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2018 | OriginalPaper | Buchkapitel

PSO-Based Search Rules for Aerial Swarms Against Unexplored Vector Fields via Genetic Programming

verfasst von : Palina Bartashevich, Illya Bakurov, Sanaz Mostaghim, Leonardo Vanneschi

Erschienen in: Parallel Problem Solving from Nature – PPSN XV

Verlag: Springer International Publishing

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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 behavior while being unaware of the dynamic external environment, mimicking the zigzag upwind flights of birds towards the food source.

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Vorheriges Kapitel Generalized Self-adapting Particle Swarm Optimization Algorithm

Nächstes Kapitel Towards an Adaptive CMA-ES Configurator

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Titel: PSO-Based Search Rules for Aerial Swarms Against Unexplored Vector Fields via Genetic Programming
verfasst von: Palina Bartashevich
Illya Bakurov
Sanaz Mostaghim
Leonardo Vanneschi
Verlag: Springer International Publishing
Buch: Parallel Problem Solving from Nature – PPSN XV
Print ISBN: 978-3-319-99252-5

Electronic ISBN: 978-3-319-99253-2

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-99253-2_4

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