Evolutionary Algorithms for Designing Self-sufficient Floating Neighborhoods

Abstract

Floating neighborhoods are innovative and promising urban areas for challenges in the development of cities and settlements. However, this design task requires a lot of considerations and technical challenges. Computational tools and methods can be beneficial to tackle the complexity of floating neighborhood design. This paper considers the design of a self-sufficient floating neighborhood by using computational intelligence techniques. In this respect, we consider a design problem for locating each neighborhood function in each cluster with a certain density within a floating neighborhood. In order to develop a self-sufficient floating neighborhood, we propose multi-objective evolutionary algorithms , namely, a self-adaptive real-coded genetic algorithm (CGA) as well as a self-adaptive real-coded genetic algorithm (CGA_DE) employing mutation operator of differential evolution algorithm. The only difference between CGA and CGA_DE is the fact that CGA uses random immigration of certain individuals into the population as a mutation operator whereas in the mutation phase of CGA_DE algorithm, the traditional mutation operator DE/rand/1/bin of DE algorithms. The arrangement of individual functions to develop each neighborhood function is further elaborated and formed by using Voronoi diagram algorithm. An application to design a self-sufficient floating neighborhood in Urla district, which is on the west coast of Turkey, İzmir, is presented.