This paper provides an introduction to a new method for distributed optimization based on collaborative optimization, a decomposition-based method for the optimization of complex multidisciplinary designs. The key idea in this approach is to include models of the global objective and all of the subspace constraints in each subspace optimization problem while maintaining the low dimensionality o...

The distribution loads, output of distributed generations (DGs) and dynamic power price present obvious time-sequence property, the typical property is studied in this paper. The model of microgrid (including adjustable load, DGs, storage and dynamic power price) is studied. A multi-timescale collaborative optimization model is built towards microgrid; main measures in different timescale optim...

Complex systems are frequently found in modern industry. But with their multisubsystems, multiobjectives, and multiconstraints, the optimization of complex systems is extremely hard. In this paper, a new algorithm adapted from biogeography-based optimization (BBO) is introduced for complex systemoptimization. BBO/Complex is the combination of BBOwith amultiobjective ranking system, an innovativ...

Much research has recently been carried out on the incorporation of trust models into recommender systems. It is generally understood that trust-based recommender systems can help to improve the accuracy of predictions. Moreover they provide greater robustness against profile injection attacks by malicious users. In this paper we analyze these contentions in the context of two trust-based algor...

Design of complex high-tech systems typically requires an integrated analysis of the multiple interacting physical phenomena that jointly influence the overall system’s behaviour. For aircraft wings, for example, the aerodynamic loading and the wing structural deformation strongly interact, with their balance determining the overall wing behaviour. This balance must be evaluated for the differe...

In this paper we focus on solving DCOPs in computationally demanding scenarios. GDL optimally solves DCOPs, but requires exponentially large cost functions, being impractical in such settings. Function filtering is a technique that reduces the size of cost functions. We improve the effectiveness of function filtering to reduce the amount of resources required to optimally solve DCOPs. As a resu...

Electronic commerce (EC) has become an important support for business and is regarded as an efficient system that connects suppliers with online users. Among the applications of EC, the recommender system is undoubtedly a popular issue to make the best recommendation to the users. Even if tremendous approaches have been proposed to perfect the recommendation, a comprehensive module comprising o...

(ABSTRACT) Today's modern aerospace systems exhibit strong interdisciplinary coupling and require a multidisciplinary, collaborative approach. Analysis methods that were once considered feasible only for advanced and detailed design are now available and even practical at the conceptual design stage. This changing philosophy for conducting conceptual design poses additional challenges beyond th...

We propose a novel approach based on dual filtering techniques for the detection of possible variations of the thermal properties of the spacecraft that result from variations of its physical properties and for determining a complete thermal mapping of the system. System and sensor uncertainties are taken into account in the lumped parameter modeling of the thermal system and a dual filter is r...

Collaborative filtering is one of the most effective techniques for making personalized content recommendation. In the literature, a common experimental setup in the modeling phase is to minimize, either explicitly or implicitly, the (expected) error between the predicted ratings and the true user ratings, while in the evaluation phase, the resulting model is again assessed by that error. In th...