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.
Original languageEnglish
Title of host publicationOptimization in Industry
Subtitle of host publicationPresent Practices and Future Scopes
EditorsS. Datta, J. Davim
Place of PublicationCham, Switzerland
ISBN (Electronic)978-3-030-01641-8
ISBN (Print)978-3-030-01640-1
Publication statusPublished - 2019

    Research areas

  • Computational design, Performance-based design, Self-sufficient, Floating city, Multi-objective optimization, Urban design, Genetic algorithm, Differential evolution, Form-finding, Voronoi diagram

ID: 55232319