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The multi-constrained multicast routing improved by hybrid bacteria foraging-particle swarm optimization

Sahoo Satya Prakash, Kabat Manas Ranjan

Computer Science

2019

Vol. 20 (2)

245--269

To solve multicast routing under multiple constraints, it is required to generate a multicast tree that ranges from a source to the destinations with minimum cost subject to several constraints. In this paper, PSO has been embedded with BFO to improve the convergence speed and avoid premature convergence that will be used for solving QoS multicast routing problem. The algorithm proposed here generates a set of delay compelled links to every destination present in the multicast group. Then the Bacteria Foraging Algorithm (BFA) selects the paths to all the destinations sensibly from the set of least delay paths to construct a multicast tree. The robustness of the algorithm being proposed had been established through the simulation. The efficiency and effectiveness of the algorithm being proposed was validated through the comparison study with other existing meta-heuristic algorithms. It shows that our proposed algorithm IBF-PSO outperforms its competitive algorithms.

Multi-channeled MR brain image segmentation: A novel double optimization approach combined with clustering technique for tumor identification and tissue segmentation

Narayanan Anitha, Rajasekaran M. Pallikonda, Zhang Yudong, Govindaraj Vishnuvarthanan, Thiyagarajan Arunprasath

Biocybernetics and Biomedical Engineering

2019

Vol. 39, no. 2

350--381

Growth of cancer cells within the human body is a major outcome of the manipulation of cells and it has resulted in the deterioration of the life span of humans. The impact of cancer cells is irretrievable and it has paved the way to the formation of tumors within the human body. For achieving and developing a single-structured framework to prominently identify the tumor regions and segmenting the tissue structures specifically in human brain, a novel combinational algorithm is proposed through this paper. The algorithm has been embodied with two optimization techniques namely particle swarm optimization (PSO) and bacteria foraging optimization (BFO), wherein, PSO helps in finding the best position of global bacterium for BFO, consecutively, BFO supports the modified fuzzy c means (MFCM) algorithm by providing optimized cluster heads. Finally, MFCM segments the tissue regions and identifies the tumor portion, thereby reducing the interaction and complication experienced by a radiologist during patient diagnosis. The strength of the proposed algorithm is proven by comparing it with the state-of-the-art techniques by means of evaluation parameters like mean squared error (MSE), peak signal to noise ratio (PSNR), sensitivity, specificity, etc., Data sets used in this paper were exclusively obtained from hospital, Brain web simulator and BRATS-2013 challenge. The sensitivity and specificity values for 115 MR brain slice images.

Evolutionary computing approaches to optimum design of fuzzy logic controller for a flexible robot system

Subudhi B., Ranasingh S.

Archives of Control Sciences

2013

Vol. 23, no. 4

395--412

This paper presents the design of a Fuzzy Logic Controller (FLC) whose parameters are optimized by using Genetic Algorithm (GA) and Bacteria Foraging Optimization (BFO) for tip position control of a single link flexible manipulator. The proposed FLC is designed by minimizing the fitness function, which is defined as a function of tip position error, through GA and BFO optimization algorithms achieving perfect tip position tracking of the single link flexible manipulator. Then the tip position responses obtained by using both the above controllers are compared to suggest the best controller for the tip position tracking.