algorithm Chameleon algorithm Rock algorithm advanced algorithms aggregate interconnectivity closeness closest items data analysis data item clustering data mining data mining data set discovery process dynamic modeling graph partitioning algorithm graph theory hierarchical clustering highly variable clusters most similar pair pattern clustering subclusters two-phase algorithm

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