### Abstract

Language | English |
---|---|

Title of host publication | 2016 IEEE Congress on Evolutionary Computation, (CEC) |

Place of Publication | Piscataway |

Publisher | IEEE |

Pages | 869-876 |

Number of pages | 8 |

ISBN (Print) | 9781509006236 |

DOIs | |

Publication status | Published - 21 Nov 2016 |

Event | 2016 IEEE Congress on Evolutionary Computation (IEEE CEC 2016) - Vancouver, Vancouver, Canada Duration: 24 Jul 2016 → 29 Jul 2016 http://www.wcci2016.org/ |

### Conference

Conference | 2016 IEEE Congress on Evolutionary Computation (IEEE CEC 2016) |
---|---|

Abbreviated title | IEEE CEC 2016 |

Country | Canada |

City | Vancouver |

Period | 24/07/16 → 29/07/16 |

Internet address |

### Fingerprint

### Keywords

- optimal control
- optimisation
- aerospace engineering
- space access
- launch vehicle
- finite elements in time (FET)
- collaboration
- programming

### Cite this

*2016 IEEE Congress on Evolutionary Computation, (CEC)*(pp. 869-876). [7743882] Piscataway: IEEE. https://doi.org/10.1109/CEC.2016.7743882

}

*2016 IEEE Congress on Evolutionary Computation, (CEC).*, 7743882, IEEE, Piscataway, pp. 869-876, 2016 IEEE Congress on Evolutionary Computation (IEEE CEC 2016), Vancouver, Canada, 24/07/16. https://doi.org/10.1109/CEC.2016.7743882

**Global solution of multi-objective optimal control problems with multi agent collaborative search and direct finite elements transcription.** / Ricciardi, Lorenzo A.; Vasile, Massimiliano; Maddock, Christie.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

TY - GEN

T1 - Global solution of multi-objective optimal control problems with multi agent collaborative search and direct finite elements transcription

AU - Ricciardi, Lorenzo A.

AU - Vasile, Massimiliano

AU - Maddock, Christie

N1 - © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2016/11/21

Y1 - 2016/11/21

N2 - 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.

AB - 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.

KW - optimal control

KW - optimisation

KW - aerospace engineering

KW - space access

KW - launch vehicle

KW - finite elements in time (FET)

KW - collaboration

KW - programming

UR - http://www.scopus.com/inward/record.url?scp=85008255734&partnerID=8YFLogxK

UR - http://www.wcci2016.org/

U2 - 10.1109/CEC.2016.7743882

DO - 10.1109/CEC.2016.7743882

M3 - Conference contribution book

SN - 9781509006236

SP - 869

EP - 876

BT - 2016 IEEE Congress on Evolutionary Computation, (CEC)

PB - IEEE

CY - Piscataway

ER -