Optimization of bagged denim fabric behaviors using the genetic algorithms and the ant colony optimization methods

2015 ◽  
Vol 27 (6) ◽  
pp. 772-792 ◽  
Author(s):  
Mouna Gazzah ◽  
Boubaker Jaouachi ◽  
Faouzi Sakli
Keyword(s):  
Genetic Algorithms ◽  
Ant Colony Optimization ◽  
Optimization Methods ◽  
Ant Colony ◽  
Denim Fabric

Risk Budgeted Portfolio Optimization Using an Extended Ant Colony Optimization Metaheuristic

2012 ◽  
Vol 3 (4) ◽  
pp. 25-42 ◽  
Author(s):  
G. A. Vijayalakshmi Pai
Keyword(s):  
Ant Colony Optimization ◽  
Portfolio Optimization ◽  
Stock Exchange ◽  
Experimental Studies ◽  
Optimization Methods ◽  
Investment Strategy ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Data Set ◽  
Exchange Data

Risk Budgeted portfolio optimization problem centering on the twin objectives of maximizing expected portfolio return and minimizing portfolio risk and incorporating the risk budgeting investment strategy, turns complex for direct solving by classical methods triggering the need to look for metaheuristic solutions. This work explores the application of an extended Ant Colony Optimization algorithm that borrows concepts from evolution theory, for the solution of the problem and proceeds to compare the experimental results with those obtained by two other Metaheuristic optimization methods belonging to two different genres viz., Evolution Strategy with Hall of Fame and Differential Evolution, obtained in an earlier investigation. The experimental studies have been undertaken over Bombay Stock Exchange data set (BSE200: July 2001-July 2006) and Tokyo Stock Exchange data set (Nikkei225: July 2001-July 2006). Data Envelopment Analysis has also been undertaken to compare the performance of the technical efficiencies of the optimal risk budgeted portfolios obtained by the three approaches.

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.

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