Bianchi, L., Dorigo, M., Gambardella, L.M. and Gutjahr, W.J. (2009) A Survey on Metaheuristics for Stochastic Combinatorial Optimization. Natural Computing, 8, 239-287.
https://doi.org/10.1007/s11047-008-9098-4

has been cited by the following article:

• TITLE: A Heuristic Search Approach to Multidimensional Scaling

  AUTHORS: Patrick R. McMullen

  KEYWORDS: Optimization, Search, Heuristic

  JOURNAL NAME: American Journal of Operations Research, Vol.12 No.5, September 15, 2022

  ABSTRACT: This research effort presents an approach to accomplish Multidimensional Scaling (MDS) via the heuristic approach of Simulated Annealing. Multidimensional scaling is an approach used to convert matrix-based similarity (or dissimilarity data) into spatial form, usually via two or three dimensions. Performing MDS has several important applications—Geographic Information Systems, DNA Sequencing, and Marketing Research are just a few. Traditionally, classical MDS decomposes the similarity or dissimilarity matrix into its eigensystem and uses the eigensystem to calculate spatial coordinates. Here, a heuristic search-based approach is used to find coordinates from a dissimilarity matrix that minimizes a cost function. The proposed methodology is used over a variety of problems. Experimentation shows that the presented methodology consistently outperforms solutions obtained via the classical MDS approach, and this approach can be used for other important applications.