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Finding Optimal Discretization Orders for Molecular Distance Geometry by Answer Set Programming Read chapter Read first chapter

2016 | OriginalPaper | Chapter

InterCriteria Analysis of ACO and GA Hybrid Algorithms

Authors : Olympia Roeva, Stefka Fidanova, Marcin Paprzycki

Published in: Recent Advances in Computational Optimization

Publisher: Springer International Publishing

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Abstract

In this paper, the recently proposed approach for multicriteria decision making—InterCriteria Analysis (ICA)—is presented. The approach is based on the apparatus of the index matrices and the intuitionistic fuzzy sets. The idea of InterCriteria Analysis is applied to establish the relations and dependencies of considered parameters based on different criteria referred to various metaheuristic algorithms. A hybrid scheme using Genetic Algorithm (GA) and Ant Colony Optimization (ACO) is used for parameter identification of E. coli MC4110 fed-batch cultivation process model. In the hybrid GA-ACO, the GA is used to find feasible solutions to the considered optimization problem. Further ACO exploits the information gathered by GA. This process obtains a solution, which is at least as good as—but usually better than—the best solution devised by GA. Moreover, a comparison with both the conventional GA and ACO identification results is presented. Based on ICA the obtained results are examined and conclusions about existing relations and dependencies between model parameters of the E. coli process and algorithms parameters and outcomes, such as number of individuals, number of generations, value of the objective function and computational time, are discussed.

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Metadata
Title
InterCriteria Analysis of ACO and GA Hybrid Algorithms
Authors
Olympia Roeva
Stefka Fidanova
Marcin Paprzycki
Copyright Year
2016
Book
Recent Advances in Computational Optimization

Print ISBN: 978-3-319-21132-9

Electronic ISBN: 978-3-319-21133-6

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-21133-6_7

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