#### Filter Results:

- Full text PDF available (125)

#### Publication Year

1976

2017

- This year (10)
- Last 5 years (32)
- Last 10 years (101)

#### Publication Type

#### Co-author

#### Journals and Conferences

Learn More

- Pierre Hansen, Nenad Mladenovic
- European Journal of Operational Research
- 2001

Systematic change of neighborhood within a possibly randomized local search algorithm yields a simple and eÂ€ective metaheuristic for combinatorial and global optimization, called variable neighborhood search (VNS). We present a basic scheme for this purpose, which can easily be implemented using any local search algorithm as a subroutine. Its eÂ€ectivenessâ€¦ (More)

- Daniel Aloise, Amit Deshpande, Pierre Hansen, Preyas Popat
- Machine Learning
- 2009

A recent proof of NP-hardness of Euclidean sum-of-squares clustering, due to Drineas et al. (Mach. Learn. 56:9â€“33, 2004), is not valid. An alternate short proof is provided.

- Pierre Hansen, Brigitte Jaumard
- Computing
- 1990

Old and new algorithms for the Maximum Satisfiability problem are studied. We first summarize the different heuristics previously proposed, i.e., the approximation algorithms of Johnson and of Lieberherr for the general Maximum Satisfiability problem, and the heuristics of Lieberherr and Specker, Poljak and Turzik for the Maximum 2-Satisfiability problem.â€¦ (More)

- Peter L. Hammer, Pierre Hansen, Bruno Simeone
- Math. Program.
- 1984

- Pierre Hansen, Nenad Mladenovic, Dionisio PÃ©rez-Brito
- J. Heuristics
- 2001

The recent Variable Neighborhood Search (VNS) metaheuristic combines local search with systematic changes of neighborhood in the descent and escape from local optimum phases. When solving large instances of various problems, its efficiency may be enhanced through decomposition. The resulting two level VNS, called Variable Neighborhood Decomposition Searchâ€¦ (More)

Variable neighbourhood search (VNS) is a metaheuristic, or a framework for building heuristics, based upon systematic changes of neighbourhoods both in descent phase, to find a local minimum, and in perturbation phase to emerge from the corresponding valley. It was first proposed in 1997 and has since then rapidly developed both in its methods and itsâ€¦ (More)

- Pierre Hansen, Brigitte Jaumard, Gilles Savard
- SIAM J. Scientific Computing
- 1992

- Martin Charles Golumbic, Peter L. Hammer, +18 authors Gerhard JÃ¤ger
- Annals of Mathematics and Artificial Intelligence
- 2004

Annals of Mathematics and Artificial Intelligence presents a range of topics of concern to scholars applying quantitative, combinatorial, logical, algebraic and algorithmic methods to diverse areas of Artificial Intelligence, from decision support, automated deduction, and reasoning, to knowledge-based systems, machine learning, computer vision, roboticsâ€¦ (More)

- Pierre Hansen, Nenad Mladenovic
- Pattern Recognition
- 2001

A new local search heuristic, called J-Means, is proposed for solving the minimum sum-of-squares clustering problem. The neighborhood of the current solution is deened by all possible centroid-to-entity relocations followed by corresponding changes of assignments. Moves are made in such neighborhoods until a local optimum is reached. The new heuristic isâ€¦ (More)

- Pierre Hansen, Nenad Mladenovic, JosÃ© A. Moreno-PÃ©rez
- European Journal of Operational Research
- 1997

Variable neighborhood search (VNS) is a recent metaheuristic for solving combinatorial and global optimization problems whose basic idea is systematic change of neighborhood within a local search. In this survey paper we present basic rules of VNS and some of its extensions. Moreover, applications are briefly summarized. They comprise heuristic solution ofâ€¦ (More)