Algorithms for solving a spatial optimisation problem on a parallel computer
Felicity George, Nicholas J. Radcliffe, Mark Smith, Mark H. Birkin, Martin Clarke · Concurrency Practice and Experience · 1997
In a collaborative project between GMAP Ltd and EPCC, an existing heuristic optimisation scheme for strategic resource planning was parallelised to run on the data parallel Connection Machine CM-200. The parallel software was found to run over 2700 times faster than the original workstation software. This has allowed the exploration of complex business planning strategies at a national, rather than regional, level for the first time. The availability of a very fast evaluation program for planning solutions also enabled an investigation of the use of genetic algorithms in place of GMAP's existing heuristic optimisation scheme. The results of this study show that genetic algorithms can provide better quality solutions in terms of both predicted profit from the solution and spatial diversity to provide a range of possible solutions. This paper discusses both the parallelisation of the original optimisation scheme and the use of genetic algorithms in place of this method. © 1997 John Wiley & Sons, Ltd.