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Efficient Local Search for Pseudo Boolean optimization 24th

Efficient Local Search for Pseudo Boolean Optimization

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24th international conference on theory and Applications of Satisfiability Testing (SAT)

作者： Lei, Zhendong Cai, Shaowei Luo, Chuan Hoos, Holger Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing Peoples R China Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing Peoples R China Microsoft Res Beijing Peoples R China Leiden Univ Leiden Netherlands

ISBN: (纸本)9783030802233;9783030802226

Pseudo-Boolean optimization (PBO) can be used to model many combinatorial optimization problems. PBO instances encoded from real-world applications are often large and difficult to solve;in many cases, close-to-optimal solutions are useful and can be found reasonably efficiently, using incomplete algorithms. Interestingly, local search algorithms, which are known to be effective for solving many other combinatorial optimization problems, have been rarely considered in the context of PBO. In this paper, we are introducing a new and surprisingly effective local search algorithm, LS-PBO, for PBO. LS-PBO adopts a well designed weighting scheme and a new scoring function. We compare LS-PBO with previous PBO solvers and with solvers for related problems, including MaxSAT, Extended CNF and integer Linear programming (ILP). We report results on three real-world application benchmarks, from the Minimum-Width Confidence Band, Wireless Sensor Network optimization and Seating Arrangement Problems, as well as on benchmarks from the most recent PB Competition. these results demonstrate that our LS-PBO algorithm achieves much better performance than previous state-of-the-art solvers on real-world benchmarks.

关键词： Local search Pseudo boolean optimization

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Surrogate-Assisted Hybrid Metaheuristic for Mixed-Variable 3-D Deployment optimization of Directional Sensor Networks

Surrogate-Assisted Hybrid Metaheuristic for Mixed-Variable 3...

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Data-driven optimization of Complex Systems (DOCS), international conference on

作者： Yuntian Zhang Chen Chen Tongyu Wu Changhao Miao Shuxin Ding School of Automation Beijing Institute of Technology Beijing China National Key Lab of Autonomous Intelligent Unmanned Systems Beijing China Signal and Communication Research Institute China Academy of Railway Sciences Corporation Limited Beijing China Traffic Management Laboratory for High-Speed Railway National Engineering Research Center of System Technology for High-Speed Railway and Urban Rail Transit Beijing China

A major concern in designing sensor networks is the deployment problem. However, establishing an efficient algorithm for the real-world deployment problem is challenging due to three issues, which are 1) the realistic mixed-integer nonlinear programming problem (MINLP) with mixed-variable; 2) the combinatorial subset selection problem; and 3) the expensive computational cost for fitness evaluation in the 3-D coverage problem. therefore, this paper addresses these challenges and proposes a surrogate-assisted hybrid metaheuristic for mixed-variable 3-D deployment optimization of directional sensor networks (DSNs). First, an MINLP with flexible coordinate transformation technique and an efficient mixed-variable encoding scheme are introduced to model and represent the problem. We propose hybrid metaheuristic which applies two reproduction methods respectively for discrete and continuous variables. Second, we design sparse population-based incremental learning (s-PBIL) to handle inherent subset selection problem. s-PBIL could accurately learn the required information, and automatically learn a sparse distribution. third, a mixed-variable surrogate with unifying space under Bayesian model management is incorporated to reduce the expensive computational cost. Experiment results on real-world deployment scenarios scaling from small-size to large-size show the effectiveness of the proposed algorithm.

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Constrained Clustering Problems: New optimization Algorithms 20th

Constrained Clustering Problems: New Optimization Algorithms

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20th international conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Ibn-Khedher, Hatem Hadji, Makhlouf Ibn Khedher, Mohamed Khebbache, Selma ALTRAN Labs F-78140 Velizy Villacoublay France Inst Rech Technol SystemX 8 Ave Vauve F-91120 Palaiseau France

ISBN: (纸本)9783030878979;9783030878962

Constrained clustering problems are often considered in massive data clustering and analysis. they are used in modeling various issues in anomaly detection, classification, systems' misbehaviour, etc. In this paper, we focus on generalizing the K-Means clustering approach when involving linear constraints on the clusters' size. Indeed, to avoid local optimum clustering solutions which consists in empty clusters or clusters with few points, we propose linear integer programming approaches based on relaxation and rounding techniques to cope with scalability issues. We show the efficiency of the new proposed approach, and assess its performance using five data-sets from different domains.

关键词： Constrained-clustering K-Means combinatorial optimization

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Critical5G-Net: Addressing Critical Node Impact on Lifetime and Latency in IoT Networks

Critical5G-Net: Addressing Critical Node Impact on Lifetime ...

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international conference on the Network of the Future (NOF)

作者： Behnam Ojaghi Ricard Vilalta Raül Muñoz CTTC Barcelona Spain

ISBN: (数字)9798350377767

ISBN: (纸本)9798350377774

In 5G networks, massive Internet of things (IoT) devices pose challenges like network management and security. Such networks are vulnerable to natural phenomena and attacks due to the lack of physical protection and insufficient security mechanisms. this paper examines that not all nodes are equally affected by their incapacitation; some critical nodes deteriorate the most in the network performance when incapacitated. Our study investigates the effects of eliminating critical nodes on two critical network performance metrics, namely lifetime and latency, using the Mixed integer programming (MIP) framework. Numerical evaluations prove that these critical nodes significantly impact lifetime and latency, and their identification is essential for adjusting the service tolerance and improving network maintenance. For example, in a disk-shaped network topology scenario with 100 nodes, if two critical nodes are removed, the lifetime can decrease by 56% when minimum latency is enforced by allowing a 24% latency increase; such lifetime deterioration can be reduced to 13%.

关键词： Performance evaluation Wireless sensor networks Pedestrians Network topology optimization models Minimization Real-time systems Internet of things Security Protection

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Multi-objective Optimisation of Wireless Sensor Networks Deployment: Application to fire surveillance in smart car parks 17

Multi-objective Optimisation of Wireless Sensor Networks Dep...

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17th IEEE international Wireless Communications and Mobile Computing conference (IEEE IWCMC)

作者： Benghelima, Slimane Charafeddine Ould-Khaoua, Mohamed Benzerbadj, Ali Baala, Oumaya Univ Blida 1 Dept Comp Sci LRDSI Blida Algeria Ain Temouchent Univ Belhadj Bouchaib Dept Math & Comp Sci Ain Temouchent Algeria Res Lab Ind Comp & Networks RIIR Oran Algeria Univ Bourgogne Franche Comte FEMTO ST Inst CNRS UTBM Belfort France

ISBN: (纸本)9781728186160

the exponential growth of the Internet-of-things (IoT) technology paradigm has resulted in new applications and on-line services. Smart car park is one interesting example among others that can take advantage of applications based on wireless sensor networks (WSNs) Which constitute the core of IoT. this paper focuses on the deployment optimization problem of WSNs dedicated to the fire detection in a smart car park. In such networks, the nodes are classified into two categories: Sensor Nodes (SNs) deployed within the smart car park for targets coverage and Relay Nodes (RNs) whose task is to relay alert messages generated by the sensor nodes up to the sink node. In this study, we propose a Multi-Objective Binary integer Linear programming (MOBILP) which minimizes simultaneously the number of sensor nodes, relay nodes and the maximum distance from sensor nodes to the sink node, while ensuring coverage and connectivity. We have conducted extensive tests in order to evaluate the performance of our proposal. the results demonstrate that the MOBILP outperforms the existing approaches in terms of quality of solutions compared to a sequential deployment method, which consists to deploy SNs then RNs, and in terms of the ability to find other efficient solutions compared to a simultaneous deployment method using a mono-objective function, which consists to deploy SNs and RNs simultaneously.

关键词： Internet of things Wireless Sensor Networks Deterministic Deployment Multi-objective combinatorial optimization Smart Car Parks Fire Surveillance

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An Incremental Planning Strategy-Based Genetic Algorithm for Integrated Scheduling and Routing optimization in Smart Manufacturing

An Incremental Planning Strategy-Based Genetic Algorithm for...

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international conference on Control and Automation (ICCA)

作者： Jiarong Yao Yao Guo Jiangpeng Li Rong Su Keck Voon Ling Boon Siew Han Hong Yee Alvin Wong School of Electrical and Electronic Engineering Nanyang Technological University Singapore Centre for Advanced Robotics Technology Innovation (CARTIN) School of Electrical and Electronic Engineering Nanyang Technological University Singapore Schaeffler Hub for Advanced Research (SHARE@NTU) Nanyang Technological University Singapore

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

Production scheduling has always been an important topic in smart manufacturing especially integrated with dispatching and routing of automatic guided vehicle or autonomous mobile robots, which is generally modeled as a nondeterministic polynomial-hard integer programming problem. However, most existing studies solve this combinatorial optimization problem through decomposition, namely dealing with scheduling and routing separately, or just assuming a fixed predefined distance matrix for routing, which sacrifices the solution quality for shorter time of decision making. As for metaheuristic solution for such integrated models, the need for effective and efficient searching rules is urgent, so that the heuristics can be more fit for specific purpose and scenarios. thus, in this paper, an integrated scheduling and routing model is proposed oriented towards the optimization of production scheduling and fleet management in smart manufacturing environment. A genetic algorithm (GA) is used to solve this problem based on incremental planning (IP) strategy which makes effect through ensemble of two local search operators considering the time windows and alternative paths for every transportation task of AGVs. the effectiveness of the proposed method is validated through a simulation case study with horizontal comparison. Results showed that the IP strategy can improve the solution quality and traffic load imbalance of the factory layout, by reducing the makespan and working time of AGV by about 11.5% and 9% respectively. About 80% of the tasks obtained a shorter makespan by adopting an alternative path, showing the ability of traffic load balancing among layout elements when the problem scale increases.

关键词： Job shop scheduling Layout Telecommunication traffic Routing Dispatching Planning IP networks

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Optimal Application Deployment in Mobile Edge Computing Environment 13

Optimal Application Deployment in Mobile Edge Computing Envi...

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IEEE 13th international conference on Cloud Computing (CLOUD)

作者： Chen, Feifei Zhou, Jingwen Xia, Xiaoyu Jin, Hai He, Qiang Deakin Univ Sch Informat Technol Geelong Vic Australia Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan Peoples R China Swinburne Univ Technol Sch Software & Elect Engn Hawthorn Vic Australia Chengdu Univ Informat Technol Sch Comp Sci Chengdu Peoples R China

ISBN: (纸本)9781728187808

Mobile edge computing has emerged as a new distributed computing paradigm that overcomes the limitations of traditional cloud computing. In an edge computing environment, an app vendor can hire computing and storage resources on edge servers for deploying their applications to deliver lower-latency services to their app users. Under a budget constraint, an optimal edge application deployment strategy allows an app vendor to deploy application instances on edge servers in a specific area and provide services to the most app users in the area. In this paper, we make the first attempt to tackle this edge application deployment (EAD) problem. Specifically, we formulate the EAD problem as a constrained optimization problem and prove its NP-hardness. then, we propose an optimal approach named EAD-opt to find the optimal solution of EAD based on integer programming, and an approximation approach named EAD-apx to find approximate solutions in large-scale EAD scenarios efficiently. We evaluate our approaches by conducting experiments on a widely used real-world data set and a synthetic data set with comparison against two baseline approaches. the experimental results demonstrate that our approaches can solve the EAD problem effectively and efficiently.

关键词： Edge computing application deployment low-latency services optimization integer programming approximation algorithm

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Persistency of Linear programming Relaxations for the Stable Set Problem 21st

Persistency of Linear Programming Relaxations for the Stable...

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21st international conference on integer programming and combinatorial optimization (IPCO)

作者： Rodriguez-Heck, Elisabeth Stickler, Karl Walter, Matthias Weltge, Stefan Rhein Westfal TH Aachen Lehrstuhl Operat Res Aachen Germany Univ Twente Dept Appl Math Enschede Netherlands Tech Univ Munich Dept Math Munich Germany

ISBN: (纸本)9783030457716;9783030457709

the Nemhauser-Trotter theorem states that the standard linear programming (LP) formulation for the stable set problem has a remarkable property, also known as (weak) persistency: for every optimal LP solution that assigns integer values to some variables, there exists an optimal integer solution in which these variables retain the same values. While the standard LP is defined by only non-negativity and edge constraints, a variety of stronger LP formulations have been studied and one may wonder whether any of them has the this property as well. We show that any stronger LP formulation that satisfies mild conditions cannot have the persistency property on all graphs, unless it is always equal to the stable-set polytope.

关键词： Persistency integer linear programming Stable set

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Target Coverage with Minimum Number of Camera Sensors 1

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15th Annual international conference on combinatorial optimization and Applications (COCOA)

作者： Yao, Pei Guo, Longkun Li, Shuangjuan Peng, Huihong Fuzhou Univ Fuzhou Peoples R China South China Agr Univ Guangzhou Peoples R China

ISBN: (数字)9783030926816

ISBN: (纸本)9783030926816;9783030926809

With the development of the smart city, camera sensors have attracted more and more research interests from both academic researchers and industrial engineers. Given a set of points of interests (POI) and a set of cameras, practical applications require to deploy these cameras with the minimum cost so that these POIs can be fully covered by these cameras. In this paper, we study a problem called Min-Num LTC-CS, which is, given a set of POIs located on a line segment and a set of cameras distributed on the plane, to choose a minimum number of cameras so that these POIs can be fully covered by the sensing ranges of these cameras. We first propose a grouping algorithm by grouping the POIs according to whether they can be covered by the same camera with certain rotation angle and then construct a graph using these POI groups. We show that there exists a feasible constrained st-flow if and only if there exists a subset of cameras that can completely cover these POIs. then we propose an LP formulation for the constrained flow problem and prove that any basic solution of the LP formulation is integral, which consequently leads to an optimal solution to Min-Num LTC-CS by solving this LP formulation. Lastly, extensive numerical experiments are conducted to demonstrate the practical performance of our algorithms.

关键词： Camera sensor network Constrained flow Linear programming integer optimal solution

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Structural analysis of branch-and-cut and the learnability of gomory mixed integer cuts 22

Structural analysis of branch-and-cut and the learnability o...

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Proceedings of the 36th international conference on Neural Information Processing Systems

作者： Maria-Florina Balcan Siddharth Prasad Tuomas Sandholm Ellen Vitercik School of Computer Science Carnegie Mellon University Computer Science Department Carnegie Mellon University Computer Science Department Carnegie Mellon University Optimized Markets Inc. Strategic Machine Inc. Strategy Robot Inc. Management Science and Engineering Department Computer Science Department Stanford University

ISBN: (纸本)9781713871088

the incorporation of cutting planes within the branch-and-bound algorithm, known as branch-and-cut, forms the backbone of modern integer programming solvers. these solvers are the foremost method for solving discrete optimization problems and have a vast array of applications in machine learning, operations research, and many other fields. Choosing cutting planes effectively is a major research topic in the theory and practice of integer programming. We conduct a novel structural analysis of branch-and-cut that pins down how every step of the algorithm is affected by changes in the parameters defining the cutting planes added to an integer program. Our main application of this analysis is to derive sample complexity guarantees for using machine learning to determine which cutting planes to apply during branch-and-cut. these guarantees apply to infinite families of cutting planes, such as the family of Gomory mixed integer cuts, which are responsible for the main breakthrough speedups of integer programming solvers. We exploit geometric and combinatorial structure of branch-and-cut in our analysis, which provides a key missing piece for the recent generalization theory of branch-and-cut.

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