- author = "Palina Bartashevich and Illya Bakurov and Sanaz Mostaghim and Leonardo Vanneschi",
- title = "{PSO}-based Search Rules for Aerial Swarms Against Unexplored Vector Fields via Genetic Programming",
- booktitle = "15th International Conference on Parallel Problem Solving from Nature",
- year = "2018",
- editor = "Anne Auger and Carlos M. Fonseca and Nuno Lourenco and Penousal Machado and Luis Paquete and Darrell Whitley",
- volume = "11101",
- series = "LNCS",
- pages = "41--53",
- address = "Coimbra, Portugal",
- month = "8-12 " # sep,
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming, Particle swarm optimization, Vector fields, Semantics, EDDA",
- isbn13 = "978-3-319-99252-5",
-
URL = "
https://www.springer.com/gp/book/9783319992587",
-
DOI = "
doi:10.1007/978-3-319-99253-2_4",
- abstract = "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 behaviour while being unaware of the dynamic external environment, mimicking the zigzag upwind flights of birds towards the food source.",
-
notes = "PPSN2018 http://ppsn2018.dei.uc.pt
This two-volume set LNCS 11101 and 11102 constitutes the refereed proceedings of the 15th International Conference on Parallel Problem Solving from Nature, PPSN 2018",
