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Ant colony optimization for optimal control
 in Proceedings of the 2008 Congress on Evolutionary Computation (CEC 2008), Hong Kong
, 2008
"... Fuzzy ant colony optimization for optimal control ∗ ..."
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Fuzzy ant colony optimization for optimal control ∗
Extending ACOR to solve multiobjective problems
 In Proceedings of the 2007 UK Workshop on Computational Intelligence
, 2007
"... Ant Colony Optimization (ACO) was first proposed to solve the Traveling Salesman Problem, and later applied to solve more problems of a combinatorial nature. Some research based on ACO to tackle continuous problems has been published, but this has not followed the original ACO metaheuristic exactly. ..."
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Ant Colony Optimization (ACO) was first proposed to solve the Traveling Salesman Problem, and later applied to solve more problems of a combinatorial nature. Some research based on ACO to tackle continuous problems has been published, but this has not followed the original ACO metaheuristic exactly. Recently, ACOR has been proposed to solve continuous function optimization problems. We have taken this work and extended it to solve multiobjective optimization problems. After an analysis of the results obtained, including comparisons with two other wellknown methods, we conclude that ACOR is a promising new technique for solving multiobjective problems. 1
GAME Hybrid SelfOrganizing Modeling
"... In this chapter, you will find a description of the recently introduced Adaptive Models Evolution (GAME) algorithm [24] with respect to its selforganizing properties and the hybrid nature of its building blocks. The GAME algorithm uses a data driven approach. Resulting models fully reflect the char ..."
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In this chapter, you will find a description of the recently introduced Adaptive Models Evolution (GAME) algorithm [24] with respect to its selforganizing properties and the hybrid nature of its building blocks. The GAME algorithm uses a data driven approach. Resulting models fully reflect the character of a data set used for training.
Presenting Author’s Biography
"... When parameters of model are being adjusted, model is learning to mimic the behaviour of a real world system. Optimization methods are responsible for parameters adjustment. The problem is that each real world system is different and its model should be of different complexity. It is almost impossib ..."
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When parameters of model are being adjusted, model is learning to mimic the behaviour of a real world system. Optimization methods are responsible for parameters adjustment. The problem is that each real world system is different and its model should be of different complexity. It is almost impossible to decide which optimization method will perform the best (optimally adjust parameters of the model). In this paper we compare the performance of several methods for nonlinear parameters optimization. The gradient based methods such as QuasiNewton or Conjugate Gradient are compared to several nature inspired methods. We designed an evolutionary algorithm selecting the best optimization methods for models of various complexity. Our experiments proved that the evolution of optimization methods for particular problems is very promising approach.
Generalized Pheromone Update for Ant Colony Learning in Continuous State Spaces
"... Generalized pheromone update for ant colony learning in continuous state spaces ∗ J. van Ast, R. Babuˇska, and B. De Schutter If you want to cite this report, please use the following reference instead: J. van Ast, R. Babuˇska, and B. De Schutter, “Generalized pheromone update for ant colony learnin ..."
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Generalized pheromone update for ant colony learning in continuous state spaces ∗ J. van Ast, R. Babuˇska, and B. De Schutter If you want to cite this report, please use the following reference instead: J. van Ast, R. Babuˇska, and B. De Schutter, “Generalized pheromone update for ant colony learning in continuous state spaces, ” Proceedings of the 2010