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Tesi etd-05112010-121336


Thesis type
Tesi di dottorato di ricerca
Author
PASSARO, ALESSANDRO
URN
etd-05112010-121336
Title
Niching in Particole Swarm Optimization
Settore scientifico disciplinare
INF/01
Corso di studi
INFORMATICA
Commissione
tutor Prof.ssa Starita, Antonina
Parole chiave
  • swarm intelligence
  • Particle Swarm Optimization
  • niching
  • multimodal function optimization.
Data inizio appello
11/12/2007;
Consultabilità
completa
Riassunto analitico
The Particle Swarm Optimization (PSO) algorithm, like many optimization algorithms, is designed to find a single optimal solution. When dealing with multimodal functions, it needs some modifications to be able to locate multiple optima. In a parallel with Evolutionary Computation algorithms, these modifications can be grouped in the framework of Niching. <br>In this thesis, we present a new approach to niching in PSO that is based on clustering particles to identify niches. The neighborhood structure, on which particles rely for communication, is exploited together with the niche information to perform parallel searches to locate multiple optima. The clustering approach was implemented in the k-means based PSO (kPSO), which employs the standard k-means clustering algorithm. We follow the development of kPSO, starting from a first, simple implementation, and then introducing several improvements, such as a mechanism to adaptively identify the number of clusters. <br>The final kPSO algorithm proves to be a competitive solution when compared with other existing algorithms, since it shows better performance on most multimodal functions in a commonly used benchmark set.<br><br>
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