Scatter Search for Constrained Layout Optimization Problem

We explore the meta-heuristic approach called scatter search for the constrained layout optimization. The scatter search framework is flexible and allows the development of alternative implementations for different problems. We empirically study the coordinate transformation-based diversification method and the reference set update method on the basis of two dissimilarities. We present two approaches using a scatter search framework. One only uses the gradient descent algorithm as the improvement method, so-called basic scatter search. The other, so- called hybrid scatter search, adopts gradient descent and Nelder-Mead simplex algorithm to improve the trial solution generated at two different stages. We attempt to determine the efficiency and efficacy of two variants of scatter search. Case studies show that the experimental results are either superior or comparable to the best known published results. The results also show that the hybrid scatter search is better than the basic scatter search for the constrained layout optimization problems.