Application of a new multi-agent Hybrid Co-evolution based Particle Swarm Optimisation methodology in ship design

In this paper, a multiple objective 'Hybrid Co-evolution based Particle Swarm Optimisation' methodology (HCPSO) is proposed. This methodology is able to handle multiple objective optimisation problems in the area of ship design, where the simultaneous optimisation of several conflicting objectives is considered. The proposed method is a hybrid technique that merges the features of co-evolution and Nash equilibrium with a ε-disturbance technique to eliminate the stagnation. The method also offers a way to identify an efficient set of Pareto (conflicting) designs and to select a preferred solution amongst these designs. The combination of co-evolution approach and Nash-optima contributes to HCPSO by utilising faster search and evolution characteristics. The design search is performed within a multi-agent design framework to facilitate distributed synchronous cooperation. The most widely used test functions from the formal literature of multiple objectives optimisation are utilised to test the HCPSO. In addition, a real case study, the internal subdivision problem of a ROPAX vessel, is provided to exemplify the applicability of the developed method

An Empirical Study on Android-related Vulnerabilities

Mobile devices are used more and more in everyday life. They are our cameras, wallets, and keys. Basically, they embed most of our private information in our pocket. For this and other reasons, mobile devices, and in particular the software that runs on them, are considered first-class citizens in the software-vulnerabilities landscape. Several studies investigated the software-vulnerabilities phenomenon in the context of mobile apps and, more in general, mobile devices. Most of these studies focused on vulnerabilities that could affect mobile apps, while just few investigated vulnerabilities affecting the underlying platform on which mobile apps run: the Operating System (OS). Also, these studies have been run on a very limited set of vulnerabilities. In this paper we present the largest study at date investigating Android-related vulnerabilities, with a specific focus on the ones affecting the Android OS. In particular, we (i) define a detailed taxonomy of the types of Android-related vulnerability; (ii) investigate the layers and subsystems from the Android OS affected by vulnerabilities; and (iii) study the survivability of vulnerabilities (i.e., the number of days between the vulnerability introduction and its fixing). Our findings could help OS and apps developers in focusing their verification & validation activities, and researchers in building vulnerability detection tools tailored for the mobile world

Optimizing Engagement Simulations Through the Advanced Framework for Simulation, Integration, and Modeling (AFSIM) Software

The ability to effectively model and simulate military missions holds the potential to save lives, money, and resources for the United States. The Advanced Framework for Simulation, Integration, and Modeling (AFSIM) software is a tool used to rapidly simulate and model new technologies and mission level scenarios. In this thesis, our objective is to integrate a closed loop optimization routine with AFSIM to identify an effective objective function to assess optimal inputs for engagement scenarios. Given the many factors which impact a mission level engagement, we developed a tool which interfaces with AFSIM to observe the effects from multiple inputs in an engagement scenario. Our tool operates under the assumption that simulation results have met an acceptable convergence threshold. The objective function evaluates the effectiveness and associated cost with a scenario using a genetic algorithm and a particle swarm optimization algorithm. From this, a statistical analysis was performed to assess risk from the distribution of effectiveness and cost at each point. The method allows an optimal set of inputs to be selected for a desired result from the selected engagement scenario.No embargoAcademic Major: Mechanical Engineerin

Enhancing Fault / Intrusion Tolerance through Design and Configuration Diversity

Fault/intrusion tolerance is usually the only viable way of improving the system dependability and security in the presence of continuously evolving threats. Many of the solutions in the literature concern a specific snapshot in the production or deployment of a fault-tolerant system and no immediate considerations are made about how the system should evolve to deal with novel threats. In this paper we outline and evaluate a set of operating systems’ and applications’ reconfiguration rules which can be used to modify the state of a system replica prior to deployment or in between recoveries, and hence increase the replicas chance of a longer intrusion-free operation

Multi-criteria Evolution of Neural Network Topologies: Balancing Experience and Performance in Autonomous Systems

Majority of Artificial Neural Network (ANN) implementations in autonomous systems use a fixed/user-prescribed network topology, leading to sub-optimal performance and low portability. The existing neuro-evolution of augmenting topology or NEAT paradigm offers a powerful alternative by allowing the network topology and the connection weights to be simultaneously optimized through an evolutionary process. However, most NEAT implementations allow the consideration of only a single objective. There also persists the question of how to tractably introduce topological diversification that mitigates overfitting to training scenarios. To address these gaps, this paper develops a multi-objective neuro-evolution algorithm. While adopting the basic elements of NEAT, important modifications are made to the selection, speciation, and mutation processes. With the backdrop of small-robot path-planning applications, an experience-gain criterion is derived to encapsulate the amount of diverse local environment encountered by the system. This criterion facilitates the evolution of genes that support exploration, thereby seeking to generalize from a smaller set of mission scenarios than possible with performance maximization alone. The effectiveness of the single-objective (optimizing performance) and the multi-objective (optimizing performance and experience-gain) neuro-evolution approaches are evaluated on two different small-robot cases, with ANNs obtained by the multi-objective optimization observed to provide superior performance in unseen scenarios

Fault diversity among off-the-shelf SQL database servers

Fault tolerance is often the only viable way of obtaining the required system dependability from systems built out of "off-the-shelf" (OTS) products. We have studied a sample of bug reports from four off-the-shelf SQL servers so as to estimate the possible advantages of software fault tolerance - in the form of modular redundancy with diversity - in complex off-the-shelf software. We checked whether these bugs would cause coincident failures in more than one of the servers. We found that very few bugs affected two of the four servers, and none caused failures in more than two. We also found that only four of these bugs would cause identical, undetectable failures in two servers. Therefore, a fault-tolerant server, built with diverse off-the-shelf servers, seems to have a good chance of delivering improvements in availability and failure rates compared with the individual off-the-shelf servers or their replicated, nondiverse configurations