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2019 | OriginalPaper | Buchkapitel

An Adaptive Parameter Free Particle Swarm Optimization Algorithm for the Permutation Flowshop Scheduling Problem

verfasst von : Yannis Marinakis, Magdalene Marinaki

Erschienen in: Machine Learning, Optimization, and Data Science

Verlag: Springer International Publishing

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Abstract

The finding of suitable values for all parameters of a Particle Swarm Optimization (PSO) algorithm is a crucial issue in the design of the algorithm. A trial and error procedure is the most common way to find the parameters but, also, a number of different procedures have been applied in the past. In this paper, an adaptive strategy is used where random values are assigned in the initialization of the algorithm and, then, during the iterations the parameters are optimized together and simultaneously with the optimization of the objective function of the problem. This approach is used for the solution of the Permutation Flowshop Scheduling Problem. The algorithm is tested in 120 benchmark instances and is compared with a number of algorithms from the literature.

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Vorheriges Kapitel A Beam Search for the Longest Common Subsequence Problem Guided by a Novel Approximate Expected Length Calculation

Nächstes Kapitel The Measure of Regular Relations Recognition Applied to the Supervised Classification Task

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Titel: An Adaptive Parameter Free Particle Swarm Optimization Algorithm for the Permutation Flowshop Scheduling Problem
verfasst von: Yannis Marinakis
Magdalene Marinaki
Verlag: Springer International Publishing
Buch: Machine Learning, Optimization, and Data Science
Print ISBN: 978-3-030-37598-0

Electronic ISBN: 978-3-030-37599-7

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-37599-7_15

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