Heuristic search methods and cellular automata modelling for layout design

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Spatial layout design must consider not only ease of movement for pedestrians under normal conditions, but also their safety in panic situations, such as an emergency evacuation in a theatre, stadium or hospital. Using pedestrian simulation statistics, the movement of crowds can be used to study the consequences of different spatial layouts. Previous works either create an optimal spatial arrangement or an optimal pedestrian circulation. They do not automatically optimise both problems simultaneously. Thus, the idea behind the research in this thesis is to achieve a vital architectural design goal by automatically producing an optimal spatial layout that will enable smooth pedestrian flow. The automated process developed here allows the rapid identification of layouts for large, complex, spatial layout problems. This is achieved by using Cellular Automata (CA) to model pedestrian simulation so that pedestrian flow can be explored at a microscopic level and designing a fitness function for heuristic search that maximises these pedestrian flow statistics in the CA simulation. An analysis of pedestrian flow statistics generated from feasible novel design solutions generated using the heuristic search techniques (hill climbing, simulated annealing and genetic algorithm style operators) is conducted. The statistics that are obtained from the pedestrian simulation is used to measure and analyse pedestrian flow behaviour. The analysis from the statistical results also provides the indication of the quality of the spatial layout design generated. The technique has shown promising results in finding acceptable solutions to this problem when incorporated with the pedestrian simulator when demonstrated on simulated and real-world layouts with real pedestrian data.This study was funded by the University Science of Malaysia and Kementerian Pengajian Tinggi Malaysia

Using Heuristic Search with Pedestrian Simulation Statistics to Find Feasible Spatial Layout Design Elements

A spatial layout design must consider not only ease of movement, but also safety in a panic situation, such as an emergency evacuation in a theatre, stadium or hospital. Using pedestrian simulation statistics, the activity of crowds can be used to study the consequences of different spatial layouts. A cellular automaton has been proposed to model pedestrian simulation so that pedestrian flows can be explored at a microscopic level. We examined four-way pedestrian flow statistics generated from feasible seating layout solutions using heuristic search techniques (hill climbing, simulated annealing and genetic algorithm style operators). The technique has shown promising results in identifying useful characteristics of spatial layout and sources of the pedestrian clogging phenomenon

Experimental user interface design toolkit for interaction research (IDTR).

The research reported and discussed in this thesis represents a novel approach to User Interface evaluation and optimisation through cognitive modelling. This is achieved through the development and testing of a toolkit or platform titled Toolkit for Optimisation of Interface System Evolution (TOISE). The research is conducted in two main phases. In phase 1, the Adaptive Control of Thought Rational (ACT-R) cognitive architecture is used to design Simulated Users (SU) models. This allows models of user interaction to be tested on a specific User Interface (UI). In phase 2, an evolutionary algorithm is added and used to evolve and test an optimised solution to User Interface layout based on the original interface design. The thesis presents a technical background, followed by an overview of some applications in their respective fields. The core concepts behind TOISE are introduced through a discussion of the Adaptive Control of Thought “ Rational (ACT-R) architecture with a focus on the ACT-R models that are used to simulate users. The notion of adding a Genetic Algorithm optimiser is introduced and discussed in terms of the feasibility of using simulated users as the basis for automated evaluation to optimise usability. The design and implementation of TOISE is presented and discussed followed by a series of experiments that evaluate the TOISE system. While the research had to address and solve a large number of technical problems the resulting system does demonstrate potential as a platform for automated evaluation and optimisation of user interface layouts. The limitations of the system and the approach are discussed and further work is presented. It is concluded that the research is novel and shows considerable promise in terms of feasibility and potential for optimising layout for enhanced usability

An Intelligent Expert System for Decision Analysis and Support in Multi-Attribute Layout Optimization

Layout Decision Analysis and Design is a ubiquitous problem in a variety of work domains that is important from both strategic and operational perspectives. It is largely a complex, vague, difficult, and ill-structured problem that requires intelligent and sophisticated decision analysis and design support. Inadequate information availability, combinatorial complexity, subjective and uncertain preferences, and cognitive biases of decision makers often hamper the procurement of a superior layout configuration. Consequently, it is desirable to develop an intelligent decision support system for layout design that could deal with such challenging issues by providing efficient and effective means of generating, analyzing, enumerating, ranking, and manipulating superior alternative layouts. We present a research framework and a functional prototype for an interactive Intelligent System for Decision Support and Expert Analysis in Multi-Attribute Layout Optimization (IDEAL) based on soft computing tools. A fundamental issue in layout design is efficient production of superior alternatives through the incorporation of subjective and uncertain design preferences. Consequently, we have developed an efficient and Intelligent Layout Design Generator (ILG) using a generic two-dimensional bin-packing formulation that utilizes multiple preference weights furnished by a fuzzy Preference Inferencing Agent (PIA). The sub-cognitive, intuitive, multi-facet, and dynamic nature of design preferences indicates that an automated Preference Discovery Agent (PDA) could be an important component of such a system. A user-friendly, interactive, and effective User Interface is deemed critical for the success of the system. The effectiveness of the proposed solution paradigm and the implemented prototype is demonstrated through examples and cases. This research framework and prototype contribute to the field of layout decision analysis and design by enabling explicit representation of experts? knowledge, formal modeling of fuzzy user preferences, and swift generation and manipulation of superior layout alternatives. Such efforts are expected to afford efficient procurement of superior outcomes and to facilitate cognitive, ergonomic, and economic efficiency of layout designers as well as future research in related areas. Applications of this research are broad ranging including facilities layout design, VLSI circuit layout design, newspaper layout design, cutting and packing, adaptive user interfaces, dynamic memory allocation, multi-processor scheduling, metacomputing, etc

Optimisation and Decision Support during the Conceptual Stage of Building Design

Merged with duplicate record 10026.1/726 on 28.02.2017 by CS (TIS)Modern building design is complex and involves many different disciplines operating in a fragmented manner. Appropriate computer-based decision support (DS) tools are sought that can raise the level of integration of different activities at the conceptual stage, in order to help create better designs solutions. This project investigates opportunities that exist for using techniques based upon the Genetic Algorithm (GA) to support critical activities of conceptual building design (CBD). Collective independent studies have shown that the GA is a powerful optimisation and exploratory search technique with widespread application. The GA is essentially very simple yet it offers robustness and domain independence. The GA efficiently searches a domain to exploit highly suitable information. It maintains multiple solutions to problems simultaneously and is well suited to non-linear problems and those of a discontinuous nature found in engineering design. The literature search first examines traditional approaches to supporting conceptual design. Existing GA techniques and applications are discussed which include pioneering studies in the field of detailed structural design. Broader GA studies are also reported which have demonstrated possibilities for investigating geometrical, topological and member size variation. The tasks and goals of conceptual design are studied. A rationale is introduced, aimed at enabling the GA to be applied in a manner that provides the most effective support to the designer. Numerical experiments with floor planning are presented. These studies provide a basic foundation for a subsequent design support system (DSS) capable of generating structural design concepts. A hierarchical Structured GA (SGA) created by Dasgupta et al [1] is investigated to support the generation of diverse structural design concepts. The SGA supports variation in the size, shape and structural configuration of a building and in the choice of structural frame type and floor system. The benefits and limitations of the SGA approach are discussed. The creation of a prototype DSS system, abritrarily called Designer-Pro (DPRO), is described. A detailed building design model is introduced which is required for design development and appraisal. Simplifications, design rationale and generic component modelling are mentioned. A cost-based single criteria optimisation problem (SCOP) is created in which other constraints are represented as design parameters. The thesis describes the importance of the object-oriented programming (OOP) paradigm for creating a versatile design model and the need for complementary graphical user interface (GUI) tools to provide human-computer interaction (HCI) capabilities for control and intelligent design manipulation. Techniques that increase flexibility in the generation and appraisal of concept are presented. Tools presented include a convergence plot of design solutions that supports cursor-interrogation to reveal the details of individual concepts. The graph permits study of design progression, or evolution of optimum design solutions. A visualisation tool is also presented. The DPRO system supports multiple operating modes, including single-design appraisal and enumerative search (ES). Case study examples are provided which demonstrate the applicability of the DPRO system to a range of different design scenarios. The DPRO system performs well in all tests. A parametric study demonstrates the potential of the system for DS. Limitations of the current approach and opportunities to broaden the study form part of the scope for further work. Some suggestions for further study are made, based upon newly-emerging techniques

Improved Neighbourhood Search-Based Methods for Graph Layout

Graph drawing, or the automatic layout of graphs, is a challenging problem. There are several search-based methods for graph drawing that are based on optimising a fitness function which is formed from a weighted sum of multiple criteria. This thesis proposes a new neighbourhood search-based method that uses a tabu search coupled with path relinking in order to optimise such fitness functions for general graph layouts with undirected straight lines. None of these methods have been previously used in general multi-criteria graph drawing. Tabu search uses a memory list to speed up searching by avoiding previously tested solutions, while the path relinking method generates new solutions by exploring paths that connect high quality solutions. We use path relinking periodically within the tabu search procedure to speed up the identification of good solutions. We have evaluated our new method against the commonly used neighbourhood search optimisation techniques: hill climbing and simulated annealing. Our evaluation examines the quality of the graph layout (fitness function's value) and the speed of the layout in terms of the number of the evaluated solutions required to draw a graph. We also examine the relative scalability of our method. Our experimental results were applied to both random graphs and a real-world dataset. We show that our method outperforms both hill climbing and simulated annealing by producing a better layout in a lower number of evaluated solutions. In addition, we demonstrate that our method has greater scalability as it can lay out larger graphs than the state-of-the-art neighbourhood search-based methods. Finally, we show that similar results can be produced in a real world setting by testing our method against a standard public graph dataset

User hints for optimisation processes

Innovative improvements in the area of Human-Computer Interaction and User Interfaces have en-abled intuitive and effective applications for a variety of problems. On the other hand, there has also been the realization that several real-world optimization problems still cannot be totally auto-mated. Very often, user interaction is necessary for refining the optimization problem, managing the computational resources available, or validating or adjusting a computer-generated solution. This thesis investigates how humans can help optimization methods to solve such difficult prob-lems. It presents an interactive framework where users play a dynamic and important role by pro-viding hints. Hints are actions that help to insert domain knowledge, to escape from local minima, to reduce the space of solutions to be explored, or to avoid ambiguity when there is more than one optimal solution. Examples of user hints are adjustments of constraints and of an objective function, focusing automatic methods on a subproblem of higher importance, and manual changes of an ex-isting solution. User hints are given in an intuitive way through a graphical interface. Visualization tools are also included in order to inform about the state of the optimization process. We apply the User Hints framework to three combinatorial optimization problems: Graph Clus-tering, Graph Drawing and Map Labeling. Prototype systems are presented and evaluated for each problem. The results of the study indicate that optimization processes can benefit from human interaction. The main goal of this thesis is to list cases where human interaction is helpful, and provide an ar-chitecture for supporting interactive optimization. Our contributions include the general User Hints framework and particular implementations of it for each optimization problem. We also present a general process, with guidelines, for applying our framework to other optimization problems

Metaheuristic approaches for Complete Network Signal Setting Design (CNSSD)

2014 - 2015In order to mitigate the urban traffic congestion and increase the travelers’ surplus, several policies can be adopted which may be applied in short or long time horizon. With regards to the short term policies, one of the most straightforward is control through traffic lights at single junction or network level. The main goal of traffic control is avoiding that incompatible approaches have green at the same time. With respect to this aim existing methodologies for Signal Setting Design (NSSD) can be divided into two classes as in following described Approach-based (or Phase-based) methods address the signal setting as a periodic scheduling problem: the cycle length, and for each approach the start and the end of the green are considered as decision variables, some binary variables (or some non-linear constraints) are included to avoid incompatible approaches having green at the same time (see for instance Improta and Cantarella, 1987). If needed the stage composition and sequence may easily be obtained from decision variables. Commercial software codes following this methodology are available for single junction control only, such Oscady Pro® (TRL, UK; Burrow, 1987). Once the green timing and scheduling have been carried out for each junction, offsets can be optimized (coordination) using the stage matrices obtained from single junction optimization (possibly together with green splits again) through one of codes mentioned below. Stage-based signal setting methods dealt with that by dividing the cycle length into stages, each one being a time interval during which some mutually compatible approaches have green. Stage composition, say which approaches have green, and sequence, say their order, can be represented through the approach-stage incidence matrix, or stage matrix for short. Once the stage matrix is given for each junction, the cycle length, the green splits and the offsets can be optimised (synchronisation) through some well established commercial software codes. Two of the most commonly used codes are: TRANSYT14® (TRL, UK) (recently TRANSYT15® has been released) and TRANSYT-7F® (FHWA, USA). Both allow to compute the green splits, the offsets and the cycle length by combining a traffic flow model and a signal setting optimiser. Both may be used for coordination (optimisation of offsets only, once green splits are known) or synchronisation. TRANSYT14® generates several (but not all) significant stage sequences to be tested but the optimal solution is not endogenously generated, while TRANSYT-7F® is able to optimise the stage sequence for each single junction starting from the ring and barrier NEMA (i.e. National Electrical Manufacturers Association) phases. Still these methods do not allow for stage matrix optimisation; moreover the effects of stage composition and sequence on network performance are not well analysed in literature... [edited by Author]XIV n.s

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