Traversing news with ant colony optimisation and negative pheromones

The past decade has seen the rapid development of the online newsroom. News published online are the main outlet of news surpassing traditional printed newspapers. This poses challenges to the production and to the consumption of those news. With those many sources of information available it is important to find ways to cluster and organise the documents if one wants to understand this new system. Traditional approaches to the problem of clustering documents usually embed the documents in a suitable similarity space. Previous studies have reported on the impact of the similarity measures used for clustering of textual corpora [1]. These similarity measures usually are calculated for bag of words representations of the documents. This makes the final document-word matrix high dimensional. Feature vectors with more than 10,000 dimensions are common and algorithms have severe problems with the high dimensionality of the data. A novel bio inspired approach to the problem of traversing the news is presented. It finds Hamiltonian cycles over documents published by the newspaper The Guardian. A Second Order Swarm Intelligence algorithm based on Ant Colony Optimisation was developed [2, 3] that uses a negative pheromone to mark unrewarding paths with a “no-entry” signal. This approach follows recent findings of negative pheromone usage in real ants [4]. In this case study the corpus of data is represented as a bipartite relation between documents and keywords entered by the journalists to characterise the news. A new similarity measure between documents is presented based on the Q-analysis description [5, 6, 7] of the simplicial complex formed between documents and keywords. The eccentricity between documents (two simplicies) is then used as a novel measure of similarity between documents. The results prove that the Second Order Swarm Intelligence algorithm performs better in benchmark problems of the travelling salesman problem, with faster convergence and optimal results. The addition of the negative pheromone as a non-entry signal clearly improved the quality of the solutions. The application of the algorithm to the corpus of news of The Guardian creates a coherent navigation system among the news. This allows the users to navigate the news published during a certain period of time in a semantic sequence instead of a time sequence. This work as broader application as it can be applied to many cases where the data is mapped to bipartite relations (e.g. protein expressions in cells, sentiment analysis, brand awareness in social media, routing problems), as it highlights the connectivity of the underlying complex system

The multiple pheromone Ant clustering algorithm

Ant Colony Optimisation algorithms mimic the way ants use pheromones for marking paths to important locations. Pheromone traces are followed and reinforced by other ants, but also evaporate over time. As a consequence, optimal paths attract more pheromone, whilst the less useful paths fade away. In the Multiple Pheromone Ant Clustering Algorithm (MPACA), ants detect features of objects represented as nodes within graph space. Each node has one or more ants assigned to each feature. Ants attempt to locate nodes with matching feature values, depositing pheromone traces on the way. This use of multiple pheromone values is a key innovation. Ants record other ant encounters, keeping a record of the features and colony membership of ants. The recorded values determine when ants should combine their features to look for conjunctions and whether they should merge into colonies. This ability to detect and deposit pheromone representative of feature combinations, and the resulting colony formation, renders the algorithm a powerful clustering tool. The MPACA operates as follows: (i) initially each node has ants assigned to each feature; (ii) ants roam the graph space searching for nodes with matching features; (iii) when departing matching nodes, ants deposit pheromones to inform other ants that the path goes to a node with the associated feature values; (iv) ant feature encounters are counted each time an ant arrives at a node; (v) if the feature encounters exceed a threshold value, feature combination occurs; (vi) a similar mechanism is used for colony merging. The model varies from traditional ACO in that: (i) a modified pheromone-driven movement mechanism is used; (ii) ants learn feature combinations and deposit multiple pheromone scents accordingly; (iii) ants merge into colonies, the basis of cluster formation. The MPACA is evaluated over synthetic and real-world datasets and its performance compares favourably with alternative approaches

The Use of Persistent Explorer Artificial Ants to Solve the Car Sequencing Problem

Ant Colony Optimisation is a widely researched meta-heuristic which uses the behaviour and pheromone laying activities of foraging ants to find paths through graphs. Since the early 1990’s this approach has been applied to problems such as the Travelling Salesman Problem, Quadratic Assignment Problem and Car Sequencing Problem to name a few. The ACO is not without its problems it tends to find good local optima and not good global optima. To solve this problem modifications have been made to the original ACO such as the Max Min ant system. Other solutions involve combining it with Evolutionary Algorithms to improve results. These improvements focused on the pheromone structures. Inspired by other swarm intelligence algorithms this work attempts to develop a new type of ant to explore different problem paths and thus improve the algorithm. The exploring ant would persist throughout the running time of the algorithm and explore unused paths. The Car Sequencing problem was chosen as a method to test the Exploring Ants. An existing algorithm was modified to implement the explorers. The results show that for the car sequencing problem the exploring ants did not have any positive impact, as the paths they chose were always sub-optimal

Dynamic pricing services to minimise CO2 emissions of delivery vehicles

In recent years, companies delivering goods or services to customers have been under increasing legal and administrative pressure to reduce the amount of CO2 emissions from their delivery vehicles, while the need to maximise profit remains a prime objective. In this research, we aim to apply revenue management techniques, in particular incentive/dynamic pricing to the traditional vehicle routing and scheduling problem while the objective is to reduce CO2 emissions. With the importance of accurately estimating emissions recognised, emissions models are first reviewed in detail and a new emissions calculator is developed in Java which takes into account time-dependent travel speeds, road distance and vehicle specifications. Our main study is a problem where a company sends engineers with vehicles to customer sites to provide services. Customers request for the service at their preferred time windows and the company needs to allocate the service tasks to time windows and decide on how to schedule these tasks to their vehicles. Incentives are provided to encourage customers choosing low emissions time windows. To help the company in determining the schedules/routes and incentives, our approach solves the problem in two phases. The first phase solves time-dependent vehicle routing/scheduling models with the objective of minimising CO2 emissions and the second phase solves a dynamic pricing model to maximise profit. For the first phase problem, new solution algorithms together with existing ones are applied and compared. For the second phase problem, we consider three different demand modelling scenarios: linear demand model, discrete choice demand model and demand model free pricing strategy. For each of the scenarios, dynamic pricing techniques are implemented and compared with fixed pricing strategies through numerical experiments. Results show that dynamic pricing leads to a reduction in CO2 emissions and an improvement in profits

Individual cognition and collective behaviour in ants

Eusocial insects are impressive on individual and collective level. Collectively, they build nests and efficiently exploit and monopolise nearby resources by sharing information amongst their members. Such collective behaviours are shaped by remarkable and multi-facetted individual abilities and decision-making processes. By revealing and investigating factors which influence individual decision making, this thesis provides a deeper insight into the cognitive lives of ants while contributing to a more cohesive understanding of colony behaviour as a whole. In chapter 2, we demonstrated that foraging ants form expectations about value-neutral qualities of a food source – such as its taste – and dislike food which deviates from those expectations. As such dislike is translated into reduced recruitment, it will have a direct impact on colony-level behaviour. Expectations are thus a potential driver of foraging decisions. Ants excel in navigation, and we showed in chapter 3 that ants can rapidly learn olfactory or spatial cues to localise food sources. Moreover, when those two types of private information where put into conflict, ants exclusively relied on olfaction. This demonstrates that one type of information can entirely dominate decisions in certain situations. While we demonstrated that ants learn simple associations rapidly, nothing was known about their ability to learn abstract relations between stimuli, although concept learning has been reported in honeybees. Thus, in chapter 4, we trained ants to learn a relational rule of same/different, but found that the ants, instead of solving such a complex task via learning, resorted to heuristics such as ‘go left’ or ‘go to the more salient cue’. Intriguingly, the heuristics used varied between individual ants. Learning is dependent on reward and motivation and in chapter 5 we investigated whether those factors alone could explain individual and collective foraging behaviour. While we only found small effects of reward magnitude and motivation on learning, persistence rates varied dramatically in lower motivated colonies. An agent-based model using the empirical data further demonstrated that individual decisions alone can cause ecologically sensible colony-level foraging behaviour. Finally, in chapter 6 we explored whether colonies could make sensible collective decisions in the context of trail-clearing by Australian meat ants. We found that ant colonies preferentially cleared trails toward a food source if the alternative detours were long. The underlying mechanisms seem to be dependent on individual propensities to remove obstacles which leads to the emergence of paths. Taken together, the results of this thesis broaden our understanding of individual cognition in ants and demonstrate that properties inherent to individuals, such as experience or task propensities, systematically influence decisions and thus impact the collective

New Archive-Based Ant Colony Optimization Algorithms for Learning Predictive Rules from Data

Data mining is the process of extracting knowledge and patterns from data. Classification and Regression are among the major data mining tasks, where the goal is to predict a value of an attribute of interest for each data instance, given the values of a set of predictive attributes. Most classification and regression problems involve continuous, ordinal and categorical attributes. Currently Ant Colony Optimization (ACO) algorithms have focused on directly handling categorical attributes only; continuous attributes are transformed using a discretisation procedure in either a preprocessing stage or dynamically during the rule creation. The use of a discretisation procedure has several limitations: (i) it increases the computational runtime, since several candidates values need to evaluated; (ii) requires access to the entire attribute domain, which in some applications all data is not available; (iii) the values used to create discrete intervals are not optimised in combination with the values of other attributes. This thesis investigates the use of solution archive pheromone model, based on Ant Colony Optimization for mixed-variable (ACOMV) algorithm, to directly cope with all attribute types. Firstly, an archive-based ACO classification algorithm is presented, followed by an automatic design framework to generate new configuration of ACO algorithms. Then, we addressed the challenging problem of mining data streams, presenting a new ACO algorithm in combination with a hybrid pheromone model. Finally, the archive-based approach is extended to cope with regression problems. All algorithms presented are compared against well-known algorithms from the literature using publicly available data sets. Our results have been shown to improve the computational time while maintaining a competitive predictive performance

Graph-based Algorithms for Smart Mobility Planning and Large-scale Network Discovery

Graph theory has become a hot topic in the past two decades as evidenced by the increasing number of citations in research. Its applications are found in many fields, e.g. database, clustering, routing, etc. In this thesis, two novel graph-based algorithms are presented. The first algorithm finds itself in the thriving carsharing service, while the second algorithm is about large graph discovery to unearth the unknown graph before any analyses can be performed. In the first scenario, the automatisation of the fleet planning process in carsharing is proposed. The proposed work enhances the accuracy of the planning to the next level by taking an advantage of the open data movement such as street networks, building footprints, and demographic data. By using the street network (based on graph), it solves the questionable aspect in many previous works, feasibility as they tended to use rasterisation to simplify the map, but that comes with the price of accuracy and feasibility. A benchmark suite for further research in this problem is also provided. Along with it, two optimisation models with different sets of objectives and contexts are proposed. Through a series of experiment, a novel hybrid metaheuristic algorithm is proposed. The algorithm is called NGAP, which is based on Reference Point based Non-dominated Sorting genetic Algorithm (NSGA-III) and Pareto Local Search (PLS) and a novel problem specific local search operator designed for the fleet placement problem in carsharing called Extensible Neighbourhood Search (ENS). The designed local search operator exploits the graph structure of the street network and utilises the local knowledge to improve the exploration capability. The results show that the proposed hybrid algorithm outperforms the original NSGA-III in convergence under the same execution time. The work in smart mobility is done on city scale graphs which are considered to be medium size. However, the scale of the graphs in other fields in the real-world can be much larger than that which is why the large graph discovery algorithm is proposed as the second algorithm. To elaborate on the definition of large, some examples are required. The internet graph has over 30 billion nodes. Another one is a human brain network contains around 1011 nodes. Apart of the size, there is another aspect in real-world graph and that is the unknown. With the dynamic nature of the real-world graphs, it is almost impossible to have a complete knowledge of the graph to perform an analysis that is why graph traversal is crucial as the preparation process. I propose a novel memoryless chaos-based graph traversal algorithm called Chaotic Traversal (CHAT). CHAT is the first graph traversal algorithm that utilises the chaotic attractor directly. An experiment with two well-known chaotic attractors, Lozi map and Rössler system is conducted. The proposed algorithm is compared against the memoryless state-of-the-art algorithm, Random Walk. The results demonstrate the superior performance in coverage rate over Random Walk on five tested topologies; ring, small world, random, grid and power-law. In summary, the contribution of this research is twofold. Firstly, it contributes to the research society by introducing new study problems and novel approaches to propel the advance of the current state-of-the-art. And Secondly, it demonstrates a strong case for the conversion of research to the industrial sector to solve a real-world problem

Secure multi-constrained QoS reliable routing algorithm for vehicular ad hoc networks (VANETs)

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonVehicular Ad hoc Networks (VANETs) are a particular form of wireless network made by vehicles communicating among themselves and with roadside base stations. A wide range of services has been developed for VANETs ranging from safety to infotainment applications. A key requirement for such services is that they are offered with Quality of Service (QoS) guarantees in terms of service reliability and availability. Furthermore, due to the openness of VANET’s wireless channels to both internal and external attacks, the application of security mechanisms is mandatory to protect the offered QoS guarantees. QoS routing plays an essential role in identifying routes that meet the QoS requirements of the offered service over VANETs. However, searching for feasible routes subject to multiple QoS constraints is in general an NP-hard problem. Moreover, routing reliability needs to be given special attention as communication links frequently break in VANETs. To date, most existing QoS routing algorithms are designed for stable networks without considering the security of the routing process. Therefore, they are not suitable for applications in VANETs. In this thesis, the above issues are addressed firstly by developing a link reliability model based on the topological and mathematical properties of vehicular movements and velocities. Evolving graph theory is then utilised to model the VANET communication graph and integrate the developed link reliability model into it. Based on the resulting extended evolving graph model, the most reliable route in the network is picked. Secondly, the situational awareness model is applied to the developed reliable routing process because picking the most reliable route does not guarantee reliable transmission. Therefore, a situation-aware reliable multipath routing algorithm for VANETs is proposed. Thirdly, the Ant Colony Optimisation (ACO) technique is employed to propose an Ant-based multi-constrained QoS (AMCQ) routing algorithm for VANETs. AMCQ is designed to give significant advantages to the implementation of security mechanisms that are intended to protect the QoS routing process. Finally, a novel set of security procedures is proposed to defend the routing process against external and internal threats. Simulation results demonstrate that high levels of QoS can be still guaranteed by AMCQ even when the security procedures are applied

Modeling and Simulation of Vehicle Performance in a UAV Swarm Using Horizon Simulation Framework

A UAV swarm is simulated using Horizon Simulation Framework. The asset utilized for the swarm agent is a simplified model of the MQ-1 Predator, a large fixed-wing aircraft. The simulated swarm utilizes a decentralized cooperative control approach to command the assets through the use of digital pheromones and a pheromone map. Each vehicle operates at steady-state flight conditions of 36 m/s with an altitude of 1,800 m, and utilize an LQR set-point controller to maneuver through the pheromone map. All pheromone and aircraft related models are written in Python to expand the HSF scripting capability and include airborne scenarios. The simulation study focuses in the variation of three parameters in the repelling pheromone model. The first two are the update and deposit parameters with values of 2, 10, and 18. The third is the threshold parameter with values of 1e-02, 1e-10, and 1e-18. The lower parameter values provide more time-on-target while the higher parameters allow the swarm to search the surrounding area by only visiting the grid-space once