Spatial resource allocation via extremal optimization enhanced by cell-based local search

A new treatment is presented for land use planning problems by means of extremal optimization (EO) in conjunction to cell-based neighborhood local search. EO, inspired by self-organized critical models of evolution has been applied mainly to the solution of classical combinatorial optimization problems. Cell-based local search has been employed by the author elsewhere in problems of spatial resource allocation in combination with genetic algorithms and simulated annealing. In this paper, it complements EO in order to enhance its capacity for a spatial optimization problem. The hybrid method thus formed is compared to methods of the literature on a specific resource allocation problem by taking into account both the development and the transportation cost. It yields better results both in terms of objective function values and in terms of compactness. The latter is an important quantity for spatial planning and its meaning is discussed. The appearance of significant compactness values as emergent results is investigated.