A Coevolutionary, Hyper Heuristic approach to the optimization of Three-dimensional Process Plant Layouts — A comparative study

A Coevolutionary, Hyper Heuristic approach to the optimization of Three-dimensional Process Plant Layouts (3DPPLs) is explored. By taking advantage of the natural problem decomposition, one population of layout heuristics, and another population of scheduling heuristics are coevolved. Generalized heuristics are evolved by training on multiple small problem instances, so that training time is reduced. The best generalized heuristic builds arbitrary sized 3DPPLs which reduce the cost by 18% when compared to a handmade heuristic. Specialized heuristics are evolved by optimizing each problem instance and outperforms the generalized heuristics after a fixed number of generations. Compared to a direct-encoded Genetic Algorithm, the benefit of specialized heuristics increases with the size of the problem, and costs are reduced by 30% when compared to the handmade heuristic.