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Improving combinatorial optimization algorithms through Record Keeping in Constructive Multistart Search

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2014年第9期29卷 864-879页

作者： King, Charles R. Tamir, Dan E. McKenney, Mark Texas State Univ Dept Comp Sci San Marcos TX 78666 USA So Illinois Univ Dept Comp Sci Edwardsville IL 62026 USA

Constructive multistart search algorithms are commonly used to address combinatorial optimization problems;however, constructive multistart search algorithm performance is fundamentally affected by two factors: (i) The choice of construction algorithm utilized and (ii) the rate of state space search redundancy. Construction algorithms are typically specific to a particular combinatorial optimization problem;therefore, we first investigate construction algorithms for iterative hill climbing applied to the traveling salesman problem and experimentally determine the best performing algorithms. We then investigate the more general problem of utilizing record-keeping mechanisms to mitigate state space search redundancy. Our research shows that a good choice of construction algorithm paired with effective record keeping significantly improves the quality of traveling salesmen problem solutions in a constant number of state explorations. Particularly, we show that Bloom filters considerably improve time performance and solution quality for iterative hill climbing approaches to the traveling salesman problem. (C) 2014 Wiley Periodicals, Inc.

关键词： Improving combinatorial optimization algorithms Constructive Multistart Search optimization algorithms Climbing combinatorial optimization algorithms constructivity SEARCHING ALGORITHM Constructive Algorithm travelling salesman problems bloom state space

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RECOGNITION AND combinatorial optimization algorithms FOR BIPARTITE CHAIN GRAPHS

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COMPUTING AND INFORMATICS 2013年第2期32卷 313-329页

作者： Talmaciu, Mihai Nechita, Elena Iantovics, Barna Vasile Alecsandri Univ Bacau Dept Math & Informat Bacau Romania Petru Maior Univ Targu Mures Dept Informat Targu Mures Romania

In this paper we give a recognition algorithm in O(n(n + m)) time for bipartite chain graphs, and directly calculate the density of such graphs. For their stability number and domination number, we give algorithms comparable to the existing ones. We point out some applications of bipartite chain graphs in chemistry and approach the Minimum Chain Completion problem.

关键词： Bipartite chain graphs weakly decomposition recognition algorithms combinatorial optimization algorithms

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Using the Variational-Quantum-Eigensolver (VQE) to Create an Intelligent Social Workers Schedule Problem Solver 15th

Using the Variational-Quantum-Eigensolver (VQE) to Create an...

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15th International Conference on Hybrid Artificial Intelligence Systems (HAIS)

作者： Atchade Adelomou, Parfait Golobardes Ribe, Elisabet Vilasis Cardona, Xavier Univ Ramon Llull Res Grp Data Sci Digital Soc DS4DS La Salle St Joan Salle 42 Barcelona 08022 Spain

ISBN: (纸本)9783030617059;9783030617042

The scheduling problem of social workers is a class of combinatorial optimization problems that can be solved in exponential time at best. Because is belongs to class of problems known as NP-Hard, which have huge impact huge impact on our society. Nowadays, the focus on the quantum computer should no longer be just for its enormous computing capacity but also for the use of its imperfection, (Noisy Intermediate-Scale Quantum (NISQ) era) to create a powerful machine learning device that uses the variational principle to solve the optimization problem by reducing their complexity's class. We propose a formulation of the Vehicle Rooting Problem (VRP) with time windows to solve efficiently the social workers schedule problem using Variational Quantum Eigensolver (VQE). The quantum feasibility of the algorithm will be modelled with docplex and tested on IBMQ computers.

关键词： Quantum algorithms Variational Quantum Eigensolvers Docplex combinatorial optimization algorithms

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Solving 3D Bin Packing Problem via Multimodal Deep Reinforcement Learning 21

Solving 3D Bin Packing Problem via Multimodal Deep Reinforce...

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International Conference on Autonomous Agents and Multiagent Systems

作者： Yuan Jiang Zhiguang Cao Jie Zhang Nanyang Technological University National University of Singapore

ISBN: (纸本)9781713832621

Recently, there is growing attention on applying deep reinforcement learning (DRL) to solve the 3D bin packing problem (3D BPP), given its favorable generalization and independence of ground-truth label. However, due to the relatively less informative yet computationally heavy encoder, and considerably large action space inherent to the 3D BPP, existing methods are only able to handle up to 50 boxes. In this paper, we propose to alleviate this issue via an end-to-end multimodal DRL agent, which sequentially addresses three sub-tasks of sequence, orientation and position, respectively. The resulting architecture enables the agent to solve large-scale instances of 100 boxes or more. Experiments show that the agent could learn highly efficient policies that deliver superior performance against all the baselines on instances of various scales.

关键词： Bin packing problem combinatorial optimization problem Multimodal learning Deep reinforcement learning bin packing Agents Activity spaces Learning learning (artificial intelligence) combinatorial optimization algorithms BASELINE Perisylvian syndrome

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Formulation of the Social Workers’ Problem in Quadratic Unconstrained Binary optimization Form and Solve It on a Quantum Computer

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Journal of Computer and Communications 2020年第11期8卷 44-68页

作者： Atchade Parfait Adelomou Elisabet Golobardes Ribé Xavier Vilasis Cardona Research Group in Data Science for the Digital Society (DS4DS) Barcelona Spain

The problem of social workers visiting their patients at home is a class of combinatorial optimization problems and belongs to the class of problems known as NP-Hard. These problems require heuristic techniques to provide an efficient solution in the best of cases. In this article, in addition to providing a detailed resolution of the social workers’ problem using the Quadratic Unconstrained Binary optimization Problems (QUBO) formulation, an approach to mapping the inequality constraints in the QUBO form is given. Finally, we map it in the Hamiltonian of the Ising model to solve it with the Quantum Exact Solver and Variational Quantum Eigensolvers (VQE). The quantum feasibility of the algorithm will be tested on IBMQ computers.

关键词： QUBO Quantum algorithms Variational Quantum Eigensolvers combinatorial optimization algorithms

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Hardness of Online Sleeping combinatorial optimization Problems 16

Hardness of Online Sleeping Combinatorial Optimization Probl...

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Annual Conference on Neural Information Processing Systems

作者： Satyen Kale Chansoo Leet David Pal Yahoo Research Univ. of Michigan Ann Arbor

ISBN: (纸本)9781510838819

We show that several online combinatorial optimization problems that admit efficient no-regret algorithms become computationally hard in the sleeping setting where a subset of actions becomes unavailable in each round. Specifically, we show that the sleeping versions of these problems are at least as hard as PAC learning DNF expressions, a long standing open problem. We show hardness for the sleeping versions of Online Shortest Paths, Online Minimum Spanning Tree, Online k-Subsets, Online k-Truncated Permutations, Online Minimum Cut, and Online Bipartite Matching. The hardness result for the sleeping version of the Online Shortest Paths problem resolves an open problem presented at COLT 2015 [Koolen et al., 2015].

关键词： Soundness hardness Sleep Not Available combinatorial optimization algorithms

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A new web-based solver for combinatorial optimization Problems under Permutation Property

A new web-based solver for Combinatorial Optimization Proble...

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International Conference on Modeling, Simulation and Applied optimization

作者： Lamia Trabelsi Talel Ladhari Laboratoire ROI Ecole Polytechnique Laboratoire ROI Ecole Polytechnique BEM-Bordeaux Management School

ISBN: (纸本)9781467358125

combinatorial optimization problems under permutation property (COPP) are generally NP-hard. To address such problems, metaheuristic approaches have increasingly captured the research interest due to their abilities to provide acceptable solutions in a reasonable time. This paper deals with a web based-solver destined for young researchers to solve COPP by metaheuristic approaches. This new tool will provide the opportunities to browse related works proposed for the COPP and metaheuristics. It will evaluate several versions of metaheuristic, and then, to give a best approximated solution to a studied COPP thanks to the design of experiment (DOE) module. As a first application, we will present a genetic solver tool for permutation flow shop scheduling problem.

关键词： COPP protocol Permutation Solvers combinatorial optimization algorithms Web search engines

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Connection of Users with a Telecommunications Network: Multicriteria Assignment Problem

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JOURNAL OF COMMUNICATIONS TECHNOLOGY AND ELECTRONICS 2010年第12期55卷 1532-1541页

作者： Levin, M. Sh. Petukhov, M. V.

The problem of connection of users with access points of a wireless telecommunications network is analyzed. An approach is based on the multicriteria assignment problem. A set of criteria involves the maximum bandwidth, the number of simultaneously served users, service reliability requirements, etc. Restrictions on the number of users served by access points and the frequency spectrum width of an access point are discussed. The combinatorial optimization problem employed belongs to the NP - hard class, and its solution is sought via the proposed heuristic methods. The proposed approach is illustrated by a numerical example. DOI: 10.1134/S1064226910120272

关键词： Nondeterministic polynomial-time hard combinatorial optimization algorithms Consumers access points Connections Telecommunication Networks CARRIER SYSTEM numerical example

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A Meta-Heuristic Algorithm for Multi-Objective Optimal Design of Hybrid Laminate Composite Structures

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COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING 2010年第3期25卷 149-170页

作者： Rao, A. Rama Mohan Shyju, P. P. Struct Engn Res Ctr Madras 600113 Tamil Nadu India

In this article, we propose a meta-heuristic algorithm for solving multi-objective combinatorial optimization problems. The proposed multi-objective combinatorial optimization algorithm is developed by combining the good features of popular guided local search algorithms like simulated annealing (SA) and tabu search (TS). It has been organized as a multiple start algorithm to maintain a good balance between intensification and diversification. The proposed meta-heuristic algorithm is evaluated by solving the stacking sequence optimization of hybrid fiber-reinforced composite plate, cylindrical shell, and pressure vessel problems. The standard performance metrics for evaluating multi-objective optimization algorithms are used to demonstrate the effectiveness of the proposed algorithm over other popular evolutionary algorithms like Nondominated Sorting Genetic algorithms (NSGA-II), Pareto Archived Evolutionary Strategy (PAES), micro-GA, and Multi-Objective Particle Swarm optimization (MOPSO).

关键词： simulated annealing combinatorial optimization algorithms standard performance phenyl-2-aminoethyl sulfide evolution strategy algorithms Meta-heuristic algorithms Intensification sequence optimization SEARCHING ALGORITHM pressure vessels optimization algorithms FIBER REINFORCED COMPOSITES Hybrid fibers

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A combinatorial optimization problem for high order PODs with few sensors

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JOURNAL OF VIBRATION AND ACOUSTICS-TRANSACTIONS OF THE ASME 2007年第2期129卷 252-255页

作者： Mohanty, Atanu K. Chakraborty, Kanad Chatterjee, Anindya Indian Inst Sci Supercomp Educ & Res Ctr Bangalore 560012 Karnataka India Agere Syst Signal Integr & Interconnect Anal Grp Allentown PA 18109 USA Indian Inst Sci Dept Mech Engn Bangalore 560012 Karnataka India

Experimental characterization of high dimensional dynamic systems sometimes uses the proper orthogonal decomposition (POD). If there are many measurement locations and relatively fewer sensors, then steady-state behavior can still be studied by sequentially taking several sets of simultaneous measurements. The number required of such sets of measurements can be minimized if we solve a combinatorial optimization problem. We aim to bring this problem to the attention of engineering audiences, summarize some known mathematical results about this problem, and present a heuristic (suboptimal) calculation that gives reasonable, if not stellar results.

关键词： Pods higher-order combinatorial optimization algorithms Sensors SETTING

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