Large-scale parallelization of the Borg multiobjective evolutionary algorithm to enhance the management of complex environmental systems. (July 2015)

• Record Type: Journal Article
• Authors: Hadka, David; Reed, Patrick
• Is Part Of: Environmental modelling & software. Volume 69(2015:Jul.)
• Page Start: 353
• Page End: 369
• Publication Date: 2015-07
• Subjects: Evolutionary algorithm -- Borg MOEA -- Multiobjective optimization -- Large-scale parallelization; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2014.10.014
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: The Borg MOEA is a self-adaptive multiobjective evolutionary algorithm capable of solving complex, many-objective environmental systems problems efficiently and reliably. Water and environmental resources problems pose significant computational challenges due to their potential for large Pareto optimal sets, the presence of disjoint Pareto-optimal regions that arise from discrete choices, multi-modal suboptimal regions, and expensive objective function calculations. This work develops two large-scale parallel implementations of the Borg MOEA, the master–slave and multi-master Borg MOEA, and applies them to a highly challenging risk-based water supply portfolio planning problem. The performance and scalability of both implementations are compared on up to 16384 processors. The multi-master Borg MOEA is shown to scale efficiently on tens of thousands of cores while dramatically improving the reliability of attaining high-quality solutions. Our results dramatically expand the scale and scope of complex environmental systems that can be addressed using many-objective evolutionary optimization. Highlights: Massively parallel extensions of the Borg multiobjective evolutionary algorithm. Cooperating instances of master slave parallelizations dramatically enhance search. Parallelizing the Borg MOEA improves efficiency, search quality, and reliability. Discrete event simulation shows theoretical scalability for 100, 000 compute cores. 250+ years of search possible in 24 h.