Tapas Si

Particle Swarm Optimization with adaptive polynomial mutation

Particle Swarm Optimization (PSO) has shown its good search ability in many optimization problem. But PSO easily gets trapped into local optima while dealing with complex problems. In this work, we proposed an improved PSO, namely PSO-APM, in which adaptive polynomial mutation strategy is employed on global best particle with the hope that it will help the particles jump out local optima. In this work, we carried out our experiments on 8 well-known benchmark problems. Finally the results are compared with classical PSO and PSO with power mutation (PMPSO).

Artificial Neural Network Training Using Differential Evolutionary Algorithm for Classification

In this work, we proposed a method of artificial neural network learning using differential evolutionary(DE) algorithm. DE with global and local neighborhood based mutation(DEGL) algorithm is used to search the synaptic weight coefficients of neural network and to minimize the learning error in the error surface.DEGL is a version of DE algorithm in which both global and local neighborhood-based mutation operator is combined to create donor vector.The proposed method is applied for classification of real-world data and experimental results show the efficiency and effectiveness of the proposed method and also a comparative study has been made with classical DE algorithm.

Brain MRI segmentation for tumor detection via entropy maximization using Grammatical Swarm

This paper presents a new method for the segmentation of Magnetic Resonance Imaging (MRI) of brain tumor. First, discrete wavelet transform (DWT)-based soft-thresholding technique is used for removing noise in the MRI. Second, intensity inhomogeneity (IIH) independent of noise is removed from the MRI image. Third, again DWT is used to sharpen the de-noised and IIH corrected image. In this method, the image is decomposed into first level using wavelet decomposition and approximate values are assigned to zero and reconstruct the image results in detailed image. The detailed image is added with the pre-processed image to produce sharpened image. Entropy maximization using Grammatical Swarm (GS) algorithm is used to obtain a set of threshold values and a threshold value is selected with the expert knowledge to separate the lesion part from the other non-diseased cells in the image.

Constrained function optimization using PSO with polynomial mutation

Constrained function optimization using particle swarm optimization (PSO) with polynomial mutation is proposed in this work. In this method non-stationary penalty function approach is adopted and polynomial mutation is performed on global best solution in PSO. The proposed method is applied on 6 benchmark problems and obtained results are compared with the results obtained from basic PSO. The experimental results show the efficiency and effectiveness of the method.

Grammatical Bee Colony

This paper presents Grammatical Bee Colony algorithm. Grammatical Bee Colony is variant of Grammatical Evolution algorithm in which Artificial Bee Colony is used as search engine to write a program in any arbitrary language. The performance of Grammatical Bee Colony is tested on benchmark problems. Experimental results shows that Grammatical Bee Colony is able to generate programs.

MRI brain lesion segmentation using generalized opposition-based glowworm swarm optimization

An improved glowworm swarm optimization algorithm with generalized opposition-based learning is proposed in this paper and is used in segmentation for magnetic resonance images. Noises are removed and intensity inhomogeneities are corrected in the MR images. Next, a clustering technique with glowworm swarm optimization algorithm with generalized opposition based learning is used. Finally, lesions are separated from the normal tissues of the brain in the post-processing step. The performance of the proposed methodology based on both numerical and visual results are compared with K-means and particle swarm optimization based methodologies over two sets of MR images. The experimental results demonstrate that the proposed methodology statistically outperforms other methodologies.

Brain MRI segmentation for tumor detection using Grammatical Swarm based clustering algorithm

This paper presents segmentation of brains Magnetic Resonance image for tumor detection using Grammatical Swarm based clustering algorithm. Grammatical Swarm is a variant of Grammatical Evolution which can generate computer programs. First, MR images of brain are de-noised using Discrete Wavelet Transform based soft-thresholding technique. Grammatical Swarm based clustering algorithm is developed to segment the de-noised images to separate the mass lesion or tumor from the non-diseased objects in the image.

Fast Convergence in Function Optimization Using Modified Velocity Updating in PSO Algorithm

In this paper, a new version of Particle Swarm Optimization (PSO) Algorithm has been proposed where the velocity update equation of PSO has been modified. A new term is added withthe original velocity update equation by calculating difference between the global best of swarm and local best of particles. The proposed method is applied on eight well known benchmark problems and experimental results are compared with the standard PSO (SPSO). From the experimental results, it has been observed that the newly proposed PSO algorithm outperforms the SPSO in terms of convergence, speed and quality.

Segmentation of Brain MRI Using Wavelet Transform and Grammatical Bee Colony

Multimodal Magnetic Resonance Imaging (MRI) is an imaging technique widely used in the diagnosis and treatment planning of patients. Lesion segmentation of brain MRI is one of the most important image analysis task in medical imaging. In this paper, a new method for the supervised segmentation of the lesion in brain MRI using Grammatical Bee Colony (GBC) is proposed. The segmentation process is adversely affected by the presence of noises and intensity inhomogeneities in the Magnetic Resonance (MR) images. Therefore, noises are removed from the images and intensity inhomogeneities are corrected in the pre-processing steps. A set of stationary wavelet features are extracted from the co-registered T1-weighted (T1-W), T2-weighted (T2-W) and Fluid–Attenuated Inversion Recovery (FLAIR) images after skull stripping. A classifier is evolved using the GBC to classify the tissues as healthy tissues or lesions. The GBC classifier is trained with extracted features. The trained classifier is used to segment the test...