Experiments with firefly algorithm
Firefly Algorithm (FA) is one of the recent swarm intelligence methods developed by Xin-She Yang in 2008 [12]. FA is a stochastic, nature-inspired, meta- heuristic algorithm that can be applied for solving the hardest optimization problems. The main goal of this paper is to analyze the influence of...
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| Outros Autores: | , |
| Formato: | conferencePaper |
| Idioma: | eng |
| Publicado em: |
2014
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| Assuntos: | |
| Texto completo: | http://hdl.handle.net/1822/30837 |
| País: | Portugal |
| Oai: | oai:repositorium.sdum.uminho.pt:1822/30837 |
| Resumo: | Firefly Algorithm (FA) is one of the recent swarm intelligence methods developed by Xin-She Yang in 2008 [12]. FA is a stochastic, nature-inspired, meta- heuristic algorithm that can be applied for solving the hardest optimization problems. The main goal of this paper is to analyze the influence of changing some parameters of the FA when solving bound constrained optimization problems. One of the most important aspects of this algorithm is how far is the distance between the points and the way they are drawn to the optimal solution. In this work, we aim to analyze other ways of calculating the distance between the points and also other functions to com- pute the attractiveness of fireflies. To show the performance of the proposed modified FAs a set of 30 benchmark global optimization test problems are used. Preliminary experiments reveal that the obtained results are competitive when comparing with the original FA version. |
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