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Experimental and Numerical Shortest Route Optimization in Generating a Design Template for a Recreation Area in Kadifekale

  • Gülden KöktürkEmail author
  • Ayça Tokuç
  • T. Didem Altun
  • İrem Kale
  • F. Feyzal Özkaban
  • Özge Andiç Çakır
  • Aylin Şendemir
Conference paper
Part of the Green Energy and Technology book series (GREEN)

Abstract

As cities grow, their complexity and the complexity of their infrastructure for various applications increase. Especially, transportation design is usually a very cumbersome process in current urban development models, and it is becoming more complex. Traditional approaches are not always sufficient to solve such complex problems, therefore, design disciplines like architecture and urban design need new tools to optimize many parameters related to their design. An alternate way to solve this problem can be via finding shortest routes. In this context, this study aims to evaluate different shortest path algorithms within a methodological approach to urban transportation planning via either experimentation or mathematical modeling. Three methods; namely live slime mold plasmodium, Floyd–Warshall algorithm, and ant colony algorithm are used to design a template for routes within the historical Kadifekale district of Izmir, Turkey. The results from these approaches are compared, contrasted, and discussed in terms of their suitability for use as a guide for route creation. In conclusion, the parameters of an algorithm are significant on suggesting routes, thus the strengths and weaknesses of an algorithm should be carefully considered before application in a design problem.

Keywords

Ant colony optimization (ACO) algorithm Floyd–Warshall (FW) algorithm Design template Physarum polycephalum (P. polycephalumRoute planning Slime molds 

List of Symbols

\(D_{ij}\)

The density of pheromone trace between \(i\) and \(j\)

\(L_{ij}\)

The length matrix of the edges

\(A_{ij}\)

The result matrix defining shortest paths

\(p_{ij}^{k}\)

The probability between node \(i\) and node \(j\)

\(\Delta D_{ij}^{k}\)

The increment of trail level of the edge connecting \(i\) and \(j\) by ant \(k\)

\(\Delta D_{ij}\)

The total increment of pheromone trace on the edge between \(i\) and \(j\)

\(\eta_{ij}\)

Visibility from \(i\) to \(j\)

\(\alpha\)

The parameter regulating the effect of \(D_{ij}\)

\(\beta\)

The parameter regulating the effect of \(\eta_{ij}\)

\(Q\)

The pheromone amount produced per tour by ant

\(l_{k}\)

The tour length of ant \(k\)

\(k\)

Number of ants

\(\lambda\)

Evaporation rate

\(t\)

Number of iterations

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Gülden Köktürk
    • 1
    Email author
  • Ayça Tokuç
    • 2
  • T. Didem Altun
    • 2
  • İrem Kale
    • 2
    • 3
  • F. Feyzal Özkaban
    • 2
  • Özge Andiç Çakır
    • 4
  • Aylin Şendemir
    • 5
  1. 1.Department of Electrics and Electronics EngineeringDokuz Eylul UniversityIzmirTurkey
  2. 2.Department of ArchitectureDokuz Eylul UniversityIzmirTurkey
  3. 3.Department of ArchitectureBursa Technical UniversityBursaTurkey
  4. 4.Application and Research Center for Testing and Analysis (EGE-MATAL)Ege UniversityIzmirTurkey
  5. 5.Department of BioengineeringEge UniversityIzmirTurkey

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