Optimizing an in Situ Bioremediation Technology to Manage Perchlorate-Contaminated Groundwater

Combining horizontal flow treatment wells (HFTWs) with in situ biodegradation is an innovative approach with the potential to remediate perchlorate-contaminated groundwater. A technology model was recently developed that combines the groundwater flow induced by HFTWs with in situ biodegration processes that result from using the HFTWs to mix electron donor into perchlorate-contaminated groundwater. A field demonstration of this approach is planned to begin this year. In order to apply the technology in the field, project managers need to understand how contaminated site conditions and technology design parameters impact technology performance. One way to gain this understanding is to use the technology model to select engineering design parameters that optimize performance under given site conditions. In particular, a project manager desires to design a system that: 1) maximizes perchlorate destruction; 2) minimizes treatment expense; and 3) attains regulatory limits on down gradient contaminant concentrations. Unfortunately, for a relatively complex technology with a number of engineering design parameters to determine, as well as multiple objectives, system optimization is not straight forward. In this study, a multi-objective genetic algorithm (MOGA) is used to determine design parameter values (flow rate, well spacing, concentration of injection electron donor, and injection schedule) that optimize the first two objectives noted; to maximize perchlorate destruction while minimizing cost. Four optimization runs are performed, using two different remediation time spans (300 and 600 days) for two different sets of site conditions. Results from all four optimization runs indicate that the relationship between perchlorate mass removal and operating cost is positively correlated and nonlinear

SPEA2-based safety system multi-objective optimization

Safety systems are designed to prevent the occurrence of certain conditions and their future development into a hazardous situation. The consequence of the failure of a safety system of a potentially hazardous industrial system or process varies from minor inconvenience and cost to personal injury, significant economic loss and death. To minimise the likelihood of a hazardous situation, safety systems must be designed to maximise their availability. Therefore, the purpose of this thesis is to propose an effective safety system design optimization scheme. A multi-objective genetic algorithm has been adopted, where the criteria catered for includes unavailability, cost, spurious trip and maintenance down time. Analyses of individual system designs are carried out using the latest advantages of the fault tree analysis technique and the binary decision diagram approach (BDD). The improved strength Pareto evolutionary approach (SPEA2) is chosen to perform the system optimization resulting in the final design specifications. The practicality of the developed approach is demonstrated initially through application to a High Integrity Protection System (HIPS) and subsequently to test scalability using the more complex Firewater Deluge System (FDS). Computer code has been developed to carry out the analysis. The results for both systems are compared to those using a single objective optimization approach (GASSOP) and exhaustive search. The overall conclusions show a number of benefits of the SPEA2 based technique application to the safety system design optimization. It is common for safety systems to feature dependency relationships between its components. To enable the use of the fault tree analysis technique and the BDD approach for such systems, the Markov method is incorporated into the optimization process. The main types of dependency which can exist between the safety system component failures are identified. The Markov model generation algorithms are suggested for each type of dependency. The modified optimization tool is tested on the HIPS and FDS. Results comparison shows the benefit of using the modified technique for safety system optimization. Finally the effectiveness and application to general safety systems is discussed

Search-Based Software Maintenance and Testing

2012 - 2013In software engineering there are many expensive tasks that are performed during development and maintenance activities. Therefore, there has been a lot of e ort to try to automate these tasks in order to signi cantly reduce the development and maintenance cost of software, since the automation would require less human resources. One of the most used way to make such an automation is the Search-Based Software Engineering (SBSE), which reformulates traditional software engineering tasks as search problems. In SBSE the set of all candidate solutions to the problem de nes the search space while a tness function di erentiates between candidate solutions providing a guidance to the optimization process. After the reformulation of software engineering tasks as optimization problems, search algorithms are used to solve them. Several search algorithms have been used in literature, such as genetic algorithms, genetic programming, simulated annealing, hill climbing (gradient descent), greedy algorithms, particle swarm and ant colony. This thesis investigates and proposes the usage of search based approaches to reduce the e ort of software maintenance and software testing with particular attention to four main activities: (i) program comprehension; (ii) defect prediction; (iii) test data generation and (iv) test suite optimiza- tion for regression testing. For program comprehension and defect prediction, this thesis provided their rst formulations as optimization problems and then proposed the usage of genetic algorithms to solve them. More precisely, this thesis investigates the peculiarity of source code against textual documents written in natural language and proposes the usage of Genetic Algorithms (GAs) in order to calibrate and assemble IR-techniques for di erent software engineering tasks. This thesis also investigates and proposes the usage of Multi-Objective Genetic Algorithms (MOGAs) in or- der to build multi-objective defect prediction models that allows to identify defect-prone software components by taking into account multiple and practical software engineering criteria. Test data generation and test suite optimization have been extensively investigated as search- based problems in literature . However, despite the huge body of works on search algorithms applied to software testing, both (i) automatic test data generation and (ii) test suite optimization present several limitations and not always produce satisfying results. The success of evolutionary software testing techniques in general, and GAs in particular, depends on several factors. One of these factors is the level of diversity among the individuals in the population, which directly a ects the exploration ability of the search. For example, evolutionary test case generation techniques that employ GAs could be severely a ected by genetic drift, i.e., a loss of diversity between solutions, which lead to a premature convergence of GAs towards some local optima. For these reasons, this thesis investigate the role played by diversity preserving mechanisms on the performance of GAs and proposed a novel diversity mechanism based on Singular Value Decomposition and linear algebra. Then, this mechanism has been integrated within the standard GAs and evaluated for evolutionary test data generation. It has been also integrated within MOGAs and empirically evaluated for regression testing. [edited by author]XII n.s

SPEA2-based safety system multi-objective optimization

Safety systems are designed to prevent the occurrence of certain conditions and their future development into a hazardous situation. The consequence of the failure of a safety system of a potentially hazardous industrial system or process varies from minor inconvenience and cost to personal injury, significant economic loss and death. To minimise the likelihood of a hazardous situation, safety systems must be designed to maximise their availability. Therefore, the purpose of this thesis is to propose an effective safety system design optimization scheme. A multi-objective genetic algorithm has been adopted, where the criteria catered for includes unavailability, cost, spurious trip and maintenance down time. Analyses of individual system designs are carried out using the latest advantages of the fault tree analysis technique and the binary decision diagram approach (BDD). The improved strength Pareto evolutionary approach (SPEA2) is chosen to perform the system optimization resulting in the final design specifications. The practicality of the developed approach is demonstrated initially through application to a High Integrity Protection System (HIPS) and subsequently to test scalability using the more complex Firewater Deluge System (FDS). Computer code has been developed to carry out the analysis. The results for both systems are compared to those using a single objective optimization approach (GASSOP) and exhaustive search. The overall conclusions show a number of benefits of the SPEA2 based technique application to the safety system design optimization. It is common for safety systems to feature dependency relationships between its components. To enable the use of the fault tree analysis technique and the BDD approach for such systems, the Markov method is incorporated into the optimization process. The main types of dependency which can exist between the safety system component failures are identified. The Markov model generation algorithms are suggested for each type of dependency. The modified optimization tool is tested on the HIPS and FDS. Results comparison shows the benefit of using the modified technique for safety system optimization. Finally the effectiveness and application to general safety systems is discussed.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

SPEA2-based safety system multi-objective optimization

Safety systems are designed to prevent the occurrence of certain conditions and their future development into a hazardous situation. The consequence of the failure of a safety system of a potentially hazardous industrial system or process varies from minor inconvenience and cost to personal injury, significant economic loss and death. To minimise the likelihood of a hazardous situation, safety systems must be designed to maximise their availability. Therefore, the purpose of this thesis is to propose an effective safety system design optimization scheme. A multi-objective genetic algorithm has been adopted, where the criteria catered for includes unavailability, cost, spurious trip and maintenance down time. Analyses of individual system designs are carried out using the latest advantages of the fault tree analysis technique and the binary decision diagram approach (BDD). The improved strength Pareto evolutionary approach (SPEA2) is chosen to perform the system optimization resulting in the final design specifications. The practicality of the developed approach is demonstrated initially through application to a High Integrity Protection System (HIPS) and subsequently to test scalability using the more complex Firewater Deluge System (FDS). Computer code has been developed to carry out the analysis. The results for both systems are compared to those using a single objective optimization approach (GASSOP) and exhaustive search. The overall conclusions show a number of benefits of the SPEA2 based technique application to the safety system design optimization. It is common for safety systems to feature dependency relationships between its components. To enable the use of the fault tree analysis technique and the BDD approach for such systems, the Markov method is incorporated into the optimization process. The main types of dependency which can exist between the safety system component failures are identified. The Markov model generation algorithms are suggested for each type of dependency. The modified optimization tool is tested on the HIPS and FDS. Results comparison shows the benefit of using the modified technique for safety system optimization. Finally the effectiveness and application to general safety systems is discussed.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

MFIRE-2: A Multi Agent System for Flow-based Intrusion Detection Using Stochastic Search

Detecting attacks targeted against military and commercial computer networks is a crucial element in the domain of cyberwarfare. The traditional method of signature-based intrusion detection is a primary mechanism to alert administrators to malicious activity. However, signature-based methods are not capable of detecting new or novel attacks. This research continues the development of a novel simulated, multiagent, flow-based intrusion detection system called MFIRE. Agents in the network are trained to recognize common attacks, and they share data with other agents to improve the overall effectiveness of the system. A Support Vector Machine (SVM) is the primary classifier with which agents determine an attack is occurring. Agents are prompted to move to different locations within the network to find better vantage points, and two methods for achieving this are developed. One uses a centralized reputation-based model, and the other uses a decentralized model optimized with stochastic search. The latter is tested for basic functionality. The reputation model is extensively tested in two configurations and results show that it is significantly superior to a system with non-moving agents. The resulting system, MFIRE-2, demonstrates exciting new network defense capabilities, and should be considered for implementation in future cyberwarfare applications

Optimization of Airfield Parking and Fuel Asset Dispersal to Maximize Survivability and Mission Capability Level

While the US focus for the majority of the past two decades has been on combatting insurgency and promoting stability in Southwest Asia, strategic focus is beginning to shift toward concerns of conflict with a near-peer state. Such conflict brings with it the risk of ballistic missile attack on air bases. With 26 conflicts worldwide in the past 100 years including attacks on air bases, new doctrine and modeling capacity are needed to enable the Department of Defense to continue use of vulnerable bases during conflict involving ballistic missiles. Several models have been developed to date for Air Force strategic planning use, but these models have limited use on a tactical level or for civil engineer use. This thesis presents the development of a novel model capable of identifying base layout characteristics for aprons and fuel depots to maximize dispersal and minimize impact on sortie generation times during normal operations. This model is implemented using multi-objective genetic algorithms to identify solutions that provide optimal tradeoffs between competing objectives and is assessed using an application example. These capabilities are expected to assist military engineers in the layout of parking plans and fuel depots that ensure maximum resilience while providing minimal impact to the user while enabling continued sortie generation in a contested region

Navigation Constellation Design Using a Multi-Objective Genetic Algorithm

In satellite constellation design, performance and cost of the system drive the design process. The Global Positioning System (GPS) constellation is currently used to provide positioning and timing worldwide. As satellite technology has improved over the years, the cost to develop and maintain the satellites has increased. Using a constellation design tool, it is possible to analyze the tradeoffs of new navigation constellation designs (Pareto fronts) that illustrate the tradeoffs between position dilution of precision (PDOP) and system cost. This thesis utilized Satellite Tool Kit (STK) to calculate PDOP values of navigation constellations, and the Unmanned Spacecraft Cost Model (USCM) along with the Small Spacecraft Cost Model (SSCM) to determine system cost. The design parameters used include Walker constellation parameters, orbital elements, and transmit power. The results show that the constellation design tool produces realistic solutions. Using the generated solutions, an analysis of the navigation constellation designs was presented

A Multi Agent System for Flow-Based Intrusion Detection Using Reputation and Evolutionary Computation

The rising sophistication of cyber threats as well as the improvement of physical computer network properties present increasing challenges to contemporary Intrusion Detection (ID) techniques. To respond to these challenges, a multi agent system (MAS) coupled with flow-based ID techniques may effectively complement traditional ID systems. This paper develops: 1) a scalable software architecture for a new, self-organized, multi agent, flow-based ID system; and 2) a network simulation environment suitable for evaluating implementations of this MAS architecture and for other research purposes. Self-organization is achieved via 1) a reputation system that influences agent mobility in the search for effective vantage points in the network; and 2) multi objective evolutionary algorithms that seek effective operational parameter values. This paper illustrates, through quantitative and qualitative evaluation, 1) the conditions for which the reputation system provides a significant benefit; and 2) essential functionality of a complex network simulation environment supporting a broad range of malicious activity scenarios. These results establish an optimistic outlook for further research in flow-based multi agent systems for ID in computer networks

Static and dynamic overproduction and selection of classifier ensembles with genetic algorithms

The overproduce-and-choose sttategy is a static classifier ensemble selection approach, which is divided into overproduction and selection phases. This thesis focuses on the selection phase, which is the challenge in overproduce-and-choose strategy. When this phase is implemented as an optimization process, the search criterion and the search algorithm are the two major topics involved. In this thesis, we concentrate in optimization processes conducted using genetic algorithms guided by both single- and multi-objective functions. We first focus on finding the best search criterion. Various search criteria are investigated, such as diversity, the error rate and ensemble size. Error rate and diversity measures are directly compared in the single-objective optimization approach. Diversity measures are combined with the error rate and with ensemble size, in pairs of objective functions, to guide the multi-optimization approach. Experimental results are presented and discussed. Thereafter, we show that besides focusing on the characteristics of the decision profiles of ensemble members, the control of overfitting at the selection phase of overproduce-and-choose strategy must also be taken into account. We show how overfitting can be detected at the selection phase and present three strategies to control overfitting. These strategies are tailored for the classifier ensemble selection problcm and compared. This comparison allows us to show that a global validation strategy should be applied to control overfitting in optimization processes involving a classifier ensembles selection task. Furthermore, this study has helped us establish that this global validation strategy can be used as a tool to measure the relationship between diversity and classification performance when diversity measures are employed as single-objective functions. Finally, the main contribution of this thesis is a proposed dynamic overproduce-and-choose strategy. While the static overproduce-and-choose selection strategy has traditionally focused on finding the most accurate subset of classifiers during the selection phase, and using it to predict the class of all the test samples, our dynamic overproduce-and- choose strategy allows the selection of the most confident subset of classifiers to label each test sample individually. Our method combines optimization and dynamic selection in a two-level selection phase. The optimization level is intended to generate a population of highly accurate classifier ensembles, while the dynamic selection level applies measures of confidence in order to select the ensemble with the highest degree of confidence in the current decision. Three different confidence measures are presented and compared. Our method outperforms classical static and dynamic selection strategies