For accurate prognostics, users have to determine the current health of the system and predict future degradation pattern of the system. An increasingly popular approach toward tackling prognostic problems involves the use of switching models to represent various degradation phases, which the system undergoes. Such approaches have the advantage of determining the exact degradation phase of the system and being able to handle nonlinear degradation models through piecewise linear approximation. However, limitations of such existing methods include, limited applicability due to the discretization of predicted remaining useful life, insufficient robustness due to the use of single models and others. This paper circumvents these limitations by proposing a hybrid of ensemble methods with switching methods. The proposed method first implements a switching Kalman filter (SKF) to classify between various linear degradation phases, then predict the future propagation of fault dimension using appropriate Kalman filters for each phase. This proposed method achieves both continuous and discrete prediction values representing the remaining life and degradation phase of the system, respectively. The proposed framework is shown via a case study on benchmark simulated aeroengine data sets. The evaluation of the proposed framework shows that the proposed method achieves better accuracy and robustness against noise compared with other methods reported in the literature. The results also indicate the effectiveness of the SKF in detecting the switching point between various degradation modes.

As scientific applications like Computational Fluid Dynamics (CFD) simulations generate more and more data, co-processing becomes the most cost effective way to process the vast amount of data generated by these simulation. In a co-processing environment, analysis and/or visualization of intermediate results occur concurrently to the simulation itself. Improved efficiency and early insight into the simulation process and results are potential advantages in comparison to postprocessing, where analysis and/or visualization are performed after the completion of the simulation. To enable co-processing, however, intermediate data needs to be shared between simulation and data analysis, and some degree of coordination may be required to maintain the correctness of both simulation and data analysis. The overhead incurred to facilitate data sharing and coordination may well offset benefits gained, particularly where distributed, large-scale systems are involved as workload sharing, processor affinity and data locality introduce significant effects to the overall performance. In this paper, we propose a co-processing framework to address these issues. The empirical benchmarking results suggest that co-processing overhead tasks scale well with the system size, the overall gain of about 20% in turnaround time compared to post-processing and that the coprocessing framework allows simulation and data analysis task to scale up to their individual limits.

Stochastic optimization of computationally expensive problems is a relatively new field of research in evolutionary computation (EC). At present, few EC works have been published to handle problems plagued with constraints that are expensive to compute. This paper presents a surrogate-assisted memetic co-evolutionary framework to tackle both facets of practical problems, i.e. the optimization problems having computationally expensive objectives and constraints. In contrast to existing works, the cooperative co-evolutionary mechanism is adopted as the backbone of the framework to improve the efficiency of surrogate-assisted evolutionary techniques. The idea of random-problem decomposition is introduced to handle interdependencies between variables, eliminating the need to determine the decomposition in an ad-hoc manner. Further, a novel multi-objective ranking strategy of constraints is also proposed. Empirical results are presented for a series of commonly used benchmark problems to validate the proposed algorithm.

Effective coordination of agents' actions in partially-observable domains is a major challenge of multi-agent systems research. To address this, many researchers have developed techniques that allow the agents to make decisions based on estimates of the states and actions of other agents that are typically learnt using some form of machine learning algorithm. Nevertheless, many of these approaches fail to provide an actual means by which the necessary information is made available so that the estimates can be learnt. To this end, we argue that cooperative communication of state information between agents is one such mechanism. However, in a dynamically changing environment, the accuracy and timeliness of this communicated information determine the fidelity of the learned estimates and the usefulness of the actions taken based on these. Given this, we propose a novel information-sharing protocol, post-task-completion sharing, for the distribution of state information. We then show, through a formal analysis, the improvement in the quality of estimates produced using our strategy over the widely used protocol of sharing information between nearest neighbours. Moreover, communication heuristics designed around our information-sharing principle are subjected to empirical evaluation along with other benchmark strategies (including Littman's Q-routing and Stone's TPOT-RL) in a simulated call-routing application. These studies, conducted across a range of environmental settings, show that, compared to the different benchmarks used, our strategy generates an improvement of up to 60% in the call connection rate; of more than 1000% in the ability to connect long-distance calls; and incurs as low as 0.25 of the message overhead.

We have evaluated the effectiveness of a probabilistic reciprocity scheme for promoting co-operation among self-interested agents. The probabilistic reciprocity mechanism is used to determine whether an agent should co-operate when approached for help by another agent. The situation becomes more complex when a group of agents seeks help from another group. The opinions of the members of the helping group about each of the asking group members can be combined to evaluate such a request for help. Exploitative agents would want to be part of groups that receive help from other groups, but will try to prevent its group from helping other groups. Such agents, revealing false opinion about the reputation of others, can cause unwarranted rejection of help requests from other groups. This leads to global performance degradation in terms of reduced inter-group co-operation and increased cost for the individual agents. We study the viability of reciprocative agents in randomly formed groups and when groups are formed by agents contracting other helpful agents. Group helping decisions are based on both average and worst combined ratings of group members. A key result from our study is that lying exploitative agents, who provide false opinions about other agents, become ineffective when focused group selection is enabled.