Using software visualization technology to help genetic algorithm designers

This work is part of a three year PhD project to examine how Software Visualization(SV) can be applied to support the design and construction of Genetic Algorithms (GAs). A user survey carried out at the start of this project identified a set of key system features required by GA users. A visualization system embodying these features was then designed and a prototype built. This paper describes what genetic algorithms are and how they can be applied. It then reviews some of the survey results and their impact on the design of the visualization system. The paper concludes with an exploration of how the resulting prototype may be evaluated

Engineering design using genetic algorithms

As modern computational and modeling technologies grow, engineering design heavily relies on computer modeling and simulation to accelerate design cycles and save cost. A complex design problem will involve many design parameters and tables. Exploring design space and finding optimal solutions are still major challenges for complex systems. This dissertation proposed to use Genetic Algorithms to optimize engineering design problems. It proposed a software infrastructure to combine engineering modeling with Genetic algorithms and covered several aspects in engineering design problems. The dissertation suggested a new Genetic Algorithm (Completely dominant Genetic algorithm) to quickly identify High Performance Areas for Engineering Design. To help design engineers to explore design space, the dissertation used a new visualization tool to demonstrate high dimensional Genetic Algorithm results in dynamical graphics. Robustness of design is critical for some of the engineering design applications due to perturbation and manufacturing tolerance. This dissertation demonstrated to use Genetic Algorithms to locate robust design areas and provided a thorough discussion on robustness and diversity in depth

Assessing gaps and needs for integrating building performance optimization tools in net zero energy buildings design

This paper summarizes a study undertaken to reveal potential challenges and opportunities for integrating optimization tools in net zero energy buildings (NZEB) design. The paper reviews current trends in simulation-based building performance optimization (BPO) and outlines major criteria for optimization tools selection and evaluation. This is based on analyzing user's needs for tools capabilities and requirement specifications. The review is carried out by means of a literature review of 165 publications and interviews with 28 optimization experts. The findings are based on an inter-group comparison between experts. The aim is to assess the gaps and needs for integrating BPO tools in NZEB design. The findings indicate a breakthrough in using evolutionary algorithms in solving highly constrained envelope, HVAC and renewable optimization problems. Simple genetic algorithm solved many design and operation problems and allowed measuring the improvement in the optimality of a solution against a base case. Evolutionary algorithms are also easily adapted to enable them to solve a particular optimization problem more effectively. However, existing limitations including model uncertainty, computation time, difficulty of use and steep learning curve. Some future directions anticipated or needed for improvement of current tools are presented.Peer reviewe

Artificial Intelligence Applied to Conceptual Design. A Review of Its Use in Architecture

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] Conceptual architectural design is a complex process that draws on past experience and creativity to generate new designs. The application of artificial intelligence to this process should not be oriented toward finding a solution in a defined search space since the design requirements are not yet well defined in the conceptual stage. Instead, this process should be considered as an exploration of the requirements, as well as of possible solutions to meet those requirements. This work offers a tour of major research projects that apply artificial intelligence solutions to architectural conceptual design. We examine several approaches, but most of the work focuses on the use of evolutionary computing to perform these tasks. We note a marked increase in the number of papers in recent years, especially since 2015. Most employ evolutionary computing techniques, including cellular automata. Most initial approaches were oriented toward finding innovative and creative forms, while the latest research focuses on optimizing architectural form.This project was supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia (Ref. ED431G/01, ED431D 2017/16), and the Spanish Ministry of Economy and Competitiveness via funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13-3503) and the European Regional Development Funds (FEDER)Xunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/1

Optimization and visualization of rapid prototyping process parameters.

The optimal selection of rapid prototyping (RP) process parameters is a great concern to RP designers. When dealing with this problem, different build objectives have to be taken into consideration. Using virtual rapid prototyping (VRP) systems as a visualization tool to verify the optimally selected process parameters will assist designers in taking critical decisions regarding modeling of prototypes. This will lead to substantial improvements in part accuracy using minimal number of iterations, and no physical fabrication until confident enough to do so. The purpose of this thesis is to demonstrate that virtual validation of optimally selected process parameters can significantly reduce time and effort spent on traditional RP experimentation. To achieve the goal of this thesis, a multi-objective optimization technique is proposed and a model is generated taking into consideration different build objectives, which are surface roughness, support structure volume, build time and dimensional accuracy. The multi-objective method used is the weighted sum method, where a single utility function has been formulated, which combines all the objective functions together. The orders of magnitudes have been normalized, and finally weights have been assigned for each objective function in order to create the general formulation. (Abstract shortened by UMI.)Dept. of Industrial and Manufacturing Systems Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .E47. Source: Masters Abstracts International, Volume: 43-03, page: 0959. Adviser: Waguih ElMaraghy. Thesis (M.A.Sc.)--University of Windsor (Canada), 2004

Generative Design in Energy Efficient Buildings

This is an investigative report on the use of generative design and genetic algorithms in the design of energy-efficient buildings. It focuses on a workflow using Project Refinery© and Dynamo© in Revit® for multi-objective optimization and visualization, and Honeybee© for daylighting analysis. The workflow was not about to run completely due to instability in Dynamo, however daylighting analysis and visualizations were produced separately from demonstrations of Refinery. It concludes that while genetic algorithms have potential to be useful in energy-efficient building design, these programs are not yet fully developed and difficult to use without extensive background knowledge and fluency in Python, due to issues regarding incompatibility between software versions

A virtual engineering framework to support progressive interaction in engineering design

Engineering design encompasses a series of non-trivial decision making phases in generating initial solutions, developing mathematical models, performing analysis, and optimizing designs. Engineering analysis and optimization are the phases that often significantly slow down the design process. Thorough designer exploration on the solution space increases the likelihood of determining the most feasible solution but, at the expense of longer lead times. The exploratory capabilities of the designer could be enhanced by creating an interactive virtual engineering framework. This research presents progressive interaction with the designer-in-the-loop whose intelligence is blended with the computational power to suitably control the optimization. Progressive interaction is a human-guided preference articulation method where the designer intelligence continuously controls the engineering analysis and optimization by visualization, modification and controlled re-optimization. Based on the designer\u27s knowledge and the knowledge available from the interaction system, the designer preferences can be modified anytime to expedite optimization. Progressive interaction not only helps the designer discover the hidden relationship between the decision variables but it also uncovers the implicit constraints and other performance limitations of the design. In summary, this research work proposes human-guided, progressive interaction as a solution to complex engineering optimization problems. The proposed solution is demonstrated using three test cases: (1) Interactive image segmentation and optimization, (2) Designer interaction to support shape optimization of a finned dissipater, and (3) Interactive analysis, optimization and design of hydraulic mixing nozzle