Global solution of multi-objective optimal control problems with multi agent collaborative search and direct finite elements transcription

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Ricciardi, Lorenzo A. and Vasile, Massimiliano and Maddock, Christie; (2016) Global solution of multi-objective optimal control problems with multi agent collaborative search and direct finite elements transcription. In: 2016 IEEE Congress on Evolutionary Computation, (CEC). IEEE, CAN, pp. 869-876. ISBN 9781509006236 (https://doi.org/10.1109/CEC.2016.7743882)

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Abstract

This paper addresses the solution of optimal control problems with multiple and possibly conflicting objective functions. The solution strategy is based on the integration of Direct Finite Elements in Time (DFET) transcription into the Multi Agent Collaborative Search (MACS) framework. Multi Agent Collaborative Search is a memetic algorithm in which a population of agents performs a set of individual and social actions looking for the Pareto front. Direct Finite Elements in Time transcribe an optimal control problem into a constrained Non-linear Programming Problem (NLP) by collocating states and controls on spectral bases. MACS operates directly on the NLP problem and generates nearly-feasible trial solutions which are then submitted to a NLP solver. If the NLP solver converges to a feasible solution, an updated solution for the control parameters is returned to MACS, along with the corresponding value of the objective functions. Both the updated guess and the objective function values will be used by MACS to generate new trial solutions and converge, as uniformly as possible, to the Pareto front. To demonstrate the applicability of this strategy, the paper presents the solution of the multi-objective extensions of two well-known space related optimal control problems: the Goddard Rocket problem, and the maximum energy orbit rise problem.

ORCID iDs

Ricciardi, Lorenzo A. ORCID logoORCID: https://orcid.org/0000-0002-0895-7961, Vasile, Massimiliano ORCID logoORCID: https://orcid.org/0000-0001-8302-6465 and Maddock, Christie ORCID logoORCID: https://orcid.org/0000-0003-1079-4863;
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