GRASP and path-relinking for Coalition Structure Generation

TL;DR

This paper introduces a greedy adaptive search with path-relinking for efficiently solving the NP-complete Coalition Structure Generation problem, demonstrating its effectiveness through experiments and comparisons.

Contribution

It presents a novel heuristic method combining GRASP and path-relinking specifically tailored for coalition structure generation.

Findings

Proves the method's effectiveness on complex instances.

Outperforms existing algorithms in solution quality.

Efficiently searches large coalition spaces.

Abstract

In Artificial Intelligence with Coalition Structure Generation (CSG) one refers to those cooperative complex problems that require to find an optimal partition, maximising a social welfare, of a set of entities involved in a system into exhaustive and disjoint coalitions. The solution of the CSG problem finds applications in many fields such as Machine Learning (covering machines, clustering), Data Mining (decision tree, discretization), Graph Theory, Natural Language Processing (aggregation), Semantic Web (service composition), and Bioinformatics. The problem of finding the optimal coalition structure is NP-complete. In this paper we present a greedy adaptive search procedure (GRASP) with path-relinking to efficiently search the space of coalition structures. Experiments and comparisons to other algorithms prove the validity of the proposed method in solving this hard combinatorial problem.