Benchmarking heuristic search and optimisation algorithms in Matlab

Wuqiao Luo, Yun Li

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

7 Citations (Scopus)

Abstract

With the proliferating development of heuristic methods, it has become challenging to choose the most suitable ones for an application at hand. This paper evaluates the performance of these algorithms available in Matlab, as it is problem dependent and parameter sensitive. Further, the paper attempts to address the challenge that there exists no satisfied benchmarks to evaluation all the algorithms at the same standard. The paper tests five heuristic algorithms in Matlab, the Nelder-Mead simplex search, the Genetic Algorithm, the Genetic Algorithm with elitism, Simulated Annealing and Particle Swarm Optimization, with four widely adopted benchmark problems. The Genetic Algorithm has an overall best performance at optimality and accuracy, while PSO has fast convergence speed when facing unimodal problem.

Original languageEnglish
Title of host publication2016 22nd International Conference on Automation and Computing, ICAC 2016
Subtitle of host publicationTackling the New Challenges in Automation and Computing
Pages250-255
Number of pages6
DOIs
Publication statusPublished - 20 Oct 2016
Event22nd International Conference on Automation and Computing, ICAC 2016 - University of Essex, Colchester, United Kingdom
Duration: 7 Sep 20168 Sep 2016
http://www.cacsuk.co.uk/index.php/conferences

Conference

Conference22nd International Conference on Automation and Computing, ICAC 2016
Abbreviated titleICAC 2016
CountryUnited Kingdom
CityColchester
Period7/09/168/09/16
Internet address

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Keywords

  • benchmarks
  • evolutionary algorithms
  • numerical optimization
  • single-objective

Cite this

Luo, W., & Li, Y. (2016). Benchmarking heuristic search and optimisation algorithms in Matlab. In 2016 22nd International Conference on Automation and Computing, ICAC 2016: Tackling the New Challenges in Automation and Computing (pp. 250-255). [7604927] https://doi.org/10.1109/IConAC.2016.7604927