In recent years, the optimization of multi-objective service composition in distributed systems has become an important issue. Existing work makes a smaller set Pareto-optimal solutions to represent Pareto Front (PF). However, they do not support complex mapping quality (QoS) objective space, thus having limitations providing representative solutions. We propose enhanced differential evolution ...

Spatial independent component analysis (ICA) applied to functional magnetic resonance imaging (fMRI) data identifies functionally connected networks by estimating spatially independent patterns from their linearly mixed fMRI signals. Several multi-subject ICA approaches estimating subject-specific time courses (TCs) and spatial maps (SMs) have been developed, however, there has not yet been a f...

The allocation of design tolerances between the components of a mechanical assembly and manufacturing tolerances can significantly affect the functionality of products and related production costs. This paper introduces Imperialist Competitive Algorithm (ICA) approach to solve the machining tolerance allocation of an overrunning clutch assembly.  The objective is to obtain optimum tolerances of...

This paper presents a new mathematical model for a hybrid flow shop scheduling problem with multiprocessor tasks in which sequence dependent set up times and preemption are considered. The objective is to minimize the weighted sum of makespan and maximum tardiness. Three meta-heuristic methods based on genetic algorithm (GA), imperialist competitive algorithm (ICA) and a hybrid approach of GA a...

Remote sensing image fusion can effectively improve the accuracy of image classification. This paper proposes an image fusion algorithm based on extensions of independent component analysis (ICA) and multi-classifier system. Firstly a novel method of fusing panchromatic and multi-spectral remote sensing images is developed by contourlet transform which can offer a much richer set of directions ...

The performance of unsupervised learning models for natural images is evaluated quantitatively by means of information theory. We estimate the gain in statistical independence (the multi-information reduction) achieved with independent component analysis (ICA), principal component analysis (PCA), zero-phase whitening, and predictive coding. Predictive coding is translated into the transform cod...

Principal component analysis (PCA) and independent component analysis (ICA) are both based on a linear model of multivariate data. They are often seen as complementary tools, PCA providing dimension reduction and ICA separating underlying components or sources. In practice, a two-stage approach is often followed, where first PCA and then ICA is applied. Here, we show how PCA and ICA can be seen...

for the rural population, an improvement in the income of the agriculture and allied sectors is essential for improving the welfare, rural economic prosperity, and the overall economic development. the objective of this study was to determine the optimal pattern in various activities of forest fringe villagers of hezarjarib area in 2013 for management of resources and rural development planning...

in this study, a multi-objective genetic algorithm (moga) is utilized to extract interpretable and compact fuzzy rule bases for modeling nonlinear multi-input multi-output (mimo) systems. in the process of non- linear system identi cation, structure selection, parameter estimation, model performance and model validation are important objectives. furthermore, se- curing low-level and high-level ...