Handling constraints for manufacturing process optimisation using genetic algorithms

Authors:
Jing Ying Zhang;John B. Morehouse;Steven Y. Liang;Jun Yao;Xiaoqin Zhou
Affiliations:
George W. Woodruff School of Mechanical Engineering, Manufacturing Research Center, Georgia Institute of Technology, Atlanta, GA 30332-0405, USA.;George W. Woodruff School of Mechanical Engineering, Manufacturing Research Center, Georgia Institute of Technology, Atlanta, GA 30332-0405, USA.;George W. Woodruff School of Mechanical Engineering, Manufacturing Research Center, Georgia Institute of Technology, Atlanta, GA 30332-0405, USA.;Shanghai Machine Tool Works, Shanghai 200093, PR China.;Shanghai Machine Tool Works, Shanghai 200093, PR China
Venue:
International Journal of Computer Applications in Technology
Year:
2007

Citing 8
Cited 1

Genetic algorithms + data structures = evolution programs (3rd ed.)

Genetic algorithms + data structures = evolution programs (3rd ed.)
Evolutionary algorithms for constrained engineering problems

Computers and Industrial Engineering
Evolutionary Algorithms in Engineering Applications

Evolutionary Algorithms in Engineering Applications
Using Genetic Algorithms in Engineering Design Optimization with Non-Linear Constraints

Proceedings of the 5th International Conference on Genetic Algorithms
A Decoder-Based Evolutionary Algorithm for Constrained Parameter Optimization Problems

PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Co-evolutionary Constraint Satisfaction

PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization

Evolutionary Computation
Evolutionary algorithms for constrained parameter optimization problems

Evolutionary Computation

Copula estimation of distribution algorithms based on exchangeable Archimedean copula

International Journal of Computer Applications in Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

Handling constraints is a common challenge to all optimisationmethods. To no exception is the planning and optimisation ofmanufacturing processes that often involves a number of constraintsreflecting the complicated reality of manufacturing to which thepursuit of the best operation condition is subject. Mathematicalmodels describing today's manufacturing processes are generallydiscontinuous, non-explicit, and not analytically differentiable;all of which renders traditional optimisation methods difficult toapply. Genetic Algorithm (GA) is known to provide an optimisationplatform method capable of treating highly nonlinear andill-behaved complex problems, thereby making it an appealingcandidate. However, several issues in regard to the handlingconstraints must be rigorously addressed in order for GA to becomea viable and effective method for manufacturing optimisation. Inthis paper, a new constraint handling strategy combined with(α,μ)-population initialisation is proposed. Twelvenumerical test cases and one surface grinding process optimisationare presented to evaluate its optimisation performance.