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Erschienen in:
Rough Sets and Rule Induction from Indiscernibility Relations Based on Possible World Semantics in Incomplete Information Systems with Continuous Domains Kapitel lesen Erstes Kapitel lesen

2021 | OriginalPaper | Buchkapitel

Particle Swarm Optimization and Grey Wolf Optimizer to Solve Continuous p-Median Location Problems

verfasst von : Hassan Mohamed Rabie

Erschienen in: Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges

Verlag: Springer International Publishing

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Abstract

The continuous p-median location problem is to locate p facilities in the Euclidean plane in such a way that the sum of distances between each demand point and its nearest median/facility is minimized. In this chapter, the continuous p-median problem is studied, and a proposed Grey Wolf Optimizer (GWO) algorithm, which has not previously been applied to solve this problem, is presented and compared to a proposed Particle Swarm Optimization (PSO) algorithm. As an experimental evidence for the NFL theorem, the experimental results showed that the no algorithm can outperformed the other in all cases, however the proposed PSO has better performance in most of the cases. The experimental results show that the two proposed algorithms have better performance than other PSO methods in the literature.

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Vorheriges Kapitel Deep Learning for Taxonomic Classification of Biological Bacterial Sequences
Nächstes Kapitel Gene Ontology Analysis of Gene Expression Data Using Hybridized PSO Triclustering
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Metadaten
Titel
Particle Swarm Optimization and Grey Wolf Optimizer to Solve Continuous p-Median Location Problems
verfasst von
Hassan Mohamed Rabie
Copyright-Jahr
2021
Buch
Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges

Print ISBN: 978-3-030-59337-7

Electronic ISBN: 978-3-030-59338-4

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-59338-4_21

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