Article

A genetic algorithm based optimization framework to visualize, evaluate, and modify 3D space configurations in Desktop VR

¹ School of Technology, Purdue University, Calumet 46323, USA
² School of Technology, Purdue University, West Lafayette 47907, USA

^* e-mail: mchandr@purduecal.edu

Received: 15 August 2013
Accepted: 12 December 2013

Abstract

This paper presents the design and implementation of a Desktop VR (Virtual Reality) framework for generating and evaluating Pareto-optimal alternate 3D spatial configurations using GA (genetic algorithms). The 3-tier framework involves the generation of the Pareto-optimal plans using GA which are subsequently visualized first using a Java-based 2D Interface and finally in the form of a 3D VR scene. The search spaces (function domains) are extremely large in today’s multifaceted interior design situations, and the optimization procedure involves conflicting objective functions, and limitations in the form of constraint functions. The interior space allocation problem is formulated and implemented as the “optimal configuration of artifacts”. When using GAs, a group of Pareto-optimal solutions (Pareto set) are available for the planners and decision-makers, wherefrom one solution ought to be picked. Therefore, this study applies a tool to not only visually evaluate the plans, but also to interact with those plans to develop them further if needed. Besides enabling the optimal spatial configuration of the scene elements, this framework also facilitates evaluation and interaction via the 3D VR worlds. The framework aids the proactive exploration, analysis, and finalization of design aspects such as color, size, lighting, etc. of the various elements prior to the actual construction. The results demonstrate the robust performance of the GA and the final 3D VR environment with dynamic interactive capabilities. This final interface facilitates “GA-Compliant” transformations and scene modifications thereby facilitating the exploration and examination of alternative scene configurations.