The ill-effects of stress due to fatigue significantly impact the welfare of individuals and consequently impact overall corporate productivity. This study introduces a simplified model of stress in the workplace using agent-based simulation. This study represents a novel contribution to the field of evolutionary computation. Agents are encoded initially using a String Representation and later expanded to multi-state Binary Decision Automata to choose between work on a base task, special project or rest. Training occurs by agents inaccurately mimicking behaviour of highly productive mentors. Stress is accumulated through working long hours thereby decreasing productivity performance of an agent. Lowest productivity agents are fired or retrained. The String representation for agents demonstrated near average performance attributed to the normally distributed tasks assigned to the string. The BDA representation was found to be highly adaptive, responding robustly to parameter changes. By reducing the number of simplifications for the model, a more accurate representation of the real world can be achieved.

ABSTRACT Voltage instability has become a major concern in power systems. Many blackouts have been reported where the main cause is voltage instability. This thesis deals with two specific areas of voltage stability in on-line power system security assessments: small-disturbance (long-term) and large-disturbance (short-term) voltage stability assessment. For each category of voltage stability, both voltage stability analysis and controls are studied. The overall objective is to use the learning capabilities of computational intelligence technology to build up the comprehensive on-line power system security assessment and control strategy as well as to enhance the speed and efficiency of the process with minimal human intervention. The voltage stability problems are quantified by voltage stability indices which measure the system for the closeness of current operating point to voltage instability. The indices are different for small-disturbance and large-disturbance voltage stability assessment. Conventional approaches, such as continuation power flow or time-domain simulation, can be used to obtain voltage stability indices. However, these conventional approaches are limited by computation time that is significant for on-line computation. The Artificial Neural Network (ANN) approach is proposed to compute voltage stability indices as an alternative to the conventional approaches. The proposed ANN algorithm is used to estimate voltage stability indices under both normal and contingency operating conditions. The input variables of ANN are obtained in real-time by an on-line measurement system, i.e. Phasor Measurement Units (PMU). This thesis will propose a suboptimal approach for seeking the best locations for PMUs from a voltage stability viewpoint. The ANN-based method is not limited to compute voltage stability indices but can also be extended to determine suitable control actions. Load shedding is one of the most effective approaches against short-term voltage instability under large disturbances. The basic requirement of load shedding for recovering voltage stability is to seek an optimal solution for when, where, and how much load should be shed. Two simulation based approaches, particle swarm optimization (PSO) algorithm and sensitivity based algorithm, are proposed for load shedding to prevent voltage instability or collapse. Both approaches are based on time-domain simulation.

ABSTRACT Voltage instability has become a major concern in power systems. Many blackouts have been reported where the main cause is voltage instability. This thesis deals with two specific areas of voltage stability in on-line power system security assessments: small-disturbance (long-term) and large-disturbance (short-term) voltage stability assessment. For each category of voltage stability, both voltage stability analysis and controls are studied. The overall objective is to use the learning capabilities of computational intelligence technology to build up the comprehensive on-line power system security assessment and control strategy as well as to enhance the speed and efficiency of the process with minimal human intervention. The voltage stability problems are quantified by voltage stability indices which measure the system for the closeness of current operating point to voltage instability. The indices are different for small-disturbance and large-disturbance voltage stability assessment. Conventional approaches, such as continuation power flow or time-domain simulation, can be used to obtain voltage stability indices. However, these conventional approaches are limited by computation time that is significant for on-line computation. The Artificial Neural Network (ANN) approach is proposed to compute voltage stability indices as an alternative to the conventional approaches. The proposed ANN algorithm is used to estimate voltage stability indices under both normal and contingency operating conditions. The input variables of ANN are obtained in real-time by an on-line measurement system, i.e. Phasor Measurement Units (PMU). This thesis will propose a suboptimal approach for seeking the best locations for PMUs from a voltage stability viewpoint. The ANN-based method is not limited to compute voltage stability indices but can also be extended to determine suitable control actions. Load shedding is one of the most effective approaches against short-term voltage instability under large disturbances. The basic requirement of load shedding for recovering voltage stability is to seek an optimal solution for when, where, and how much load should be shed. Two simulation based approaches, particle swarm optimization (PSO) algorithm and sensitivity based algorithm, are proposed for load shedding to prevent voltage instability or collapse. Both approaches are based on time-domain simulation.

Thesis (M.Sc.)(Computer Science)--University of Pretoria, 2006. / Includes summary. Includes bibliographical references (leaves 151-152). Available on the Internet via the World Wide Web.

Thesis (M.Sc.)--Victoria University of Wellington, 2008. / Includes bibliographical references.

Thesis (Ph. D.)--University of Washington, 2002. / Vita. Includes bibliographical references (leaves 107-115).

Thesis (Ph.D.) - Carleton University, 2007. / Includes bibliographical references (p. 257-265). Also available in electronic format on the Internet.

Thesis (PhD) - Swinburne University of Technology, 2008. / A dissertation presented for fulfillment of the requirements for the degree of Doctor of Philosophy, Swinburne University of Technology - 2008. Typescript. Bibliography: p. 349-377.

M.Sc. (Computer Science) / The purpose of the research presented in this dissertation is to explore the use of a distributed multi-agent system in a general purpose crowd simulation model. Crowd simulation is becoming an increasingly important tool for analysing new construction projects, as it enables safety and performance evaluations to be performed on architectural plans before the buildings have been constructed. Crowd simulation is a challenging problem, as it requires the simulation of complex interactions of people within a crowd. The dissertation investigates existing models of crowd simulation and identifies three primary sub-tasks of crowd simulation: deliberation, path planning and collision-avoiding movement. Deliberation is the process of determining which goal an agent will attempt to satisfy next. Path planning is the process of finding a collision-free path from an agent‟s current location towards its goal. Collision-avoiding movement deals with moving an agent along its calculated path while avoiding collisions with other agents. A multi-agent crowd simulation model, DiMACS, is proposed as a means of addressing the problem of crowd simulation. Multi-agent technology provides an effective solution for representing individuals within a crowd; each member of a crowd can be represented as an intelligent agent. Intelligent agents are capable of maintaining their own internal state and deciding on a course of action based on that internal state. DiMACS is capable of producing realistic simulations while making use of distributed and parallel processing to improve its performance. In addition, the model is highly customisable. The dissertation also presents a user-friendly method for configuring agents within a simulation that abstracts the complexity of agent behaviour away from a user so as to increase the accessibility of configuring the proposed model. In addition, an application programming interface is provided that enables developers to extend the model to simulate additional agent behaviours. The research shows how distributed and parallel processing may be used to improve the performance of an agent-based crowd simulation without compromising the accuracy of the simulation.

M.Sc. (Computer Science) / This dissertation presents a method for learning from existing building designs and generating new building layouts. Generating fully furnished building layouts could be very useful for video games or for assisting architects when designing new buildings. The core concern is to drastically reduce the workload required to design building layouts. The implemented prototype features a Computer Aided Design system, named CABuilD that allows users to design fully furnished multi-storey building layouts. Building layouts designed using CABuilD can be taught to an Artificial Immune System. The Artificial Immune System tracks information such as building layouts, room sizes and furniture layouts. Once building layouts has been taught to the artificial immune system, a generation algorithm can utilise the information in order to generate fully furnished building layouts. The generation algorithm that is presented allows fully furnished buildings to be generated from high-level information such as the number of rooms to include and a building perimeter. The presented algorithm differs from existing building generation methods in the following ways: Firstly existing methods either ignore building perimeters or assume a buildings perimeter is a rectangle. The presented method allows the user to specify a closed polygon as a building perimeter which will guide the generation of the building layout. Secondly existing generation methods tend to run from a set of rules. The implemented system learns from existing building layouts, effectively allowing it to generate different building types based on the building layouts that were taught to the system. Thirdly, the system generates both the building layout as well as the furniture within rooms. Existing systems only generate the building layout or the furniture, but not both. The prototype that was implemented as a proof of concept uses a number of biologically inspired techniques such as Ant algorithms, Particle Swarm Optimisation and Artificial Immune Systems. The system also employs multiple intelligent agents in order to furnished rooms. The prototype is capable of generating furnished building layouts in merely a few seconds, much faster than a human could design such a layout. Possible improvements and future work is presented at the end of the dissertation.