TY - GEN
T1 - PSO-based search rules for aerial swarms against unexplored vector fields via genetic programming
AU - Bartashevich, Palina
AU - Bakurov, Illya
AU - Mostaghim, Sanaz
AU - Vanneschi, Leonardo
N1 - Bartashevich, P., Bakurov, I., Mostaghim, S., & Vanneschi, L. (2018). PSO-based search rules for aerial swarms against unexplored vector fields via genetic programming. In Parallel Problem Solving from Nature – PPSN XV: 15th International Conference, 2018, Proceedings (pp. 41-53). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11101 LNCS). [15th International Conference on Parallel Problem Solving from Nature, PPSN 2018, 8 to 12 september 2018, Coimbra, Portugal] Springer Verlag. DOI: 10.1007/978-3-319-99253-2_4
PY - 2018/1/1
Y1 - 2018/1/1
N2 - In this paper, we study Particle Swarm Optimization (PSO) as a collective search mechanism for individuals (such as aerial micro-robots) which are supposed to search in environments with unknown external dynamics. In order to deal with the unknown disturbance, we present new PSO equations which are evolved using Genetic Programming (GP) with a semantically diverse starting population, seeded by the Evolutionary Demes Despeciation Algorithm (EDDA), that generalizes better than standard GP in the presence of unknown dynamics. The analysis of the evolved equations shows that with only small modifications in the velocity equation, PSO can achieve collective search behavior while being unaware of the dynamic external environment, mimicking the zigzag upwind flights of birds towards the food source.
AB - In this paper, we study Particle Swarm Optimization (PSO) as a collective search mechanism for individuals (such as aerial micro-robots) which are supposed to search in environments with unknown external dynamics. In order to deal with the unknown disturbance, we present new PSO equations which are evolved using Genetic Programming (GP) with a semantically diverse starting population, seeded by the Evolutionary Demes Despeciation Algorithm (EDDA), that generalizes better than standard GP in the presence of unknown dynamics. The analysis of the evolved equations shows that with only small modifications in the velocity equation, PSO can achieve collective search behavior while being unaware of the dynamic external environment, mimicking the zigzag upwind flights of birds towards the food source.
KW - EDDA
KW - Genetic Programming
KW - Particle swarm optimization
KW - Semantics
KW - Vector fields
UR - http://www.scopus.com/inward/record.url?scp=85053632052&partnerID=8YFLogxK
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000458557700004
U2 - 10.1007/978-3-319-99253-2_4
DO - 10.1007/978-3-319-99253-2_4
M3 - Conference contribution
AN - SCOPUS:85053632052
SN - 9783319992525
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 41
EP - 53
BT - Parallel Problem Solving from Nature – PPSN XV
PB - Springer Verlag
T2 - 15th International Conference on Parallel Problem Solving from Nature, PPSN 2018
Y2 - 8 September 2018 through 12 September 2018
ER -