Daniel Angus

Multiple objective ant colony optimisation

Multiple Objective Optimisation is a fast growing area of research, and consequently several Ant Colony Optimisation approaches have been proposed for a variety of these problems. In this paper, a taxonomy for Multiple Objective Ant Colony Optimisation algorithms is proposed and many existing approaches are reviewed and described using the taxonomy. The taxonomy offers guidelines for the development and use of Multiple Objective Ant Colony Optimisation algorithms.

Making sense of big text: a visual-first approach for analysing text data using Leximancer and Discursis

This article reports on Leximancer and Discursis, two visual text analytic software tools developed at the University of Queensland. Both analyse spatial and temporal relationships in text data, but in complementary ways: Leximancer focuses on thematic analysis, while Discursis focuses on sequential analysis. Our report explains how they work, how to work with them and how visual concepts are relevant to all stages of their use in analytic decision-making.

Crowding Population-based Ant Colony Optimisation for the Multi-objective Travelling Salesman Problem

Ant inspired algorithms have gained popularity for use in multi-objective problem domains. One specific algorithm, Population-based ACO, which uses a population as well as the traditional pheromone matrix, has been shown to be effective at solving combinatorial multi-objective optimisation problems. This paper extends the population-based ACO algorithm with a crowding population replacement scheme to increase the search efficacy and efficiency. Results are shown for a suite of multi-objective travelling salesman problems of varying complexity

Conceptual Recurrence Plots: Revealing Patterns in Human Discourse

Human discourse contains a rich mixture of conceptual information. Visualization of the global and local patterns within this data stream is a complex and challenging problem. Recurrence plots are an information visualization technique that can reveal trends and features in complex time series data. The recurrence plot technique works by measuring the similarity of points in a time series to all other points in the same time series and plotting the results in two dimensions. Previous studies have applied recurrence plotting techniques to textual data; however, these approaches plot recurrence using term-based similarity rather than conceptual similarity of the text. We introduce conceptual recurrence plots, which use a model of language to measure similarity between pairs of text utterances, and the similarity of all utterances is measured and displayed. In this paper, we explore how the descriptive power of the recurrence plotting technique can be used to discover patterns of interaction across a series of conversation transcripts. The results suggest that the conceptual recurrence plotting technique is a useful tool for exploring the structure of human discourse.

Visualising conversation structure across time: insights into effective doctor-patient consultations.

Effective communication between healthcare professionals and patients is critical to patients’ health outcomes. The doctor/patient dialogue has been extensively researched from different perspectives, with findings emphasising a range of behaviours that lead to effective communication. Much research involves self-reports, however, so that behavioural engagement cannot be disentangled from patients’ ratings of effectiveness. In this study we used a highly efficient and time economic automated computer visualisation measurement technique called Discursis to analyse conversational behaviour in consultations. Discursis automatically builds an internal language model from a transcript, mines the transcript for its conceptual content, and generates an interactive visual account of the discourse. The resultant visual account of the whole consultation can be analysed for patterns of engagement between interactants. The findings from this study show that Discursis is effective at highlighting a range of consultation techniques, including communication accommodation, engagement and repetition.

Human Communication as Coupled Time Series: Quantifying Multi-Participant Recurrence

Human communication is more than just the transmission of information. It also involves complex interaction dynamics that reflect the roles and communication styles of the participants. A novel approach to studying human communication is to view conversation as a coupled time series and apply analysis techniques from dynamical systems to the recurring topics or concepts. In this paper, we define a set of metrics that enable quantification of the complex interaction dynamics visible in conceptual recurrence. These multi-participant recurrence (MPR) metrics can be seen as an extension of recurrence quantification analysis (RQA) into the symbolic domain. This technique can be used to monitor the state of a communication system and inform about interaction dynamics, including the level of topic consistency between participants; the timing of state changes for the participants as a result of changes in topic focus; and, patterns of topic proposal, reflection, and repetition. We demonstrate three use studies applying the new metrics to conversation transcripts from different genres to demonstrate their ability to characterize individual communication participants and intergroup communication patterns.

Dynamic Ant Colony Optimisation

Ant Colony optimisation has proved suitable to solve static optimisation problems, that is problems that do not change with time. However in the real world changing circumstances may mean that a previously optimum solution becomes suboptimal. This paper explores the ability of the ant colony optimisation algorithm to adapt from the optimum solution for one set of circumstances to the optimal solution for another set of circumstances. Results are given for a preliminary investigation based on the classical travelling salesman problem. It is concluded that, for this problem at least, the time taken for the solution adaption process is far shorter than the time taken to find the second optimum solution if the whole process is started over from scratch.

Niching for Population-Based Ant Colony Optimization

Most Ant Colony Optimization (ACO) algorithms are able to find a single (or few) optimal, or near-optimal, solutions to difficult (NP-hard) problems. An issue though is that a small change to the problem can have a large impact on a specific solution by decreasing its quality, or worse still, by rendering it infeasible. Niching methods, such as fitness sharing and crowding, have been implemented with success in the field of Evolutionary Computation (EC) and are aimed at simultaneously locating and maintaining multiple optima to increase search robustness - typically in multi-modal function optimization. In this paper it is shown that a niching technique applied to an ACO algorithm permits the simultaneous location and maintenance of multiple areas of interest in the search space.

Population-based ant colony optimisation for multi-objective function optimisation

Ant inspired algorithms have recently gained popularity for use in multi-objective problem domains. The Population-based ACO, which uses a population of solutions as well as the traditional pheromone matrix, has been demonstrated as an effective problem solving strategy for solving combinatorial multi-objective optimisation problems, although this algorithm has yet to be applied to multi-objective function optimisation problems. This paper tests the suitability of a Population-based ACO algorithm for the multi-objective function optimisation problem. Results are given for a suite of problems of varying complexity.

Ant Colony Optimisation Applied to a Dynamically Changing Problem

Ant Colony optimisation has proved suitable to solve static optimisation problems, that is problems that do not change with time. However in the real world changing circumstances may mean that a previously optimum solution becomes suboptimial. This paper explores the ability of the ant colony optimisation algorithm to adapt from the optimum solution to one set of circumstances to the optimal solution to another set of circumstances. Results are given for a preliminary investigation based on the classical travelling salesperson problem. It is concluded that, for this problem at least, the time taken for the solution adaption process is far shorter than the time taken to find the second optimum solution if the whole process is started over from scratch.