scholarly journals Application of Genetic Algorithms and Ant Colony Optimization for Modelling of E. coli Cultivation Process

10.5772/36662 ◽  
2012 ◽  
Author(s):  
Olympia Roeva ◽  
Stefka Fidanov
Keyword(s):  
Genetic Algorithms ◽  
Ant Colony Optimization ◽  
Ant Colony ◽  
E Coli ◽  
Cultivation Process

scholarly journals Multi-objective Topology Optimization with Ant Colony Optimization and Genetic Algorithms

2014 ◽  
Author(s):  
João Batista Zuliani ◽  
Miri Cohen ◽  
Lucas de Souza Batista ◽  
Frederico Gadelha Guimarães
Keyword(s):  
Genetic Algorithms ◽  
Topology Optimization ◽  
Ant Colony Optimization ◽  
Ant Colony ◽  
Multi Objective

Empirical Studies on Application of Genetic Algorithms and Ant Colony Optimization for Data Clustering

2010 ◽  
Author(s):  
Thelma Elita Colanzi ◽  
Wesley Klewerton Guez Assuncao ◽  
Aurora Trinidad Ramirez Pozo ◽  
Ana Cristina B. Kochem Vendramin ◽  
Diogo Augusto Barros Pereira
Keyword(s):  
Genetic Algorithms ◽  
Ant Colony Optimization ◽  
Data Clustering ◽  
Empirical Studies ◽  
Ant Colony

scholarly journals Tuning PID Controller Using Multiobjective Ant Colony Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Ibtissem Chiha ◽  
Noureddine Liouane ◽  
Pierre Borne
Keyword(s):  
Genetic Algorithms ◽  
Ant Colony Optimization ◽  
Ant Colony Algorithm ◽  
Optimal Solution ◽  
Ant Colony ◽  
Pid Controllers ◽  
Tuning Method ◽  
Classic Approach ◽  
Optimum Solution ◽  
Multiobjective Ant Colony Optimization

This paper treats a tuning of PID controllers method using multiobjective ant colony optimization. The design objective was to apply the ant colony algorithm in the aim of tuning the optimum solution of the PID controllers (Kp,Ki, andKd) by minimizing the multiobjective function. The potential of using multiobjective ant algorithms is to identify the Pareto optimal solution. The other methods are applied to make comparisons between a classic approach based on the “Ziegler-Nichols” method and a metaheuristic approach based on the genetic algorithms. Simulation results demonstrate that the new tuning method using multiobjective ant colony optimization has a better control system performance compared with the classic approach and the genetic algorithms.

scholarly journals A LOGISTIC PROCESS SCHEDULING PROBLEM: GENETIC ALGORITHMS OR ANT COLONY OPTIMIZATION?

2005 ◽  
Vol 38 (1) ◽  
pp. 206-211 ◽  
Author(s):  
C.A. Silva ◽  
J.M. Sousa ◽  
T. Runkler ◽  
J.M.G. Sá da Costa
Keyword(s):  
Genetic Algorithms ◽  
Ant Colony Optimization ◽  
Ant Colony ◽  
Scheduling Problem ◽  
Process Scheduling
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