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Erschienen in:
Possibilistic Framework for Multi-objective Optimization Under Uncertainty Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

12. A Probabilistic Finite State Machine Design of Particle Swarm Optimization

verfasst von : Abdellatif El Afia, Malek Sarhani, Oussama Aoun

Erschienen in: Bioinspired Heuristics for Optimization

Verlag: Springer International Publishing

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Abstract

Nowadays, control is the main concern with emergent behaviours of multi-agent systems and state machine reasoning. This paper focuses on the restriction of this general issue to swarm intelligence approaches designed for solving complex optimization problems. Indeed, we propose a probabilistic finite state machine for controlling particles behaviour of the particle swarm optimization algorithm. That is, our multi-agent approach consists of assigning different roles to each particle based on its probabilistic finite state machine control which is used to address this issue. We performed evaluations on ten benchmark functions to test our control scheme for particles. Experimental results show that our proposed scheme gives a distinguishable out-performance on a number of state of the art of PSO variants.

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Vorheriges Kapitel CHN and Min-Conflict Heuristic to Solve Scheduling Meeting Problems
Nächstes Kapitel Algorithm Selector and Prescheduler in the ICON Challenge
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Metadaten
Titel
A Probabilistic Finite State Machine Design of Particle Swarm Optimization
verfasst von
Abdellatif El Afia
Malek Sarhani
Oussama Aoun
Copyright-Jahr
2019
Buch
Bioinspired Heuristics for Optimization

Print ISBN: 978-3-319-95103-4

Electronic ISBN: 978-3-319-95104-1

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-319-95104-1_12