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Common Object Discovery as local search for Maximum Weight Cliques in a Global Object Similarity Graph 1

21st IAPR International Conference on Discrete Geometry for Computer Imagery (DGCI)

作者： Rao, Cong Fan, Yi Su, Kaile Latecki, Longin Jan Temple Univ Philadelphia PA 19122 USA Guilin Univ Elect Technol Guangxi Key Lab Trusted Software Guilin 541004 Peoples R China Griffith Univ Brisbane Qld Australia

ISBN: (数字)9783030140854

ISBN: (纸本)9783030140854;9783030140847

In this paper, we consider the task of discovering the common objects in images. Initially, object candidates are generated in each image and an undirected weighted graph is constructed over all the candidates. Each candidate serves as a node in the graph while the weight of the edge describes the similarity between the corresponding pair of candidates. The problem is then expressed as a search for the Maximum Weight Clique (MWC) in this graph. The MWC corresponds to a set of object candidates sharing maximal mutual similarity, and each node in the MWC represents a discovered common object across the images. Since the problem of finding the MWC is NP-hard, most research of the MWC problem focuses on developing various heuristics for finding good cliques within a reasonable time limit. We utilize a recently very popular class of heuristics called local search methods. They search for the MWC directly in the discrete domain of the solution space. The proposed approach is evaluated on the PASCAL VOC image dataset and the YouTube-Objects video dataset, and it demonstrates superior performance over recent state-of-the-art approaches.

关键词： Common Object Discovery Visual Similarity Maximum Weight Clique local search algorithm

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Indicator Weighted Based Multi-Objective Approach using Self-Adaptive Neighborhood Operator

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Procedia Computer Science 2021年 192卷 338-347页

作者： Imen BenMansour Ines Alaya Moncef Tagina Esprit School Of Engineering Tunis Tunisia ENSI-LARIA University of Manouba Tunisia

Balancing diversity and convergence seems to be a difficult task when solving multi-objective optimization problems (MOPs). For addressing this issue, researchers’ interest has been drawn to hybrid approaches since this cooperation allows benefiting from the advantages of both approaches and gains better trade-offs overall. Considering such fact, this paper aims at introducing a hybrid approach as a synergy of a multi-directional Ant Colony Optimization algorithm with a local search method based on a weighted version of epsilon Indicator using a self-adaptive neighborhood operator coined as Indicator Weighted Based local search with Ant Colony Optimization (IWBLS/ACO) to handle the knapsack problem within the multi-objective framework. In IWBLS/ACO, initial solutions are created by the ant colony. Then, the enhancement phase is ensured by the local search procedure. The algorithm is evolving based on different configurations of the epsilon quality indicator through different weight vectors. Moreover, we propose in this work, a novel self-adaptive neighborhood operator which changes automatically and dynamically as the IWBLS algorithm runs. The proposed IWBLS/ACO was tested on widely used Multi-objective Multidimensional Knapsack Problem (MOMKP) instances and compared with powerful state-of-the-art algorithms. Experimental results highlight that the proposed approach can lead to finding a good compromise between exploration and exploitation.

关键词： Multi-objective optimization Ant Colony Optimization local search algorithm Weighted epsilon quality indicator Self-adaptive neighborhood operator

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Evolutionary deployment and local search-based movements of 0th responders in disaster scenarios

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2018年 88卷 61-78页

作者： Reina, D. G. Camp, T. Munjal, A. Toral, S. L. Engn Dept Campus Palmas AltasEdificio E Seville 41014 Spain Colorado Sch Mines Div Comp Sci 1500 Illinois St Golden CO 80401 USA CableLabs Louisville CO USA Univ Seville Elect Engn Dept Camino Descubrimientos S-N Seville 41092 Spain

establishment of communications in disaster scenarios is of paramount importance, especially because preexisting communication infrastructure is likely to be destroyed or malfunctioning. Consequently, there is a need for an alternative and self-organizing communication infrastructure that can be rapidly deployed in disaster situations. In this paper, we propose to use drones or unmanned aerial vehicles as 0th responders to form a network that provides communication services to victims. Finding the best positions of the 0th responders is a non-trivial problem and is, therefore, divided into two phases. The first phase is the initial deployment, where the 0th responders are placed using partial information on the disaster scenario. In the second phase, which we call the adaptation to real conditions, the drones move according to a local search algorithm to find positions that provide better coverage to the victims. We conduct extensive simulations to validate our proposed approach for rural disaster scenarios under different conditions. We show that our proposed initial deployment based on genetic algorithm provides coverage for up to 94% (maximum) and 86% (mean) of victims if complete knowledge of the disaster scenario is known and 10 drones are used. When the adaptation to the real condition phase is used, this percentage is increased to 95% (maximum). If no knowledge of the scenario and 10 UAVs are used 80% (maximum) and 59% (mean) of victims are found and successfully covered. The proposed approach outperforms in 6.4% the random deployment method, and in 2.4% the best grid deployment approach. Finally, we show that by using different numbers of drones for the two phases of the proposed approach, the percentage of victims is increased up to 51% for low values of knowledge of the scenario. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Disaster scenarios Communications Evolutionary algorithm local search algorithm 0th responders

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A novel accelerated degradation test design considering stress optimization

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第4-Sup期22卷 S8793-S8802页

作者： Shen, Dayong Liu, Tianyu Zhang, Longfei Wang, Jie Liu, Yumeng Natl Univ Def Technol Coll Syst Engn Changsha Hunan Peoples R China Qingdao Acad Intelligent Ind Qingdao Shandong Peoples R China Chinese Acad Sci Inst Software Software Engn Beijing Peoples R China

Constant stress accelerated degradation tests (CSADT) are widely used in life perdition for highly reliable products to infer the lifetime distribution under operating conditions. Optimal design of an CSADT can improve life prediction accuracy and reduce test costs significantly. In the literature of CSADT design, most approaches focus on how to determine the sample allocation scheme, inspection frequency and test duration, but the issue of how to optimize the stress levels is seldom considered. In this work, we propose a novel method to optimize the CSADT considering both stress levels selection and samples allocation. First, an accelerated degradation model based on the Wiener process is used to model the degradation data. Next, under the constraint of sample size, a local-search based iterative algorithm is proposed to optimize parameters including stress levels and sample number under each level so as to obtain an accurate estimate of the distribution statistics. Finally, a case study of lithium-ion batteries is presented to validate the proposed method.

关键词： Degradation test design Stress optimization Wiener process local search algorithm

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algorithm and peer-to-peer negotiation strategies for train dispatching problems in railway bottleneck sections

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IET INTELLIGENT TRANSPORT SYSTEMS 2019年第11期13卷 1717-1725页

作者： Liu, Jin Chen, Lei Roberts, Clive Nicholson, Gemma Ai, Bo Univ Birmingham Sch Engn Birmingham Ctr Railway Res & Educ Birmingham B15 2TT W Midlands England Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China

Train delays occur often in daily railway operations due to a variety of initiating incidents. On a heavily loaded mainline railway, a single train delay may lead to a series of secondary delays across the network. In this study, the authors describe a peer-to-peer system to solve train rescheduling problems in railway network bottlenecks. A designed Genetic algorithm is chosen as the local search algorithm on each side. Based on the local search algorithm, different negotiation protocols are raised to find globally feasible solutions. The proposed approach is tested in a railway bottleneck section in the UK, and the computational result is compared with a centralised method to show its performance in terms of computation time and optimality.

关键词： railways genetic algorithms rail traffic delays integer programming scheduling train rescheduling problems railway network bottlenecks designed Genetic algorithm local search algorithm different negotiation protocols railway bottleneck section peer-to-peer negotiation strategies train dispatching problems train delays daily railway operations initiating incidents heavily loaded mainline railway single train delay secondary delays peer-to-peer system

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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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A Partial Decision Scheme for local search algorithms for Distributed Constraint Optimization Problems 16

A Partial Decision Scheme for Local Search Algorithms for Di...

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16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS)

作者： Yu, Zhepeng Chen, Ziyu He, Jingyuan Deng, Yancheng Chongqing Univ Coll Comp Sci Chongqing Peoples R China

ISBN: (纸本)9781510855076

local search algorithms are widely adopted in solving large-scale Distributed Constraint Optimization Problems (DCOPs). However, since each agent always makes its value decision based on the values of its neighbors in local search, those algorithms usually suffer from local premature convergence. More concretely, an agent cannot make a wise decision with poor values of its neighbors since its decision space is bound up with those poor values. In this paper, we propose a Partial Decision Scheme (PDS) to relax the decision space of an agent by ignoring the value of its neighbor which has the bad impact on its local benefits. The PDS comprises two partial decision processes: trigger partial decision and recursive partial decision. The former is iteratively performed by agents who cannot enhance their local benefits unilaterally to break out of potential local optima. The latter is recursively performed by neglected agents to improve global benefits. Besides, the trigger conditions along with a self-adaptive probability are introduced to control the use of PDS. The PDS can be easily applied to any local search algorithm to overcome its local premature convergence with a small additional overhead. In our theoretical analysis, we prove the feasibility and convergence of PDS. Moreover, the experimental results also demonstrate the superiority of the use of PDS in the typical local search algorithms over state-of-the-art local search algorithms.

关键词： Multi-agent System Distributed Constraint Optimization Problem Incomplete algorithm local search algorithm Partial Decision

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FPGA Based Satisfiability Checking

FPGA Based Satisfiability Checking

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作者： Subramanian, Rishi Bharadwaj University of Cincinnati

学位级别：M.S.

The Boolean satisfiability problem, abbreviated as SAT, is the backbone of many applications in VLSI design automation and verification. Over the years, many SAT solvers, both complete and incomplete, have been developed. Complete solvers are usually based on the DPLL (Davis–Putnam–Logemann–Loveland) algorithm, which is a backtracking algorithm. Industrial strength problems are very large and make DPLL based solvers impractical for some applications. In such cases, local search algorithms that try to find a solution within a stipulated time can be used. These algorithms look at SAT problem as an optimization problem. They start with an initial random solution and explore a certain search space by iteratively making local changes to the solution using a greedy, heuristic algorithm to find a global optimum. Over the past few years, heterogeneous devices such as Graphics Processing Units (GPU) and Field Programmable Gate Arrays (FPGA) have been used to accelerate the SAT problem and handle large SAT instances. There has been a growing interest in exploiting the parallel and pipeline processing power of FPGAs for various applications. New process technologies have allowed for more logic blocks, memory elements, and faster FPGAs, making it a perfect candidate for parallel computing. This thesis presents a local search algorithm Walksat, implemented on the Xilinx Alveo U250 Accelerator card. The entire solver has been developed using the OpenCL framework. On-chip memory available on the FPGA has been exploited to a great extent and the solver can handle SAT problems of up to 98,000 variables and 401,800 clauses. We have also analyzed the performance of our solver against the state of the art complete and incomplete solvers.

关键词： Electrical Engineering FPGA Based SAT Solver Boolean Satisfiability local search algorithm SAT Solver

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Solving Travelling Salesman Problem (TSP) by Hybrid Genetic algorithm (HGA)

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2020年第6期11卷 376-384页

作者： Al-Ibrahim, Ali Mohammad Hussein World Islamic Sci & Educ Univ WISE Fac Informat Technol Dept Comp Sci POB 1101 Amman 11947 Jordan

The Traveling Salesman Problem (TSP) is easy to qualify and describe but difficult and very hard to be solved. There is known algorithm that can solve it and find the ideal outcome in polynomial time, so it is NP-Complete problem. The Traveling Salesman Problem (TSP) is related to many others problems because the techniques used to solve it can be easily used to solve other hard Optimization problems, which allows of circulating it results on many other optimization problems. Many techniques were proposed and developed to solve such problems, including Genetic algorithms. The aim of the paper is to improve and enhance the performance of genetic algorithms to solve the Traveling Salesman Problem (TSP) by proposing and developing a new Crossover mechanism and a local search algorithm called the search for Neighboring Solution algorithm, with the goal of producing a better solution in a shorter period of time and fewer generations. The results of this study for a number of different size standard benchmarks of TSP show that the proposed algorithms that use Crossover proposed mechanism can find the optimum solution for many of these TSP benchmarks by (100%), and within the rate (96%-99%) of the optimal solution to some for others. The comparison between the proposed Crossover mechanism and other known Crossover mechanisms show that it improves the quality of the solutions. The proposed local search algorithm and Crossover mechanism produce superior results compared to previously propose local search algorithms and Crossover mechanisms. They produce near optimum solutions in less time and fewer generations.

关键词： Traveling Salesman Problem (TSP) NP-Complete problem genetic algorithms local search algorithm crossover mechanism neighboring solution algorithm

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Planning Method of Near-Minimum-Time Task Tour for Industrial Point-to-Point Robot 9

Planning Method of Near-Minimum-Time Task Tour for Industria...

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9th IEEE International Conference on Cybernetics and Intelligent Systems (CIS) / IEEE Conference on Robotics, Automation and Mechatronics (RAM)

作者： Nemoto, Kosuke Aiyama, Yasumichi Univ Tsukuba Dept Intelligent Interact Technol Tsukuba Ibaraki Japan Univ Tsukuba Fac Engn Informat & Syst Tsukuba Ibaraki Japan

ISBN: (数字)9781728134581

ISBN: (纸本)9781728134581

This paper introduces a near-optimal path planning for industrial robot for multiple point tasks. For instance, spot welding, drilling, screwing and inspection with camera are popular multiple point work for industrial robots. How to decide the sequence of robot motions is important issue for factory automation because it directory influences to system productivity. Though getting optimal tour is not easy because this is kind of Traveling Salesman Problem (TSP) that is almost impossible to solve in polynomial time. In TSP the cost between two cities is Oven by geometrical distance though in robotics time for moving between two task points is gets cost. This article adopts heuristic algorithm that is often used to solve TSP to solve about 50 task points path planning for industrial robot. As a result, the algorithm gives under PA gap solution against best known solutions. For actual robot tasks, it generates about 6 to 10% efficient results compared with human task planner's path.

关键词： traveling salesman problem industrial robot time optimization local search algorithm

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