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Discrete Energy Laws for the First-Order System Least-Squares Finite-Element Approach Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Influence of Ant Colony Optimization Parameters on the Algorithm Performance

Authors : Stefka Fidanova, Olympia Roeva

Published in: Large-Scale Scientific Computing

Publisher: Springer International Publishing

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Abstract

In this paper an Ant Colony Optimization (ACO) algorithm for parameter identification of cultivation process models is proposed. In computational point of view it is a hard problem. To be solved problem with a high accuracy in reasonable time, metaheuristic techniques are used. The influence of ACO algorithm parameters, namely number of agents (ants) and number of iterations, to the quality of achieved solution is investigated. As a case study an E. coli fed-batch cultivation process is explored. Based on the parameter identification of E. coli MC4110 cultivation process model some conclusions for the optimal ACO parameter settings are done.

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Metadata
Title
Influence of Ant Colony Optimization Parameters on the Algorithm Performance
Authors
Stefka Fidanova
Olympia Roeva
Copyright Year
2018
Book
Large-Scale Scientific Computing

Print ISBN: 978-3-319-73440-8

Electronic ISBN: 978-3-319-73441-5

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-73441-5_38

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