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Rough Sets and Rule Induction from Indiscernibility Relations Based on Possible World Semantics in Incomplete Information Systems with Continuous Domains Read chapter Read first chapter

2021 | OriginalPaper | Chapter

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

Author : Hassan Mohamed Rabie

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

Publisher: 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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previous chapter Deep Learning for Taxonomic Classification of Biological Bacterial Sequences
next chapter Gene Ontology Analysis of Gene Expression Data Using Hybridized PSO Triclustering
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Metadata
Title
Particle Swarm Optimization and Grey Wolf Optimizer to Solve Continuous p-Median Location Problems
Author
Hassan Mohamed Rabie
Copyright Year
2021
Book
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 Year: 2021

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

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