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DTLBO - A population based optimizer for changing and deceptive envirnoment

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1. The code, data, scripts and config files are placed
into their respective directories to help other 
researchers to reproduce the results.
2. Exp-1 : Convergence and Diversity comparision 
among TLBO, DE and SGA under benchmark functions
3.Exp-2 : Convergence and Diversity comparison 
between TLBO and DE under chaning landscape
4. Exp-3: Random immigrant inject into TLBO (continued..)

References:
1. V. Rao, , V.J. Savsani, D.P. Vakharia, Teaching–Learning-Based Optimization: An optimization method for continuous non-linear large scale problems, 

2.Helen G. Cobb, John J. Grefenstette,	Genetic Algorithms for Tracking Changing Environments

3. The Bees Algorithm – A Novel Tool for Complex Optimisation Problems, D.T. Pham, A. Ghanbarzad eh, E. Koç, S. Otri , S. Rahim , M. Zaidi 

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