A direct memetic approach to the solution of multi-objective optimal control problems

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

2 Citations (Scopus)

Abstract

This paper proposes a memetic direct transcription algorithm to solve Multi-Objective Optimal Control Problems (MOOCP). The MOOCP is first transcribed into a Non-linear Programming Problem (NLP) with Direct Finite Elements in Time (DFET) and then solved with a particular formulation of the Multi Agent Collaborative Search (MACS) framework. Multi Agent Collaborative Search is a memetic algorithm in which a population of agents combines local search heuristics, exploring the neighbourhood of each agent, with social actions exchanging information among agents. A collection of all Pareto optimal solutions is maintained in an archive that evolves towards the Pareto set. In the approach proposed in this paper, individualistic actions run a local search, from random points within the neighbourhood of each agent, solving a normalised Pascoletti-Serafini scalarisation of the multi-objective NLP problem. Social actions, instead, solve a bi-level problem in which the lower level handles only the constraint equations while the upper level handles only the objective functions. The proposed approach is tested on the multi-objective extensions of two well-known optimal control problems: the Goddard Rocket problem, and the maximum energy orbit rise problem.

LanguageEnglish
Title of host publication2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509042401
DOIs
Publication statusPublished - 13 Feb 2017
Event2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 - Royal Olympic Hotel, Athens, Greece
Duration: 6 Dec 20169 Dec 2016
http://ssci2016.cs.surrey.ac.uk/

Conference

Conference2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
Abbreviated titleSSCI 2016
CountryGreece
CityAthens
Period6/12/169/12/16
Internet address

Fingerprint

Optimal Control Problem
Nonlinear programming
Transcription
Rockets
Nonlinear Programming
Local Search
Orbits
Pareto Set
Scalarization
Memetic Algorithm
Pareto Optimal Solution
Multiobjective Programming
Optimal control
Objective function
Orbit
Heuristics
Finite Element
Formulation
Energy
Local search

Keywords

  • optimal control
  • linear programming
  • optimization
  • memetics
  • collaboration
  • finite element analysis
  • sociology
  • search problems
  • nonlinear programming
  • pareto optimisation

Cite this

Vasile, M., & Ricciardi, L. (2017). A direct memetic approach to the solution of multi-objective optimal control problems. In 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 [7850103] Piscataway: Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI.2016.7850103
Vasile, Massimiliano ; Ricciardi, Lorenzo. / A direct memetic approach to the solution of multi-objective optimal control problems. 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016. Piscataway : Institute of Electrical and Electronics Engineers Inc., 2017.
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abstract = "This paper proposes a memetic direct transcription algorithm to solve Multi-Objective Optimal Control Problems (MOOCP). The MOOCP is first transcribed into a Non-linear Programming Problem (NLP) with Direct Finite Elements in Time (DFET) and then solved with a particular formulation of the Multi Agent Collaborative Search (MACS) framework. Multi Agent Collaborative Search is a memetic algorithm in which a population of agents combines local search heuristics, exploring the neighbourhood of each agent, with social actions exchanging information among agents. A collection of all Pareto optimal solutions is maintained in an archive that evolves towards the Pareto set. In the approach proposed in this paper, individualistic actions run a local search, from random points within the neighbourhood of each agent, solving a normalised Pascoletti-Serafini scalarisation of the multi-objective NLP problem. Social actions, instead, solve a bi-level problem in which the lower level handles only the constraint equations while the upper level handles only the objective functions. The proposed approach is tested on the multi-objective extensions of two well-known optimal control problems: the Goddard Rocket problem, and the maximum energy orbit rise problem.",
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Vasile, M & Ricciardi, L 2017, A direct memetic approach to the solution of multi-objective optimal control problems. in 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016., 7850103, Institute of Electrical and Electronics Engineers Inc., Piscataway, 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016, Athens, Greece, 6/12/16. https://doi.org/10.1109/SSCI.2016.7850103

A direct memetic approach to the solution of multi-objective optimal control problems. / Vasile, Massimiliano; Ricciardi, Lorenzo.

2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016. Piscataway : Institute of Electrical and Electronics Engineers Inc., 2017. 7850103.

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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N1 - © 2017 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.

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N2 - This paper proposes a memetic direct transcription algorithm to solve Multi-Objective Optimal Control Problems (MOOCP). The MOOCP is first transcribed into a Non-linear Programming Problem (NLP) with Direct Finite Elements in Time (DFET) and then solved with a particular formulation of the Multi Agent Collaborative Search (MACS) framework. Multi Agent Collaborative Search is a memetic algorithm in which a population of agents combines local search heuristics, exploring the neighbourhood of each agent, with social actions exchanging information among agents. A collection of all Pareto optimal solutions is maintained in an archive that evolves towards the Pareto set. In the approach proposed in this paper, individualistic actions run a local search, from random points within the neighbourhood of each agent, solving a normalised Pascoletti-Serafini scalarisation of the multi-objective NLP problem. Social actions, instead, solve a bi-level problem in which the lower level handles only the constraint equations while the upper level handles only the objective functions. The proposed approach is tested on the multi-objective extensions of two well-known optimal control problems: the Goddard Rocket problem, and the maximum energy orbit rise problem.

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Vasile M, Ricciardi L. A direct memetic approach to the solution of multi-objective optimal control problems. In 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016. Piscataway: Institute of Electrical and Electronics Engineers Inc. 2017. 7850103 https://doi.org/10.1109/SSCI.2016.7850103