I am highly thankful to various researchers to post various forms of information at different platforms (books, papers, presentation, social media, google etc) that help in developing these presentations. The aim here is to provide information in most simple way to understand. I acknowledge the efforts of all.

Optimisation Algorithms:

Machine Learning:



1. Termite Spatial Correlation based Particle Swarm Optimization (TSC-PSO): This  proposes a new Termite Spatial Correlation based Particle Swarm Optimization (TSC-PSO) algorithm inspired by the movement strategy shown within Termites (Cornitermes cumulans). TSC-PSO modifies the velocity equation in the original PSO algorithm by replicating the step correlation based termite motion mechanism that exhibits individually in nature and works with decentralized control to collectively perform the overall task. Further, the algorithm incorporates the mutation strategy within it to make it suitable to avoid stagnation conditions while performing optimization in complex search spaces. For deriving its utility various benchmark functions of different geometric properties have been used. Experiments clearly demonstrate the success of the proposed algorithm in different benchmark conditions against various state-of-the-art optimization algorithms. (Composed by My Team consisting of Avinash SharmaB. K. Panigrahi and Swagtam Das)

Download the code

2. Structured Clanning-based Ensemble Optimization (SCEO): inspired by the social organization of the Elephant clan is proposed for solving complex numerical optimization problems. The proposed algorithm is inspired by the complex and diversified behaviour present within the fission-fusion-based social structure of the elephant society. The population of elephants can consist of various groups with relationship between individuals ranging from mother-child bond, bond groups, independent males, and strangers. The algorithm tries to model this individualistic behaviour to formulate an ensemble-based optimization algorithm. The algorithm performance on these test benchmarks is compared to various state-of-the-art optimization algorithms. Experiments clearly showcase the success of the proposed algorithm in optimizing the benchmark functions to better values. (Composed by My Team consisting of Avinash Sharma,  Akash Saxena and B. K. Panigrahi)

Download the code

3. Framework for Generation and Detection of P300 waveforms: P300 is a unique signal elicited by the brain on receiving a specific stimulus. It is most commonly known for being used as a EEG based speller, where the user can, simply with his focus, spell a word. Since the EEG data consisted of a lot of data to work with, we ended up with a large variety and quantity of features to experiment with. To enable modular organization, fast code execution and stacked feature vectors we setup a framework which utilisescaching, parallel computing and modularity. Finally after intensive testing we obtained highly accurate results, competitive with work done previously. (Composed by My Team consisting of Mr. A. Kumar, Mr. S. Shah @MNIT)

Download the code

4. Backtracking Search Optimization based Neural Network (BSANN): BSANN is a complex stochastic search based Evolutionary Algorithm which smartly backtracks from new to old populations during its evolution to reduce error caused from entering a non-ideal search space. It is based on a multilayer Neural Network Architecture. It has shown excellent results in the domain of EEG pattern detection beating the results of best available algorithms(including winner of the BCI competition) in the field of Motor Imagery BCI. A python library implementation of both the original BSA as well as BSA-NN supporting parallel processing has been attached below. (Composed by My Team consisting of Mr. S. Shah, Mr. S. Agarwal @MNIT)

Download the code

5. Vectorized Particle Swarm Optimization Algorithm: PSO code presented here is based on basic swarming techniques where the global and the personal best solution of agents lead to the global best position according to the problem. The codes of PSO and IPSO (Inertial Particle Swarm Optimization) are presented here and benchmark functions such as Ackley, FoxHoles, Rosenbroch etc. are given along the codes for verification. The code has been vectorized in MATLAB so as to make it even faster. Multiple steps have been reduced resulting in higher efficiency and speed of the algorithm. The matlab files for the PSO toolbox are given below for download. The syntaxes and try run codes are presented alongside. (Composed by My Team consisting of Mr. B. P. Singh @MNIT)

Download the code

Download Benchmark functions: Rosenbrock,  Restrigin,  Griewank, FoxHoles,  Ackley

6. Gummi : It is a LaTeX editor for the Linux platform, written in C/GTK+. It was designed with simplicity and develops pdf online structure. Gummi was released as free opensource software under the MIT license. (@Gummi)

Download the software

7. Scilab : Scilab is free and open source software for numerical computation providing a powerful computing environment for engineering and scientific applications. It supports Windows, Linux and MAC OSX. (@Scilab)

Download the software

8. Apache OpenOffice : Apache OpenOffice is the leading open-source office software suite for word processing, spreadsheets, presentations, graphics, databases and more. Apache OpenOffice is developed 100% by volunteers. It is compatible with other major office suites. (@Apache OpenOffice)

Download the software

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