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 language | English |
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Title of host publication | 2016 22nd International Conference on Automation and Computing, ICAC 2016 |
Subtitle of host publication | Tackling the New Challenges in Automation and Computing |
Pages | 250-255 |
Number of pages | 6 |
DOIs | |
Publication status | Published - 20 Oct 2016 |
Event | 22nd International Conference on Automation and Computing, ICAC 2016 - University of Essex, Colchester, United Kingdom Duration: 7 Sept 2016 → 8 Sept 2016 http://www.cacsuk.co.uk/index.php/conferences |
Conference
Conference | 22nd International Conference on Automation and Computing, ICAC 2016 |
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Abbreviated title | ICAC 2016 |
Country/Territory | United Kingdom |
City | Colchester |
Period | 7/09/16 → 8/09/16 |
Internet address |
Keywords
- benchmarks
- evolutionary algorithms
- numerical optimization
- single-objective