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Handling constraints for manufacturing process optimisation using genetic algorithms
by Jing Ying Zhang, John B. Morehouse, Steven Y. Liang, Jun Yao, Xiaoqin Zhou
International Journal of Computer Applications in Technology (IJCAT), Vol. 28, No. 1, 2007
Abstract: Handling constraints is a common challenge to all optimisation methods. To no exception is the planning and optimisation of manufacturing processes that often involves a number of constraints reflecting the complicated reality of manufacturing to which the pursuit of the best operation condition is subject. Mathematical models describing today's manufacturing processes are generally discontinuous, non-explicit, and not analytically differentiable; all of which renders traditional optimisation methods difficult to apply. Genetic Algorithm (GA) is known to provide an optimisation platform method capable of treating highly nonlinear and ill-behaved complex problems, thereby making it an appealing candidate. However, several issues in regard to the handling constraints must be rigorously addressed in order for GA to become a viable and effective method for manufacturing optimisation. In this paper, a new constraint handling strategy combined with (α,μ)-population initialisation is proposed. Twelve numerical test cases and one surface grinding process optimisation are presented to evaluate its optimisation performance.

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