In a market-oriented service computing environment, both back-end SLA (service level agreement) offers and front-end SLA requirements should be considered when performing service composition. In this paper, we address the optimization problem of SLA-constrained service composition and focus on the following issues: the difficulties related to preference definition and to weight assignment, the ...

The traditional single objective mean variance optimization model fails to satisfy the investors with multiple investment objectives. So multi-objective portfolio optimization model is considered in this paper. Since this will help investors to achieve highest expected return among the different financial products of the capital market and to fulfill the expected return objectives simultaneousl...

In a structural time series regression model, binary variables have been used to quantify qualitative or categorical quantitative events such as politic and economic structural breaks, regions, age groups and etc. The use of the binary dummy variables is not reasonable because the effect of an event decreases (increases) gradually over time not at once. The simple and basic idea in this paper i...

An iterative fuzzy clustering method is proposed to partition a set of multivariate binary observation vectors located at neighboring geographic sites. The method described here applies in a binary setup a recently proposed algorithm, called Neighborhood EM, which seeks a a partition that is both well clustered in the feature space and spatially regular [2]. This approach is derived from the EM...

a real-time optimization (rto) strategy incorporating the fuzzy sets theory is developed, where the problem constraints obtained from process considerations are treated in fuzzy environment. furthermore, the objective function is penalized by a fuzzified form of the key process constraints. to enable using conventional optimization techniques, the resulting fuzzy optimization problem is then re...

We present a fast Compressive Sensing algorithm for the reconstruction of binary signals {0, 1}-valued binary signals from its linear measurements. The proposed algorithm minimizes a non-convex penalty function that is given by a weighted sum of smoothed l0 norms, under the [0, 1] box-constraint. It is experimentally shown that the proposed algorithm is not only significantly faster than linear...

In this study, we introduce an advanced architecture of genetically optimized Hybrid Fuzzy Neural Networks (gHFNN) and develop a comprehensive design methodology supporting their construction. A series of numeric experiments is included to illustrate the performance of the networks. The construction of gHFNN exploits fundamental technologies of Computational Intelligence (CI), namely fuzzy sets...