AN EXPERIMENTAL STUDY OF HYBRIDIZING CULTURAL ALGORITHMS AND LOCAL SEARCH

In this paper the performance of the Cultural Algorithms-Iterated Local Search (CA-ILS), a new continuous optimization algorithm, is empirically studied on multimodal test functions proposed in the Special Session on Real-Parameter Optimization of the 2005 Congress on Evolutionary Computation. It is compared with state-of-the-art methods attending the Session to find out whether the algorithm is effective in solving difficult problems. The test results show that CA-ILS may be a competitive method, at least in the tested problems. The results also reveal the classes of problems where CA-ILS can work well and/or not well.

Design Optimization of a Vehicle B-Pillar Subjected to Roof Crush Using Mixed Reactive Taboo Search

The primary obstacle in automated design for crashworthiness is the heavy computational resources required during the optimization processes. Hence it is desirable to develop efficient optimization algorithms capable of finding good solutions without requiring too many model simulations. This paper presents an efficient mixed discrete and continuous optimization algorithm, Mixed Reactive Taboo Search (MRTS), and its application to the design of a vehicle B-Pillar subjected to roof crush conditions. The problem is sophisticated enough to explore the MRTS’ capability of identifying multiple local optima with a single optimization run, yet the associated finite element model (FEM) is not too large to make the computational resources required for global optimization prohibitive. The optimization results demonstrated that a single run of MRTS identified a set of better designs with smaller number of simulation runs, than multiple runs of Sequential Quadratic Programming (SQP) with several starting points.