Enhancing particle swarm optimization with socio-cognitive inspirations

Iwan Bugajski, Piotr Listkiewicz, Aleksander Byrski, Marek Kisiel-Dorohinicki, Wojciech Korczynski, Tom Lenaerts, Dana Samson, Bipin Indurkhya, Ann Nowé

Onderzoeksoutput: Conference paper

10 Citaten (Scopus)


We incorporate socio-cognitively inspired metaheuristics, which we have used successfully in the ACO algorithms in our past research, into the classical particle swarm optimization algorithms. The swarm is divided into species and the particles get inspired not only by the global and local optima, but share their knowledge of the optima with neighboring agents belonging to other species. Our experimental research gathered for common benchmark functions tackled in 100 dimensions show that the metaheuristics are effective and perform better than the classic PSO. We experimented with various proportions of different species in the swarm population to find the best mix of population.
Originele taal-2English
TitelProcedia Computer Science
Aantal pagina's10
StatusPublished - 2016
EvenementInternational Conference on Computational Science - San Diego, United States
Duur: 6 jun 20168 jun 2016


ConferenceInternational Conference on Computational Science
Verkorte titelICCS
Land/RegioUnited States
StadSan Diego
Internet adres


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