Agent-based simheuristics: Extending simulation-optimization algorithms via distributed and parallel computing

Panadero, Javier, Juan, Angel A., Corlu, Canan Gunes, Mozos, Jose M. and Onggo, Bhakti Stephan (2019) Agent-based simheuristics: Extending simulation-optimization algorithms via distributed and parallel computing. In WSC '18 Proceedings of the 2018 Winter Simulation Conference: Simulation for a Noble Cause. vol. 2018-December, IEEE. pp. 869-880 . (doi:10.1109/WSC.2018.8632426).

Abstract

This paper presents a novel agent-based simheuristic (ABSH) approach that combines simheuristic and multi-agent system to efficiently solve stochastic combinatorial optimization problems. In an ABSH approach, multiple agents cooperate in searching a near-optimal solution to a stochastic combinatorial optimization problem inside a vast space of feasible solutions. Each of these agents is a simheuristic algorithm integrating simulation within a metaheuristic optimization framework. Each agent follows a different pattern while exploring the solution space. However, all simheuristic agents cooperate in the search of a near-optimal solution by sharing critical information among them. The distributed nature of the multi-agent system makes it easy for ABSH to make use of parallel and distributed computing technology. This paper discusses the potential of this novel simulation-optimization approach and illustrates, with a computational experiment, the advantages that ABSH approaches offer over traditional simheuristic ones.

This record has no associated files available for download.

Contributors

Author: Bhakti Stephan Onggo

Download statistics