Michel Vasquez

Bio: Michel Vasquez is an academic researcher from Mines ParisTech. The author has contributed to research in topics: Knapsack problem & Tabu search. The author has an hindex of 15, co-authored 34 publications receiving 1042 citations.

A “Logic-Constrained” Knapsack Formulation and a Tabu Algorithm for the Daily Photograph Scheduling of an Earth Observation Satellite

Abstract: The daily photograph scheduling problem of earth observation satellites such as Spot 5 consists of scheduling a subset of mono or stereo photographs from a given set of candidates to different cameras. The scheduling must maximize a profit function while satisfying a large number of constraints. In this paper, we first present a formulation of the problem as a generalized version of the well-known knapsack model, which includes large numbers of binary and ternary “logical” constraints. We then develop a tabu search algorithm which integrates some important features including an efficient neighborhood, a dynamic tabu tenure mechanism, techniques for constraint handling, intensification and diversification. Extensive experiments on a set of large and realistic benchmark instances show the effectiveness of this approach.

A hybrid approach for the 0-1 multidimensional knapsack problem

Abstract: We present a hybrid approach for the 0-1 multidimensional knapsack problem The proposed approach combines linear programming and Tabu Search The resulting algorithm improves significantly on the best known results of a set of more than 150 benchmark instances

A Heuristic Approach for Antenna Positioning in Cellular Networks

Abstract: The antenna-positioning problem concerns finding a set of sites for antennas from a set of pre-defined candidate sites, and for each selected site, to determine the number and types of antennas, as well as the associated values for each of the antenna parameters. All these choices must satisfy a set of imperative constraints and optimize a set of objectives. This paper presents a heuristic approach for tackling this complex and highly combinatorial problem. The proposed approach is composed of three phases: a constraint-based pre-processing phase to filter out bad configurations, an optimization phase using tabu search, and a post-optimization phase to improve solutions given by tabu search. To validate the approach, computational results are presented using large and realistic data sets.

Bounding the optimum for the problem of scheduling the photographs of an Agile Earth Observing Satellite

Abstract: The problem of managing an Agile Earth Observing Satellite consists of selecting and scheduling a subset of photographs among a set of candidate ones that satisfy imperative constraints and maximize a gain function. We propose a tabu search algorithm to solve this NP-hard problem. This one is formulated as a constrained optimization problem and involves stereoscopic and time window visibility constraints; and a convex evaluation function that increases its hardness. To obtain a wide-ranging and an efficient exploration of the search space, we sample it by consistent and saturated configurations. Our algorithm is also hybridized with a systematic search that uses partial enumerations. To increase the solution quality, we introduce and solve a secondary problem; the minimization of the sum of the transition durations between the acquisitions. Upper bounds are also calculated by a dynamic programming algorithm on a relaxed problem. The obtained results show the efficiency of our approach.

Upper Bounds for the SPOT 5 Daily Photograph Scheduling Problem

Abstract: This paper introduces tight upper bounds for the daily photograph scheduling problem of earth observation satellites. These bounds, which were unavailable until now, allow us to assess the quality of the heuristic solutions obtained previously. These bounds are obtained with a partition-based approach following the "divide and pas conquer" principle. Dynamic programming and tabu search are conjointly used in this approach. We present also simplex-based linear programming relaxation and a relaxed knapsack approach for the problem.

Chaff: engineering an efficient SAT solver

Abstract: Boolean satisfiability is probably the most studied of the combinatorial optimization/search problems. Significant effort has been devoted to trying to provide practical solutions to this problem for problem instances encountered in a range of applications in electronic design automation (EDA), as well as in artificial intelligence (AI). This study has culminated in the development of several SAT packages, both proprietary and in the public domain (e.g. GRASP, SATO) which find significant use in both research and industry. Most existing complete solvers are variants of the Davis-Putnam (DP) search algorithm. In this paper we describe the development of a new complete solver, Chaff which achieves significant performance gains through careful engineering of all aspects of the search-especially a particularly efficient implementation of Boolean constraint propagation (BCP) and a novel low overhead decision strategy. Chaff has been able to obtain one to two orders of magnitude performance improvement on difficult SAT benchmarks in comparison with other solvers (DP or otherwise), including GRASP and SATO.

Metaheuristics: From Design to Implementation

Abstract: A unified view of metaheuristics This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling. It presents the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code. Throughout the book, the key search components of metaheuristics are considered as a toolbox for: Designing efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems Designing efficient metaheuristics for multi-objective optimization problems Designing hybrid, parallel, and distributed metaheuristics Implementing metaheuristics on sequential and parallel machines Using many case studies and treating design and implementation independently, this book gives readers the skills necessary to solve large-scale optimization problems quickly and efficiently. It is a valuable reference for practicing engineers and researchers from diverse areas dealing with optimization or machine learning; and graduate students in computer science, operations research, control, engineering, business and management, and applied mathematics.

Efficient conflict driven learning in a boolean satisfiability solver

Abstract: One of the most important features of current state-of-the-art SAT solvers is the use of conflict based backtracking and learning techniques. In this paper, we generalize various conflict driven learning strategies in terms of different partitioning schemes of the implication graph. We re-examine the learning techniques used in various SAT solvers and propose an array of new learning schemes. Extensive experiments with real world examples show that the best performing new learning scheme has at least a 2/spl times/ speedup compared with learning schemes employed in state-of-the-art SAT solvers.

Handbook of Knowledge Representation

Abstract: Knowledge Representation, which lies at the core of Artificial Intelligence, is concerned with encoding knowledge on computers to enable systems to reason automatically. The Handbook of Knowledge Representation is an up-to-date review of twenty-five key topics in knowledge representation, written by the leaders of each field.This book is an essential resource for students, researchers and practitioners in all areas of Artificial Intelligence. * Make your computer smarter* Handle qualitative and uncertain information* Improve computational tractability to solve your problems easily

Hybrid metaheuristics in combinatorial optimization: A survey

Abstract: Research in metaheuristics for combinatorial optimization problems has lately experienced a noteworthy shift towards the hybridization of metaheuristics with other techniques for optimization. At the same time, the focus of research has changed from being rather algorithm-oriented to being more problem-oriented. Nowadays the focus is on solving the problem at hand in the best way possible, rather than promoting a certain metaheuristic. This has led to an enormously fruitful cross-fertilization of different areas of optimization. This cross-fertilization is documented by a multitude of powerful hybrid algorithms that were obtained by combining components from several different optimization techniques. Hereby, hybridization is not restricted to the combination of different metaheuristics but includes, for example, the combination of exact algorithms and metaheuristics. In this work we provide a survey of some of the most important lines of hybridization. The literature review is accompanied by the presentation of illustrative examples.