Authors:
Gerrit Anders
;
Florian Siefert
and
Wolfgang Reif
Affiliation:
University of Augsburg, Germany
Keyword(s):
Set Partitioning Problem; Clustering; Particle Swarm Optimization; Evolutionary Computing.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Computational Intelligence
;
Constraint Satisfaction
;
Distributed and Mobile Software Systems
;
Enterprise Information Systems
;
Evolutionary Computing
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Multi-Agent Systems
;
Soft Computing
;
Software Engineering
;
Symbolic Systems
Abstract:
Solving the set partitioning problem (SPP) is at the heart of the formation of several organizational structures
in multi-agent systems (MAS). In large-scale MAS, these structures can improve scalability and enable cooperation
between agents with (different) limited resources and capabilities. In this paper, we present a discrete
Particle Swarm Optimizer, i.e., a metaheuristic, that solves the NP-hard SPP in the context of partitioning constraints
– which restrict the structure of valid partitionings in terms of acceptable ranges for the number and
the size of partitions – in a general manner. It is applicable to a broad range of applications in which regional
or global knowledge is available. For example, our algorithm can be used for coalition structure generation,
strict partitioning clustering (with outliers), anticlustering, and, in combination with an additional control loop,
even for the creation of hierarchical system structures. Our algorithm relies on basic set operations t
o come to
a solution and, as our evaluation shows, finds high-quality solutions in different scenarios.
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