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A greedy search Technique for Multiple Contaminant Water Network Synthesis

PROCESS INTEGRATION AND OPTIMIZATION FOR SUSTAINABILITY 2023年第4期7卷 931-940页

作者： Kamat, Shweta Gupta, Abhishek Bandyopadhyay, Santanu Indian Inst Technol Dept Energy Sci & Engn Mumbai 400076 India Univ Lincoln Sch Engn Lincoln LN6 7TS England Indian Inst Technol Dept Mech Engn Mumbai 400076 India

One of the major impacts of climate change is the scarcity of water resources. Various techniques have been employed across different sectors to conserve water. Process integration is one of the routes adopted to save water in the process industry. In this pathway, water is reused and recycled within the industry. Raw materials used and the processes performed introduce multiple contaminants in the water streams, limiting their reuse and recycling potential. This paper develops a greedy search algorithm to identify a network for water-reusing and water-recycling streams with multiple contaminants. The proposed approach involves identifying a feasible solution and its perturbation and reconstruction to achieve an improved solution. A feasible solution is determined using the concept of concentration potentials, and then every water allocation is reduced by a factor. Using this perturbed solution as an initial point, an improved solution is reconstructed through a greedy search method. The effectiveness of this technique is shown through six examples. This algorithm obtains significant water savings in a few iterations. In the examples, the approximate solutions are quickly achieved through the proposed algorithm.

关键词： greedy search algorithm Optimization Meta-heuristics Water allocation networks Process integration

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Developing two heuristic algorithms with metaheuristic algorithms to improve solutions of optimization problems with soft and hard constraints: An application to nurse rostering problems

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APPLIED SOFT COMPUTING 2020年 93卷 106336-106336页

作者： Chen, Ping-Shun Zeng, Zhi-Yang Chung Yuan Christian Univ Dept Ind & Syst Engn Taoyuan 320 Taiwan

Many researchers have studied optimization problems with soft and hard constraints, such as school timetabling, nurse rostering, vehicle routing with soft time window, and job/machine scheduling. Nurse rostering problem (NRP) is the research problem in this paper. This study proposes two heuristic algorithms, which are the decision tree method and the greedy search algorithm, to integrate with metaheuristic algorithms in order to generate better initial solutions in less time and to improve solutions' quality. This research examines the algorithms' performance based on two scenarios and two metaheuristic algorithms: bat algorithm (BA) and particle swarm optimization (PSO). For the two scenarios, BA (or PSO) with the decision tree method outperforms BA (or PSO) without the decision tree method, and BA (or PSO) with the greedy search algorithm outperforms BA (or PSO) without the greedy search algorithm. Furthermore, the results show that BA (or PSO) with the decision tree method and the greedy search algorithm can generate better initial solutions in less time and improve solutions' quality. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Nurse rostering problem Decision tree greedy search algorithm Bat algorithm Particle swarm optimization

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Online selection of the best k-feature subset for object tracking

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2012年第2期23卷 254-263页

作者： Li, Guorong Huang, Qingming Pang, Junbiao Jiang, Shuqiang Qin, Lei Chinese Acad Sci Grad Univ Beijing 100190 Peoples R China Chinese Acad Sci Inst Comput Tech Key Lab Intell Info Proc Beijing 100080 Peoples R China

In this paper, we propose a new feature subset evaluation method for feature selection in object tracking. According to the fact that a feature which is useless by itself could become a good one when it is used together with some other features, we propose to evaluate feature subsets as a whole for object tracking instead of scoring each feature individually and find out the most distinguishable subset for tracking. In the paper, we use a special tree to formalize the feature subset space. Then conditional entropy is used to evaluating feature subset and a simple but efficient greedy search algorithm is developed to search this tree to obtain the optimal k-feature subset quickly. Furthermore, our online k-feature subset selection method is integrated into particle filter for robust tracking. Extensive experiments demonstrate that k-feature subset selected by our method is more discriminative and thus can improve tracking performance considerably. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Object tracking Feature subset selection Feature selection Feature subset tree Conditional entropy greedy search algorithm Particle filter Online selection

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Path planning method for on-machine inspection of aerospace structures based on adjacent feature graph

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ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING 2018年 54卷 17-34页

作者： Li, Bo Feng, Pingfa Zeng, Long Xu, Chao Zhang, Jianfu Tsinghua Univ Grad Sch Shenzhen Div Adv Mfg Shenzhen 518055 Peoples R China Tsinghua Univ Dept Mech Engn State Key Lab Tribol Beijing 10084 Peoples R China Tsinghua Univ Grad Sch Shenzhen OpenFiesta Shenzhen 518055 Peoples R China

On-Machine Inspection (OMI) system has been playing an important role in modern aeronautical manufacturing, owing to its high efficiency, great convenience, and low application cost. The inspection path planning of OMI, which aims to find an optimal path that traverses all inspection points without collisions and by costing as little time as possible, is a main bottleneck that limits the achievements of higher efficiency and less inspection time. Besides, the path planning for OMI in aerospace manufacturing faces new challenges, which are barely explored, because aerospace structures have unique characteristics of complex geometrical features, large-scale dimensions and high-precision processing requirements. Thus, this paper proposes a novel, easily-implemented and robust inspection path planning method to plan paths for OMI of aerospace structures based on the properties of aerospace structures. In order to lift the inspection efficiency, this method makes three improvements on the path planning. First, reorganize the inspection features based on the cluster technology. Second, construct the adjacent feature graph based on Voronoi Diagram to plan the path. Third, a search algorithm is designed to search the adjacent feature graph to decide the sequence of inspection features and a convex hull based algorithm is used to avoid collisions. The proposed method has been tested for several cases and solid experimental results have shown that these improvements take effects in path planning for OMI of aerospace structures and suited paths can be provided for the inspection.

关键词： On-machine inspection Path planning Voronoi diagram greedy search algorithm Aerospace structures

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Network sensor health problem

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TRANSPORTATION REsearch PART C-EMERGING TECHNOLOGIES 2016年 68卷 300-310页

作者： Sun, Zhe Jin, Wen-Long Ng, ManWo Univ Calif Irvine Dept Civil & Environm Engn Calif Inst Telecommun & Informat Technol Inst Transportat Studies 4038 Anteater Instruct & Res Bldg Irvine CA 92697 USA Univ Calif Irvine Dept Civil & Environm Engn Inst Transportat Studies 4191 Anteater Instruct & Res Bldg Irvine CA 92697 USA Old Dominion Univ Dept Informat Technol & Decis Sci Strome Coll Business 2165 Constant Hall Norfolk VA 23529 USA

Many existing studies on the sensor health problem determine an individual sensor's health status based on the statistical characteristics of collected data by the sensor. In this research, we study the sensor health problem at the network level, which is referred to as the network sensor health problem. First, based on the conservation principle of daily flows in a network, we separate all links into base links and non-base links, such that the flows on the latter can be calculated from those on the former. In reality, the network flow conservation principle can be violated due to the existence of unhealthy sensors. Then we define the least inconsistent base set of links as those that minimize the sum of squares of the differences between observed and calculated flows on non-base links. But such least inconsistent base sets may not be unique in a general road network. Finally we define the health index of an individual sensor as the frequency that it appears in all of the least inconsistent base sets. Intuitively, a lower health index suggests that the corresponding sensor is more likely to be unhealthy. We present the brute force method to find all least inconsistent base sets and calculate the health indices. We also propose a greedy search algorithm to calculate the approximate health indices more efficiently. We solve the network sensor health problem for a real-world example with 16 nodes and 30 links, among which 18 links are monitored with loop detectors. Using daily traffic count data from the Caltrans Performance Measurement System (PeMS) database, we use both the brute-force and greedy search methods to calculate the health indices for all the sensors. We find that all the four sensors flagged as unhealthy (high value) by PeMS have the lowest health indices. This confirms that a sensor with a lower health index is more likely to be unhealthy. Therefore, we can use such health indices to determine the relative reliability of different sensors' data and pri

关键词： Network flow conservation Least inconsistent base set Health index greedy search algorithm Caltrans Performance Measurement System (PeMS) database

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DIV-TBAT algorithm for test suite reduction in software testing

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IET SOFTWARE 2018年第3期12卷 271-279页

作者： Sugave, Shounak Rushikesh Patil, Suhas Haribhau Reddy, B. Eswara JNTUA Ananthapuramu Andhra Pradesh India Bharati Vidyapeeth Univ Coll Engn Pune Maharashtra India JNTUA Coll Engn Kalikiri Andhra Pradesh India

Researchers have investigated different approaches to maintain the minimum cost and effort in regression testing. Here, test suite reduction is a common technique to decrease the cost of regression testing by removing the redundant test cases from the test suite and then, obtaining a representative set of test cases that still yield a high level of code coverage. Accordingly, here, the authors have developed two various techniques for test suite reduction. In the first technique, ATAP measure is newly developed to find the reduced test suite with the help of greedy search algorithm. In the second technique, DIV-TBAT (DIVersity-based BAT) algorithm is newly devised based on the mechanisms of Boolean logic within BAT algorithm which improve diversity during the search process. The proposed techniques are experimented using eight programs from SIR subject programs and the performance study is conducted using nine different evaluation metrics based on different research questions. The comparative analysis is performed with the existing algorithms like greedyRatio, greedyEIrreplaceability, diversity-based genetic algorithm, TBAT, and TAP, to prove the performance improvement over the eight software programs considered.

关键词： program testing genetic algorithms greedy algorithms search problems software maintenance Boolean functions mathematics computing regression testing redundant test cases test suite reduction reduced test suite DIV-TBAT algorithm software testing ATAP measure greedy search algorithm Boolean logic

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A Comparative Study of Meta-Heuristic Optimization algorithms for 0-1 Knapsack Problem: Some Initial Results

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IEEE ACCESS 2019年 7卷 43979-44001页

作者： Ezugwu, Absalom E. Pillay, Verosha Hirasen, Divyan Sivanarain, Kershen Govender, Melvin Univ KwaZulu Natal Sch Comp Sci ZA-3201 Pietermaritzburg South Africa Univ KwaZulu Natal Sch Math Stat & Comp Sci ZA-4000 Durban South Africa

In this paper, we present some initial results of several meta-heuristic optimization algorithms, namely, genetic algorithms, simulated annealing, branch and bound, dynamic programming, greedy search algorithm, and a hybrid genetic algorithm-simulated annealing for solving the 0-1 knapsack problems. Each algorithm is designed in such a way that it penalizes infeasible solutions and optimizes the feasible solution. The experiments are carried out using both low-dimensional and high-dimensional knapsack problems. The numerical results of the hybrid algorithm are compared with the results achieved by the individual algorithms. The results revealed the superior performances of the branch and bound dynamic programming, and hybrid genetic algorithm with simulated annealing methods over all the compared algorithms. This performance was established by taking into account both the algorithm computational time and the solution quality. In addition, the obtained results also indicated that the hybrid algorithm can be applied as an alternative to solve small- and large-sized 0-1 knapsack problems.

关键词： Knapsack problem genetic algorithms simulated annealing branch and bound dynamic programming greedy search algorithm hybrid IGA-SA

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A Fast Signal Timing algorithm for Individual Oversaturated Intersections

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2011年第1期12卷 280-283页

作者： Zhao, Lei Peng, Xiaoshan Li, Li Li, Zhenjiang Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China Tsinghua Univ Dept Automat TNList Beijing 100084 Peoples R China Chinese Acad Sci Inst Automat Key Lab Complex Syst & Intelligence Sci Beijing 100080 Peoples R China

In this paper, we propose a fast greedy search algorithm for optimal single-cycle signal timing at individual oversaturated intersections. We illustrate the efficiency of the algorithm with a numerical example in the ... 详细信息

关键词： greedy search algorithm oversaturated intersection signal timing

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Cluster-based resource allocation scheme with QoS guarantee in ultra-dense networks

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IET COMMUNICATIONS 2018年第7期12卷 861-867页

作者： Li, Wenchao Zhang, Jing Nanjing Univ Posts & Telecommun Coll Telecommun & Informat Engn Nanjing 210003 Jiangsu Peoples R China Key Lab Wireless Commun Jiangsu Prov Nanjing 210003 Jiangsu Peoples R China

Ultra-dense networks (UDN) can provide extremely high throughput and data rate. However, there are severe interference due to dense and random deployment of femto base stations (FBSs). To mitigate interference and allocate network resource efficiently while ensuring quality of service (QoS) of user equipments (UEs), a cluster-based resource allocation scheme for UDN is proposed in this paper. Two stages, clustering and resource allocation, are involved in the scheme. In clustering stage, a modified K-means clustering algorithm is advanced to divide FBSs into different disjoint clusters dynamically according to the density of FBSs. Thus the number of clusters can be adjusted flexibly to fit for the dynamic network topology. In resource allocation stage, a greedy-based compensatory resource allocation algorithm (GCRAA) is further proposed to maximize the throughput of UDN. Herein the orthogonal resource blocks (RBs) are initially assigned among the UEs with a greedy algorithm. In order to ensure the fairness and QoS of UEs, a compensatory resource allocation algorithm is further proposed to allocate the remaining RBs. The simulated results show that the proposed resource allocation scheme can mitigate the interference in UDN effectively, and improve the system throughput while ensuring the QoS for UEs.

关键词： resource allocation quality of service pattern clustering interference suppression greedy algorithms search problems femtocellular radio cluster-based resource allocation scheme QoS ultradense network UDN interference mitigation femtobase station FBS quality of service user equipment UE modified k-means clustering algorithm dynamic network topology greedy-based compensatory resource allocation algorithm orthogonal resource block RB greedy search algorithm

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Joint Bandwidth and Power Allocation With Admission Control in Wireless Multi-User Networks With and Without Relaying

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2011年第4期59卷 1801-1813页

作者： Gong, Xiaowen Vorobyov, Sergiy A. Tellambura, Chintha Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2V4 Canada

Equal allocation of bandwidth and/or power may not be efficient for wireless multi-user networks with limited bandwidth and power resources. Optimal joint bandwidth and power allocation strategies for wireless multi-user networks with and without relaying are proposed in this paper for 1) the maximization of the sum capacity of all users;2) the maximization of the worst user capacity;and 3) the minimization of the total power consumption of all users. It is shown that the proposed allocation problems are convex and, therefore, can be solved efficiently. Moreover, joint bandwidth and power allocation for admission control is considered. A suboptimal greedy search algorithm is developed to solve the admission control problem efficiently. Instructive analysis of the greedy search shows that it can achieve good performance, and the condition under which the greedy search is optimal is derived. The formal and in-depth analysis of the greedy search algorithm presented in this paper can serve as a benchmark for analyzing similar algorithms in other applications. The performance improvements offered by the proposed optimal joint bandwidth and power allocation are demonstrated by simulations. The advantages of the suboptimal greedy search algorithm for admission control are also shown in numerical results.

关键词： Admission control greedy search algorithm joint bandwidth and power allocation wireless multi-user networks

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