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.
|Title of host publication||Optimization in Industry|
|Subtitle of host publication||Present Practices and Future Scopes|
|Editors||S. Datta, J. Davim|
|Place of Publication||Cham, Switzerland|
|Publication status||Published - 2019|
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- Computational design
- Performance-based design
- Floating city
- Multi-objective optimization
- Urban design
- Genetic algorithm
- Differential evolution
- Voronoi diagram