Targetmatchingproblemsandanadaptiveconstraintstrategy.pdfVIP

Target Geometry Matching Problem with Conflicting Objectives for Multiobjective Topology Design Optimization Using GA K. Tai, N.F. Wang and Y.W. Yang Abstract— Genetic algorithms (GA) do have some advantages problems or multiobjective with non-conflicting objectives. over gradient-based methods for solving topology design opti- In this work, the multiple objectives are conflicting and thus mization problems. However, their success depends largely on introduce trade-off solutions which provide an interesting the geometric representation used. In this work, an enhanced morphological representation of geometry is applied and eval- demonstration and a quantitative measure of how well the uated to be efficient and effective in producing good results GA works in producing the set of Pareto optimal solutions. via a target matching problem : a simulated topology and The idea of using predefined target shapes as a means to shape design optimization problem where a ‘target’ geometry check the performance of evolutionary shape optimization set is first predefined as the Pareto optimal solutions and a algorithms was first introduced in [11] and further explored multiobjective optimization problem formulated such that the design solutions will evolve and converge towards the target in [12], [13]. However, these involved shape optimization geometry set. As the objectives (and constraints) are conflicting, only, using spline curves for boundary shape representations, the problem is challenging and an adaptive constraint strategy whereas the work here involves the optimization of both is also incorporated in the GA to improve convergence towards