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**Harvard**

Glorieux, E., Svensson, B., Danielsson, F. och Lennartson, B. (2014) *A constructive cooperative coevolutionary algorithm applied to press line optimisation*.

** BibTeX **

@conference{

Glorieux2014,

author={Glorieux, E. and Svensson, B. and Danielsson, F. and Lennartson, Bengt},

title={A constructive cooperative coevolutionary algorithm applied to press line optimisation},

booktitle={24th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2014; San Antonio; United States; 20 May 2014 through 23 May 2014},

isbn={978-160595173-7},

pages={909-916},

abstract={Simulation-based optimisation often considers computationally expensive problems. Successfully optimising such large scale and complex problems within a practical timeframe is a challenging task. Optimisation techniques to fulfil this need to be developed. A technique to address this involves decomposing the considered problem into smaller subproblems. These subproblems are then optimised separately. In this paper, an efficient algorithm for simulation-based optimisation is proposed. The proposed algorithm extends the cooperative coevolutionary algorithm, which optimises subproblems separately. To optimise the subproblems, the proposed algorithm enables using a deterministic algorithm, next to stochastic genetic algorithms, getting the flexibility of using either type. It also includes a constructive heuristic that creates good initial feasible solutions to reduce the number of fitness calculations. The extension enables solving complex, computationally expensive problems efficiently. The proposed algorithm has been applied on automated sheet metal press lines from the automotive industry. This is a highly complex optimisation problem due to its non-linearity and high dimensionality. The optimisation problem is to find control parameters that maximises the line's production rate. These control parameters determine velocities, time constants, and cam values for critical interactions between components. A simulation model is used for the fitness calculation during the optimisation. The results show that the proposed algorithm manages to solve the press line optimisation problem efficiently. This is a step forward in press line optimisation since this is to the authors' knowledge the first time a press line has been optimised efficiently in this way.},

year={2014},

}

** RefWorks **

RT Conference Proceedings

SR Print

ID 236651

A1 Glorieux, E.

A1 Svensson, B.

A1 Danielsson, F.

A1 Lennartson, Bengt

T1 A constructive cooperative coevolutionary algorithm applied to press line optimisation

YR 2014

T2 24th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2014; San Antonio; United States; 20 May 2014 through 23 May 2014

SN 978-160595173-7

SP 909

OP 916

AB Simulation-based optimisation often considers computationally expensive problems. Successfully optimising such large scale and complex problems within a practical timeframe is a challenging task. Optimisation techniques to fulfil this need to be developed. A technique to address this involves decomposing the considered problem into smaller subproblems. These subproblems are then optimised separately. In this paper, an efficient algorithm for simulation-based optimisation is proposed. The proposed algorithm extends the cooperative coevolutionary algorithm, which optimises subproblems separately. To optimise the subproblems, the proposed algorithm enables using a deterministic algorithm, next to stochastic genetic algorithms, getting the flexibility of using either type. It also includes a constructive heuristic that creates good initial feasible solutions to reduce the number of fitness calculations. The extension enables solving complex, computationally expensive problems efficiently. The proposed algorithm has been applied on automated sheet metal press lines from the automotive industry. This is a highly complex optimisation problem due to its non-linearity and high dimensionality. The optimisation problem is to find control parameters that maximises the line's production rate. These control parameters determine velocities, time constants, and cam values for critical interactions between components. A simulation model is used for the fitness calculation during the optimisation. The results show that the proposed algorithm manages to solve the press line optimisation problem efficiently. This is a step forward in press line optimisation since this is to the authors' knowledge the first time a press line has been optimised efficiently in this way.

LA eng

OL 30