Aerospace Vehicle Trajectory Design and Optimization Within a Multi-Disciplinary Environment

Windhorst, Robert; Galloway, Eric; Lau, Eric; Saunders, David; Gage, Peter

doi:10.2514/1.18410

Cited by 4 publications

(1 citation statement)

References 8 publications

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“…The genetic algorithm (GA) approach and enhanced versions of this approach have received considerable attention in the literature on engineering design and optimization [11][12][13]. GAs have been shown to be effective when the analysis model is inherently non-linear and the design problem is represented by a mixture of continuous, integer, and/or discrete design variables [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Gate positioning design of injection mould using bi-objective micro genetic algorithm

Lee

Lee²

2008

Proceedings of the Institution of Mechanical Engineers, Part B:

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The use of a micro genetic algorithm (mGA)-based approach to solve a bi-objective optimization of an injection mould design problem is presented. The advantage of the mGAbased approach is that it requires fewer computational resources than a conventional GA because it has a smaller population than a conventional GA. The main drawback of the mGAbased approach is that design diversity is not secured when multi-modal and multi-objective designs are investigated. To implement the mGA-based bi-objective optimization procedure, the present study proposes a memory set, a filtering process, weight control, and reproduction from the memory set in order to explore new optimal solutions, and identify more-evenly distributed Pareto surfaces. A number of mathematical functions and a typical structural optimization problem are tested to verify the proposed strategies. The approach is subsequently applied to the bi-objective injection moulding design problem of minimizing both the maximum injection pressure and maximum pressure difference between the gate positions in the runner system.

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