Comparative Analysis of Rank and Roulette Wheel Selection Strategies in Genetic Algorithms for Spatial Layout Optimization

Abstract

Autonomous urban planning, facility layout design, and interior design are critical and meticulous tasks that require the optimization of space arrangement. One of the main purposes of space arrangement is to achieve high space utilization with a non-complex arrangement for emergency assistance, particularly to enhance pedestrian safety in panic situations. This study explores the optimization of spatial layouts by employing Genetic Algorithms (GA) due to their robust search capabilities. However, spatial layout size limitations may affect the search capability and significantly impact space arrangement and utilization. Hence, this study presents a comparative study of two GA selection operator methods: Rank Selection (RS) and Roulette Wheel Selection (RWS) for determining the effectiveness in optimizing spatial layout arrangements and space utilization. The results demonstrated significant improvements in crowd flow management, with the RWS method showing the highest fitness value despite slower convergence compared to RS. The study highlighted the impact of different methods on the convergence of the multi-objective fitness value based on space elements such as overlapping and standard walkway distances. While both selection methods proved to be effective in optimizing space utilization, the RWS method demonstrated greater computational efficiency while still adhering to standard layout designs. This efficiency helps to ensure smoother evacuation and ease of movement during emergency situations.