Independent component analysis (ICA) is a signal processing technique that can be used to extract meaningful components from a dataset. Biogeography based optimization (BBO) algorithm is a recently developed stochastic optimization algorithm. In this paper, we report the use of the BBO algorithm to optimize a contrast function that measures the statistical independence of the recovered componen...

Data clustering is one of the most important tasks of data mining. This paper aims to describe an integrated data clustering method based on Imperialist Competitive Algorithm (ICA) and K-Harmonic Means (KHM) algorithm. The proposed method is called ICA-KHM. KHM is a well-known clustering method and its main drawback is to converge to local optimums. Imperialist competitive algorithm is an evolu...

In this paper, we propose a new population optimization algorithm called Univariate Marginal Distribution Algorithm with Independent Component Analysis(UMDA/ICA). Our main idea is to incorporate ICA into UMDA algorithm in order to tackle the interrelations among variables. We demonstrate that UMDA/ICA performs better than UMDA for a test function with highly correlated variables.

In this paper, the linear (feed-forward) multilayer ICA algorithm is proposed for the blind separation of high-dimensional mixed signals. There are two main phases in each layer. One is the local ICA phase, where the mixed signals are divided into small local modules and a simple ICA is applied to each module. Another is the mapping phase, where the locally-separated signals are arranged as a l...

The purpose of this study is to demonstrate the potential of Imperialist CompetitiveAlgorithm (ICA) for solving Blasius dierential equation. This algorithm is inspiredby competition mechanism among Imperialists and colonies and has demonstrated excellentcapabilities such as simplicity, accuracy, faster convergence and better global optimumachievement in contrast to other evolutionary algorithms...

this paper deals with volume fraction optimization of functionally graded (fg) beams resting on elastic foundation for maximizing the first natural frequency. the two-constituent functionally graded beam consists of ceramic and metal. these constituents are graded through the thickness of beam according to a generalized power-law distribution. one of the advantages of using generalized power- l...

Independent component analysis (ICA) utilizing prior information, also called semiblind ICA, has demonstrated considerable promise in the analysis of functional magnetic resonance imaging (fMRI). So far, temporal information about fMRI has been used in temporal ICA or spatial ICA as additional constraints to improve estimation of task-related components. Considering that prior information about...

Due to the resource constraint and dynamic parameters, reducing energy consumption became the most important issues of wireless sensor networks topology design. All proposed hierarchy methods cluster a WSN in different cluster layers in one step of evolutionary algorithm usage with complicated parameters which may lead to reducing efficiency and performance. In fact, in WSNs topology, increasin...

The novel Imperialist Competitive Algorithm (ICA) that was recently introduced has a good performance in some optimization problems. The ICA inspired by socio-political process of imperialistic competition of human being in the real world. In this paper, a new Imperialist Competitive Algorithm with Adaptive Radius of Colonies movement (ICAR) is proposed. In the proposed algorithm, for an effect...

ABSTRACT We propose a new algorithm for blind source separation (BSS), in which independent component analysis (ICA) and beamforming are combined to resolve the low-convergence problem through optimization in ICA. The proposed method consists of the following three parts: (1) frequency-domain ICA with direction-of-arrival (DOA) estimation, (2) null beamforming based on the estimated DOA, and (3...