Large-scale parallelization of the Borg multiobjective evolutionary algorithm to enhance the management of complex environmental systems

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 opti...

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Bibliographic Details
Published in:Environmental modelling & software : with environment data news Vol. 69; pp. 353 - 369
Main Authors: Hadka, David, Reed, Patrick
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.07.2015
Subjects:
algorithms
Borg MOEA
Computation
computer software
Environment management
environmental models
Evolutionary algorithm
Evolutionary algorithms
Large-scale parallelization
Mathematical analysis
Mathematical models
Multiobjective optimization
Optimization
planning
Self adaptive control systems
water supply
Large-scale parallelization
Borg MOEA
Evolutionary algorithm
Multiobjective optimization
ISSN:1364-8152
Online Access:Get full text
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Summary: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. •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.
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ISSN:1364-8152
DOI:10.1016/j.envsoft.2014.10.014