InterCriteria Analysis of ACO and GA Hybrid Algorithms View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2015-07-15

AUTHORS

Olympia Roeva , Stefka Fidanova , Marcin Paprzycki

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. More... »

PAGES

107-126

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-21133-6_7

DOI

http://dx.doi.org/10.1007/978-3-319-21133-6_7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1017250254


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