Feili Yu

Integration of a Holonic Organizational Control Architecture and Multiobjective Evolutionary Algorithm for Flexible Distributed Scheduling

Based on the concept of autonomous cooperating holons, this paper presents a holonic command and control ( C 2) organizational control architecture (OCA) that models a C 2 organization as an integration of holonic multilevel decentralized decision-making networks. The OCA consists of two levels: operational- and tactical-level controls. Authority and control are highly distributed among agents belonging to different levels of the holarchy to empower the edges, whereas the integration of decisions is ensured to achieve overall mission objectives. In order to complete a mission in real time in dynamic environments, the decision makers (DMs) at different control levels need to coordinate their actions extensively and be prepared to adapt their schedules. Based on the proposed OCA, we present a holonic multiobjective evolutionary algorithm that produces flexible distributed schedules that account for the unexpected changes in the mission environment, such as asset breakdown, appearance of new events, DM failure, etc. This approach generates multilevel Pareto optimal solutions and, as a consequence, produces a set of ranked neighboring schedules. The actual schedule is a combination of different phases from alternative neighboring schedules that adapt to environmental disturbances. Moreover, the cost of adaptability is reduced while maintaining the stability of the organization. Numerical experiment shows the advantages of the proposed OCA, viz., simplicity, efficiency, and flexibility, which enable an organization to achieve high performance under dynamic and uncertain environments.

Dynamic Multiple-Fault Diagnosis With Imperfect Tests

Fault diagnosis is the process of identifying the failure sources of a malfunctioning system by observing their effects at various test points. It has a number of applications in engineering and medicine. In this paper, we present a near-optimal algorithm for dynamic multiple fault diagnosis in complex systems. This problem involves on-board diagnosis of the most likely set of faults and their time-evolution based on blocks of unreliable test outcomes over time. The dynamic multiple fault diagnosis (dMFD) problem is an intractable NP-hard combinatorial optimization problem. Consequently, we decompose the dMFD problem into a series of decoupled sub-problems, and develop a successive Lagrangian relaxation algorithm (SLRA) with backtracking to obtain a near-optimal solution for the problem. SLRA solves the sub-problems at each sample point by a Lagrangian relaxation method, and shares Lagrange multipliers at successive time points to speed up convergence. In addition, we apply a backtracking technique to further maximize the likelihood of obtaining the most likely evolution of failure sources and to minimize the effects of imperfect tests.

A Probabilistic computational model for identifying organizational structures from uncertain message data

The knowledge of the principles and goals under which an adversary organization operates is required to predict its future activities. To implement successful counter-actions, additional knowledge of the specifics of the organizational structures, such as command, communication, control, and information access networks, as well as responsibility distribution among members of the organization, is required. In this paper, we employ a hidden Markov random field (HMRF) model and a graph matching algorithm to discover the attributes of and relationships among organizational members, assets, environment areas, and mission tasks. We focus on identifying the mapping between hypothesized nodes of enemy command organization and tracked individuals and resources. This also allows us to compute the posterior energy function quantifying the belief that the observed data has been generated by a particular organization. The experiment results show that our probabilistic model and the simulated annealing search algorithm can accurately identify the different organizational structures and achieve correct node mappings among organizational members.

A Lagrangian Relaxation Algorithm for Finding the MAP Configuration in QMR-DT

The quick medical reference decision-theoretic (QMR-DT) network is a large two-layer Bayesian network (BN) [consisting of 571 diseases (ldquofailure sourcesrdquo) and 4075 findings (ldquotest outcomesrdquo)] based on expert and statistical knowledge in internal medicine. The maximum a posteriori (MAP) diagnosis (configuration) based on QMR-DT constitutes an intractable inference problem for all, but a small set of, cases. Consequently, we consider near-optimal algorithms for finding the most likely set of diseases given a set of findings. A computationally efficient algorithm that can handle cases with hundreds of positive findings, i.e., the Lagrangian relaxation algorithm (LRA), is presented. By relaxing the original problem via a set of Lagrange multipliers, the LRA generates an upper bound for the objective function. The near-optimal diagnosis (configuration) is found by minimizing the duality gap via a subgradient method. Numerical experiments show that the LRA is promising in achieving highly accurate diagnosis, and that it is computationally very efficient in solving MAP configuration problems in large and dense two-layer BNs with noisy-OR (BN2O) nodes and containing undirected loops (cycles), such as the QMR-DT network.

Multiple disease (fault) diagnosis with applications to the QMR-DT problem

In this paper, we present three classes of computationally efficient algorithms that can handle cases with hundreds of positive findings in QMR-DT(Quick Medical Reference, Decision-Theoretic) Network. These include Lagrangian Relaxation Algorithm (LRA), Primal Heuristic Algorithm (PHA), and Approximate Belief Revision Algorithm (ABR). These algorithms solve the QMR-DT problem by finding the most likely set of diseases given the findings. Extensive computational experiments have shown that LRA obtains the best solutions among the three algorithms proposed within a relatively small processing time. We also show that the Variational Probabilistic Inference method is a special case of our LRA. The solutions are generic and have application to multiple fault diagnosis in complex industrial systems.

Dynamic multiple fault diagnosis with imperfect tests

Fault diagnosis is the process of identifying the failure sources of a malfunctioning system by observing their effects at various test points. It has a number of applications in engineering and medicine. In this paper, we present a near-optimal algorithm for dynamic multiple fault diagnosis in complex systems. This problem involves on-board diagnosis of the most likely set of faults and their time-evolution based on blocks of unreliable test outcomes over time. The dynamic multiple fault diagnosis (dMFD) problem is an intractable NP-hard combinatorial optimization problem. Consequently, we decompose the dMFD problem into a series of decoupled sub-problems, and develop a successive Lagrangian relaxation algorithm (SLRA) with backtracking to obtain a near-optimal solution for the problem. SLRA solves the sub-problems at each sample point by a Lagrangian relaxation method, and shares Lagrange multipliers at successive time points to speed up convergence. In addition, we apply a backtracking technique to further maximize the likelihood of obtaining the most likely evolution of failure sources and to minimize the effects of imperfect tests.