Abstract
Causality assignment is an important task in physical modeling by bond graphs. Traditional causality assignment algorithms have specific aims and particular purposes. However they may fail if a bond graph has loops or contains junction causality violations. Some of the assignment algorithms focuses on the generation of differential algebraic equations to take into account junction violations caused by nonlinear multi-port devices and is not suitable for general bond graphs. In this paper, we present a formulation of the causality assignment problem as a constrained multi-objective optimization problem. Previous solution techniques to this problem include multi-objective Branch-and-Bound and Pareto archived evolution strategy – both are highly complex and time-consuming algorithms. A new solution technique called gSEMO (global Simple Evolutionary Multi-objective Optimizer) is now used to solve the causality assignment problem with very promising results.
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Wong, T., Cormier, G. (2007). Bond Graph Causality Assignment and Evolutionary Multi-Objective Optimization. In: Elleithy, K. (eds) Advances and Innovations in Systems, Computing Sciences and Software Engineering. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6264-3_75
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DOI: https://doi.org/10.1007/978-1-4020-6264-3_75
Publisher Name: Springer, Dordrecht
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