The Effects of Ant Colony Optimization on Graph Anonymization

The growing need to address privacy concerns whensocial network data is released for mining purposes hasrecently led to considerable interest in varioustechniques for graph anonymization. These techniquesand definitions, although robust are sometimes difficultto achieve for large social net-works. In this paper, welook at applying ant colony opti-mization (ACO) to twoknown versions of social network anonymization,namely k-label sequence anonymity, known to be NPhardfor k ≥ 3. We also apply it to the more recent workof [23] and Label Bag Anonymization. Ants of the artificialcolony are able to generate successively shortertours by using information accumulated in the form ofpheromone trails deposited by the edge colonies ant.Computer simu-lations have indicated that ACO arecapable of generating good solutions for known hardergraph problems.The contributions of this paper are two fold: welook to apply ACO to k-label sequence anonymity andk=label bag based anonymization, and attempt to showthe power of ap-plying ACO techniques to socialnetwork privacy attempts. Furthermore, we look tobuild a new novel foundation of study, that althoughat its preliminary stages, can lead it ground breakingresults down the road

Ant-inspired Interaction Networks For Decentralized Vehicular Traffic Congestion Control

Mimicking the autonomous behaviors of animals and their adaptability to changing or foreign environments lead to the development of swarm intelligence techniques such as ant colony optimization (ACO) and particle swarm optimization (PSO) now widely used to tackle a variety of optimization problems. The aim of this dissertation is to develop an alternative swarm intelligence model geared toward decentralized congestion avoidance and to determine qualities of the model suitable for use in a transportation network. A microscopic multi-agent interaction network inspired by insect foraging behaviors, especially ants, was developed and consequently adapted to prioritize the avoidance of congestion, evaluated as perceived density of other agents in the immediate environment extrapolated from the occurrence of direct interactions between agents, while foraging for food outside the base/nest. The agents eschew pheromone trails or other forms of stigmergic communication in favor of these direct interactions whose rate is the primary motivator for the agents\u27 decision making process. The decision making process at the core of the multi-agent interaction network is consequently transferred to transportation networks utilizing vehicular ad-hoc networks (VANETs) for communication between vehicles. Direct interactions are replaced by dedicated short range communications for wireless access in vehicular environments (DSRC/WAVE) messages used for a variety of applications like left turn assist, intersection collision avoidance, or cooperative adaptive cruise control. Each vehicle correlates the traffic on the wireless network with congestion in the transportation network and consequently decides whether to reroute and, if so, what alternate route to take in a decentralized, non-deterministic manner. The algorithm has been shown to increase throughput and decrease mean travel times significantly while not requiring access to centralized infrastructure or up-to-date traffic information

Adaptacyjny algorytm optymalizacji stadnej cząsteczek dla dynamicznego problemu komiwojażera

The main assumption of the dissertation is the application of pheromone memory in the discrete version of the PSO algorithm (Discrete Particle Swarm Optimization) with a view to adjusting it to solving the DTSP - DTSP (Dynamic Traveling Salesman Problem). The Traveling Salesman Problem has not only a theoretical (many combinatorial problems can be reduced to the TSP problem), but also a practical meaning - especially in transport, from which it originated. The reasons behind the creation of the dynamic version of the problem are practical. What can happen very often in the road is traffic congestion, as a result of which the route is longer. The distance between vertices may refer not only to the distance but also, e.g. time or also incurred cost. Owing to that the scope of applications of the static and dynamic TSP is significantly wider. In this dissertation the Dynamic Traveling Salesman Problem was defined as a sequence of consecutive static Traveling Salesman Problems (sub-problems). The difference between one another lies in some percent of changes in the distance matrix. Despite the substantial number of works dedicated to both the static and dynamic problem, many questions still remain unanswered. These especially concern the dynamic version of TSP. The following two areas are explored in this dissertation: • theoretical and practical analysis of the Traveling Salesman Problem, as well as its dynamic version, • overview of literature connected with the computational intelligence and the most important concepts related to this field of science, among others synergy or cooperation, • description of selected computational intelligence algorithms together with explanation of how they work. The subsequent chapters include: • description of how the version of the Particle Swarm Optimization Algorithm with pheromone suggested in the dissertation works, as well as the means of adjusting it to the discussed problem, • analysis of the influence of the values of parameters of the prepared solutions on the quality of the achieved results, • description of the self-adaptive (heterogenic) version of the DPSO algorithm, • assessment of the usefulness of knowledge regarding the solution to the previous sub-problem, in order to accelerate the convergence of the DPSO algorithm for the new sub-problem in the solved DTSP problem, • comparison, by the means of static tests, of the hybrid DPSO algorithm with pheromone with the selected computational intelligence algorithms: ACO (Ant Colony Optimization) and PACO (Population Ant Colony Optimization). The tool suggested in the dissertation makes use of a limited list of neighborhoods for every vertex. This procedure reduces the overview of solution space and hence improves the rate of algorithm convergence. It has some disadvantages, e.g. the probability of finding the solution decreases in case of a lack of an edge that would belong to the optimum solution in the vertex neighborhood. Very effective Helsgaun's neighborhood was applied in the dissertation. Substantial attention was also given to examination of the influence of various parameter values on the functioning of the DPSO algorithm with pheromone, which was the purpose of creating a heterogeneous algorithm. In algorithm every particle can have different parameter values. However, their complete randomness may lead to chaotic solution space searching. Therefore, in order to prevent that a proper distribution of similarities of selection of given parameter values was chosen. It was preceded by an analysis of characteristic values of the DPSO algorithm with pheromone. The diversity of parameter values improved the quality of the obtained results. However, the main reason behind creating the heterogeneous version was the reduction of the number of algorithm parameters. The final parameters were restricted to the number of iterations, size of swarm and size of neighborhood. These are parameters, the values of which should be defined on the basis of the size of the (n) problem and the available computational budget, since the first two parameters influence the computational time. The third parameter influences the degree of exploration and exploitation of the solution space. The thesis of the dissertation „The Application of Pheromone Memory and Heterogeneity in the Discrete PSO Algorithm for the Dynamic Traveling Salesman Problem” Makes it Possible to Improve the Quality of the Obtained Results was proved on the basis of the results of computational experiments subjected to statistical analysis

The germinal centre artificial immune system

This thesis deals with the development and evaluation of the Germinal centre artificial immune system (GC-AIS) which is a novel artificial immune system based on advancements in the understanding of the germinal centre reaction of the immune system. The key research questions addressed in this thesis are: can an artificial immune system (AIS) be designed by taking inspiration from recent developments in immunology to tackle multi-objective optimisation problems? How can we incorporate desirable features of the immune system like diversity, parallelism and memory into this proposed AIS? How does the proposed AIS compare with other state of the art techniques in the field of multi-objective optimisation problems? How can we incorporate the learning component of the immune system into the algorithm and investigate the usefulness of memory in dynamic scenarios? The main contributions of the thesis are: • Understanding the behaviour and performance of the proposed GC-AIS on multiobjective optimisation problems and explaining its benefits and drawbacks, by comparing it with simple baseline and state of the art algorithms. • Improving the performance of GC-AIS by incorporating a popular technique from multi-objective optimisation. By overcoming its weaknesses the capability of the improved variant to compete with the state of the art algorithms is evaluated. • Answering key questions on the usefulness of incorporating memory in GC-AIS in a dynamic scenario