Evolutionary and Heuristic Methods Applied to Problems in Optimal Control

Bruce A. Conway ()
Additional contact information
Bruce A. Conway: 306 Talbot Laboratory, University of Illinois

A chapter in Variational Analysis and Aerospace Engineering, 2016, pp 117-143 from Springer

Abstract: Abstract About two decades ago years researchers began to apply a new approach, using evolutionary algorithms or metaheuristics, to solve continuous optimal control problems. The evolutionary algorithms use the principle of “survival of the fittest” applied to a population of individuals representing candidate solutions for the optimal trajectories. Metaheuristics optimize by iteratively acting to improve candidate solutions, often using stochastic methods. Because of certain compromises that are usually necessary when transcribing the problem for solution by these methods it has been thought that they were not capable of yielding accurate solutions. However that is a misconception as is demonstrated by examples in this work.

Keywords: Optimal control; Evolutionary algorithms; Metaheuristic algorithms (search for similar items in EconPapers)
Date: 2016
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-319-45680-5_5

Ordering information: This item can be ordered from
http://www.springer.com/9783319456805

DOI: 10.1007/978-3-319-45680-5_5

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().