Automatic Plant-Controller Input/Output Matching using Evolutionary Algorithms

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Researchers

Research units

  • Aalto University
  • St. Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO)
  • Luleå University of Technology

Abstract

Automation of software development is an actively researched problem. Search-based software engineering aims to apply various search-based techniques to software engineering problems. Recently we proposed the method for automatic generation of function block application using evolutionary algorithms and model checking and applied it to the problem of automatic generation of data connections in distributed control system. The aim of this paper is to further study this method on the problem of matching of input and output connections in a closed-loop plant-controller system. The computed fitness function distribution shows that the evaluated method successfully determines the correct input and output connections between the controller and the plant. Additionally, we evaluate how the composition of specification requirements in the fitness function affects the performance of the (1+1) evolutionary algorithm. We show that additional liveness formulas can improve the performance of the algorithm, while the introduction of safety formulas significantly decreases it.

Details

Original languageEnglish
Title of host publicationProceedings or the 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation, ETFA 2018
Publication statusPublished - 22 Oct 2018
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Emerging Technologies and Factory Automation - Torino, Italy
Duration: 4 Sep 20187 Sep 2018
Conference number: 23

Publication series

NameProceedings IEEE International Conference on Emerging Technologies and Factory Automation
PublisherIEEE
ISSN (Print)1946-0740
ISSN (Electronic)2379-9560

Conference

ConferenceIEEE International Conference on Emerging Technologies and Factory Automation
Abbreviated titleETFA
CountryItaly
CityTorino
Period04/09/201807/09/2018

    Research areas

  • evolutionary computation, model checking, search-based software engineering

ID: 30429862