Multi-Objective Large Neighborhood Search

Schaus, Pierre; Hartert, Renaud

doi:10.1007/978-3-642-40627-0_46

Cited by 18 publications

(16 citation statements)

References 18 publications

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“…This constraint reduces the domain of a variable Obj i if the domains of the other variables enter into the dominated zone of a solution in A. A detailed description of this constraint as well as an extension with Large Neighborhood Search is proposed in [52]. This constraint Pareto can be introduced in our model.…”

Section: Sincementioning

confidence: 95%

Constrained clustering by constraint programming

Dao

Duong

Vrain

2017

Artificial Intelligence

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Constrained Clustering allows to make the clustering task more accurate by integrating user constraints, which can be instance-level or cluster-level constraints. Few works consider the integration of different kinds of constraints, they are usually based on declarative frameworks and they are often exact methods, which either enumerate all the solutions satisfying the user constraints, or find a global optimum when an optimization criterion is specified. In a previous work, we have proposed a model for Constrained Clustering based on a Constraint Programming framework. It is declarative, allowing a user to integrate user constraints and to choose an optimization criterion among several ones. In this article we present a new and substantially improved model for Constrained Clustering, still based on a Constraint Programming framework. It differs from our earlier model in the way partitions are represented by means of variables and constraints. It is also more flexible since the number of clusters does not need to be set beforehand; only a lower and an upper bound on the number of clusters have to be provided. In order to make the model-based approach more efficient, we propose new global optimization constraints with dedicated filtering algorithms. We show that such a framework can easily be embedded in a more general process and we illustrate this on the problem of finding the optimal Pareto front of a bi-criterion constrained clustering task. We compare our approach with existing exact approaches, based either on a branch-and-bound approach or on graph coloring on twelve datasets. Experiments show that the model outperforms exact approaches in most cases.

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Section: Sincementioning

confidence: 95%

Constrained clustering by constraint programming

Dao

Duong

Vrain

2017

Artificial Intelligence

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“…Our DSL also permits to define other delay-respectful goals, e.g., with delay constraints expressed as a percentage of their current values or delays for given demands in the objective function. DEFO also supports multi-objective goals [17]. The next snippet shows a goal in which both the maximum link load and the average latency have to be optimized.…”

Section: Expressing Network Goalsmentioning

confidence: 99%

A Declarative and Expressive Approach to Control Forwarding Paths in Carrier-Grade Networks

Hartert

Vissicchio

Schaus

et al. 2015

Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication

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109

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SDN simplifies network management by relying on declarativity (high-level interface) and expressiveness (network flexibility). We propose a solution to support those features while preserving high robustness and scalability as needed in carrier-grade networks. Our solution is based on (i) a two-layer architecture separating connectivity and optimization tasks; and (ii) a centralized optimizer called DEFO, which translates high-level goals expressed almost in natural language into compliant network configurations. Our evaluation on real and synthetic topologies shows that DEFO improves the state of the art by (i) achieving better trade-offs for classic goals covered by previous works, (ii) supporting a larger set of goals (refined traffic engineering and service chaining), and (iii) optimizing large ISP networks in few seconds. We also quantify the gains of our implementation, running Segment Routing on top of IS-IS, over possible alternatives (RSVP-TE and OpenFlow). ABSTRACT SDN simplifies network management by relying on declarativity (high-level interface) and expressiveness (network flexibility). We propose a solution to support those features while preserving high robustness and scalability as needed in carrier-grade networks. Our solution is based on (i) a two-layer architecture separating connectivity and optimization tasks; and (ii) a centralized optimizer called DEFO, which translates high-level goals expressed almost in natural language into compliant network configurations. Our evaluation on real and synthetic topologies shows that DEFO improves the state of the art by (i) achieving better trade-offs for classic goals covered by previous works, (ii) supporting a larger set of goals (refined traffic engineering and service chaining), and (iii) optimizing large ISP networks in few seconds. We also quantify the gains of our implementation, running Segment Routing on top of IS-IS, over possible alternatives (RSVP-TE and OpenFlow).

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“…[6] describes a more dynamic CP approach to compute Pareto front: the idea is to search for all solutions, and dynamically add a constraint each time a new solution is found to prevent the search from computing solutions that are dominated by it. This idea has been improved in [22,10]. We have experimentally compared these two approaches, and found that the dynamic approach of [6] is more efficient than the static approach of [3] for our problem.…”

Section: Computation Of Extrema Solutionsmentioning

confidence: 99%

Constraint Programming for Multi-criteria Conceptual Clustering

Chabert

Solnon

2017

Lecture Notes in Computer Science

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A conceptual clustering is a set of formal concepts (i.e., closed itemsets) that defines a partition of a set of transactions. Finding a conceptual clustering is an N P-complete problem for which Constraint Programming (CP) and Integer Linear Programming (ILP) approaches have been recently proposed. We introduce new CP models to solve this problem: a pure CP model that uses set constraints, and an hybrid model that uses a data mining tool to extract formal concepts in a preprocessing step and then uses CP to select a subset of formal concepts that defines a partition. We compare our new models with recent CP and ILP approaches on classical machine learning instances. We also introduce a new set of instances coming from a real application case, which aims at extracting setting concepts from an Enterprise Resource Planning (ERP) software. We consider two classic criteria to optimize, i.e., the frequency and the size. We show that these criteria lead to extreme solutions with either very few small formal concepts or many large formal concepts, and that compromise clusterings may be obtained by computing the Pareto front of non dominated clusterings.

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Multi-Objective Large Neighborhood Search

Cited by 18 publications

References 18 publications

Constrained clustering by constraint programming

Constrained clustering by constraint programming

A Declarative and Expressive Approach to Control Forwarding Paths in Carrier-Grade Networks

Constraint Programming for Multi-criteria Conceptual Clustering

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