A Novel Approach for Regularized Signal Deconvolution Based on Hybrid Swarm Intelligence: Application to Neutron Radiography

2012 ◽  
Author(s):  
Slami Saadi ◽  
Abderrezak Guessoum ◽  
Maamar Bettayeb
Keyword(s):  
Swarm Intelligence ◽  
Neutron Radiography ◽  
Hybrid Swarm ◽  
Novel Approach ◽  
Signal Deconvolution ◽  
Intelligence Application

A Novel Approach For Privacy Preservation In Blockchain Network Using Tensor Product And A Hybrid Swarm Intelligence

2021 ◽  
Vol 12 (4) ◽  
pp. 0-0
Keyword(s):  
Tensor Product ◽  
Swarm Intelligence ◽  
Privacy Preservation ◽  
Information Loss ◽  
Data Matrix ◽  
Sensitive Information ◽  
Hybrid Swarm ◽  
Novel Approach ◽  
Block Chain ◽  
Minimal Information

Therefore, block chain-based technique is developed for privacy protection using tensor product and a hybrid swarm intelligence based coefficient generation. Initially, the block chain data with mixed attributes was subjected to the privacy preservation process, in which the raw data matrix and solitude and utility (SU) coefficient were multiplied through the tensor product. Thus, the derivation of the SU coefficient, which handles both sensitive information and utility, was formulated as a searching problem. Then, the proposed algorithm was introduced to evaluate the SU coefficient. The performance of the developed technique was evaluated by means of accuracy and information loss. The achieved results have shown that the developed hybrid sward intelligence reached a maximal accuracy of 0.840 and minimal information loss of 0.159 using dataset-2, compared to the existing system.

Protecting ECG Signals with Hybrid Swarm Intelligence Algorithm

2021 ◽  
pp. 47-60
Author(s):  
Ayushi Kirar ◽  
Siddharth Bhalerao ◽  
Om Prakash Verma ◽  
Irshad Ahmad Ansari
Keyword(s):  
Swarm Intelligence ◽  
Hybrid Swarm ◽  
Ecg Signals ◽  
Intelligence Algorithm ◽  
Swarm Intelligence Algorithm

Distributed version of hybrid swarm intelligence-Nelder Mead algorithm for DOA estimation in WSN

2020 ◽  
Vol 144 ◽  
pp. 113112
Author(s):  
Shree Prasad Maruthi ◽  
Trilochan Panigrahi ◽  
Ravi Prasad K. Jagannath
Keyword(s):  
Swarm Intelligence ◽  
Doa Estimation ◽  
Hybrid Swarm ◽  
Nelder Mead Algorithm

ARTIFICIAL BEE COLONY ALGORITHM WITH IMPROVED EXPLORATIONS: A NOVEL APPROACH FOR NUMERICAL OPTIMIZATION

2014 ◽  
Vol 13 (02) ◽  
pp. 1450010
Author(s):  
MD. SHAFIUL ALAM ◽  
MD. MONIRUL ISLAM ◽  
KAZUYUKI MURASE
Keyword(s):  
Swarm Intelligence ◽  
Numerical Optimization ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Adaptive Strategy ◽  
Local Optima ◽  
Selection Scheme ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Novel Approach

The Artificial Bee Colony (ABC) algorithm is a recently introduced swarm intelligence algorithm that has been successfully applied on numerous and diverse optimization problems. However, one major problem with ABC is its premature convergence to local optima, which often originates from its insufficient degree of explorative search capability. This paper introduces ABC with Improved Explorations (ABC-IX), a novel algorithm that modifies both the selection and perturbation operations of the basic ABC algorithm in an explorative way. First, an explorative selection scheme based on simulated annealing allows ABC-IX to probabilistically accept both better and worse candidate solutions, whereas the basic ABC can accept better solutions only. Second, a self-adaptive strategy enables ABC-IX to automatically adapt the perturbation rate, separately for each candidate solution, to customize the degree of explorations and exploitations around it. ABC-IX is evaluated on several benchmark numerical optimization problems and results are compared with a number of state-of-the-art evolutionary and swarm intelligence algorithms. Results show that ABC-IX often performs better optimization than most other algorithms in comparison on most of the problems.

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