|
|
Digital Library of the
European Council for Modelling and Simulation |
|
Title: |
A Comparative Study To Evolutionary Algorithms |
|
Authors: |
Eva Volna, Martin Kotyrba |
|
Published in:
|
(2014).ECMS 2014 Proceedings edited
by: Flaminio Squazzoni,
Fabio Baronio, Claudia Archetti,
Marco Castellani European Council for
Modeling and Simulation. doi:10.7148/2014 ISBN:
978-0-9564944-8-1 28th
European Conference on Modelling and Simulation, Brescia,
Italy, May 27th – 30th,
2014 |
|
Citation
format: |
Eva
Volna, Martin Kotyrba (2014). A Comparative Study To Evolutionary
Algorithms, ECMS 2014 Proceedings edited by: Flaminio
Squazzoni, Fabio Baronio,
Claudia Archetti, Marco Castellani European
Council for Modeling and Simulation. doi:10.7148/2014-0340 |
|
DOI: |
http://dx.doi.org/10.7148/2014-0340 |
|
Abstract: |
Evolutionary algorithms are
general iterative algorithms for combinatorial optimization. The term
evolutionary algorithm is used to refer to any probabilistic algorithm whose
design is inspired by evolutionary mechanisms found in biological species.
These algorithms have been found to
be very effective and robust in solving numerous problems from a wide range of application
domains. In this paper we perform a comparative study among Genetic
Algorithms (GA), Simulated Annealing (SA), Differential Evolution (DE), and
Self Organising Migrating Algorithms (SOMA). These
algorithms have many similarities, but they also possess distinctive
features, mainly in their strategies for searching the solution state space.
The four heuristics are applied on the same optimization problem - Travelling
Salesman Problem (TSP) and compared with respect to (1) quality of the best
solution identified by each heuristic, (2) progress of the search from an
initial solution until stopping criteria are met. |
|
Full
text: |
|
