How to read my repository

utils: includes helper functions
script: includes file runs (main files)
models: includes all algorithms
- single_solution:
- multiple_solution: 4 folders
  - human_based
  - physics_based
  - swarm_based
  - evolutionary_based
How to run?
- run files in script folder, your environment need 2 package: copy and numpy
- also change the parameters of models in scripts's file

Notes

This repository includes all optimization algorithms coded in python (Numpy) in my research time
If you want to know how to implement optimization with neural networks, take a look at this repos:
- https://github.com/chasebk/code_FLNN
If you see my code and data useful and use it, please cites us here
- Nguyen, T., Tran, N., Nguyen, B. M., & Nguyen, G. (2018, November). A Resource Usage Prediction System Using Functional-Link and Genetic Algorithm Neural Network for Multivariate Cloud Metrics. In 2018 IEEE 11th Conference on Service-Oriented Computing and Applications (SOCA) (pp. 49-56). IEEE.
- Nguyen, T., Nguyen, B. M., & Nguyen, G. (2019, April). Building Resource Auto-scaler with Functional-Link Neural Network and Adaptive Bacterial Foraging Optimization. In International Conference on Theory and Applications of Models of Computation (pp. 501-517). Springer, Cham.
If you want to know more about code, or want a pdf of both above paper, contact me: nguyenthieu2102@gmail.com

Meta-heuristics

Implement algorithms based on papers

Single Solution

None

Multiple Solution

1._. Physics-based

Tug Of War Optimization: Kaveh, A., & Zolghadr, A. (2016). A novel meta-heuristic algorithm: tug of war optimization. Iran University of Science & Technology, 6(4), 469-492.

Nuclear Reaction Optimization: Wei, Z., Huang, C., Wang, X., Han, T., & Li, Y. (2019). Nuclear Reaction Optimization: A novel and powerful physics-based algorithm for global optimization. IEEE Access.

+ So many equations and loops - take time to run on larger dimension 
+ General O (g * n * d) 
+ Good convergence curse because the used of gaussian-distribution and levy-flight trajectory
+ Use the variant of Differential Evolution

Henry Gas Solubility Optimization: Hashim, F. A., Houssein, E. H., Mabrouk, M. S., Al-Atabany, W., & Mirjalili, S. (2019). Henry gas solubility optimization: A novel physics-based algorithm. Future Generation Computer Systems, 101, 646-667.

+ Too much constants and variables
+ Still have some unclear point in Eq. 9 and Algorithm. 1
+ Can improve this algorithm by opposition-based and levy-flight
+ A wrong logic code in line 91 "j = id % self.n_elements" => to "j = id % self.n_clusters" can make algorithm converge faster. I don't know why?
+ Good results come from CEC 2014

2._. Evolutionary-based

Genetic Algorithms: Holland, J. H. (1992). Genetic algorithms. Scientific american, 267(1), 66-73.
Differential Evolution: Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11(4), 341-359.
Coral Reefs Optimization Algorithm: Salcedo-Sanz, S., Del Ser, J., Landa-Torres, I., Gil-López, S., & Portilla-Figueras, J. A. (2014). The coral reefs optimization algorithm: a novel metaheuristic for efficiently solving optimization problems. The Scientific World Journal, 2014.

3._. Swarm-based

Particle Swarm Optimization: Eberhart, R., & Kennedy, J. (1995, October). A new optimizer using particle swarm theory. In MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science (pp. 39-43). Ieee.
Cat Swarm Optimization: Chu, S. C., Tsai, P. W., & Pan, J. S. (2006, August). Cat swarm optimization. In Pacific Rim international conference on artificial intelligence (pp. 854-858). Springer, Berlin, Heidelberg.
Whale Optimization: Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51-67.
Bacterial Foraging Optimization: Passino, K. M. (2002). Biomimicry of bacterial foraging for distributed optimization and control. IEEE control systems magazine, 22(3), 52-67.
Adaptive Bacterial Foraging Optimization: Yan, X., Zhu, Y., Zhang, H., Chen, H., & Niu, B. (2012). An adaptive bacterial foraging optimization algorithm with lifecycle and social learning. Discrete Dynamics in Nature and Society, 2012.
Artificial Bee Colony: Karaboga, D., & Basturk, B. (2007, June). Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. In International fuzzy systems association world congress (pp. 789-798). Springer, Berlin, Heidelberg.
Pathfinder Algorithm: Yapici, H., & Cetinkaya, N. (2019). A new meta-heuristic optimizer: Pathfinder algorithm. Applied Soft Computing, 78, 545-568.
Harris Hawks Optimization: Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849-872.
Sailfish Optimizer: Shadravan, S., Naji, H. R., & Bardsiri, V. K. (2019). The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Engineering Applications of Artificial Intelligence, 80, 20-34.

4._. Human activity-based

Queuing Search Algorithm: Zhang, J., Xiao, M., Gao, L., & Pan, Q. (2018). Queuing search algorithm: A novel metaheuristic algorithm for solving engineering optimization problems. Applied Mathematical Modelling, 63, 464-490.

List of trash meta-heuristics (totally fake and unethical)

1._. Physics-based

2._. Evolutionary-based

3._. Swarm-based

Sandpiper Optimization Algorithm: Kaur, A., Jain, S., & Goel, S. (2019). Sandpiper optimization algorithm: a novel approach for solving real-life engineering problems. Applied Intelligence, 1-38.
Sooty Tern Optimization Algorithm: Dhiman, G., & Kaur, A. (2019). STOA: A bio-inspired based optimization algorithm for industrial engineering problems. Engineering Applications of Artificial Intelligence, 82, 148-174.
Cant even update the position itself
So many unclear operators and unclear parameters
Can't converge
Butterfly Optimization Algorithm: Arora, S., & Singh, S. (2019). Butterfly optimization algorithm: a novel approach for global optimization. Soft Computing, 23(3), 715-734.
Cant converge as the paper said
The code public from the author different with the pseudo-code in the paper
Totally trash and unethical, plagiarize from Flower Pollination Algorithm developed by Yang
Butterfly Optimization Algorithm: Arora, S., & Singh, S. (2019). Butterfly optimization algorithm: a novel approach for global optimization. Soft Computing, 23(3), 715-734.
Cant converge as the paper said
The code public from the author different with the pseudo-code in the paper
Totally trash and unethical, plagiarize from Flower Pollination Algorithm developed by Yang
Blue Monkey Optimization: (2019) The Blue Monkey: A New Nature Inspired Metaheuristic Optimization Algorithm. DOI: http://dx.doi.org/10.21533/pen.v7i3.621
Really? The idea look like "Chicken Swarm Optimization"
The pseudo-code totally shit in my opinion, just read the paper you will understand.
The unclear point here is the "Rate equation": really confuse because It's contain the position. As you know, The position is the vector, but finally, the author conclude that Rate is random number in range [0, 1] Luckily, using number we can plus/add number and vector or vector and vector. So at first, Rate is random number then after the 1st loop, its become vector.
Morever, both equtions movement of blue monkey and children is the same.
In addition, they don't check the bound after update position.
Keep going, they don't tell you the how to find the global best (I mean from blue monkey group or child group)
The code I have done here is the version which I used my knowledge about meta-heuristics to do it.
The results good for some CEC2014 functions, but can't even converge with the square function.
Can't get the results same at the paper. After all, I think this is completely trash algorithm, need to remove from our comunity.

4._. Human activity-based

The list will continue update...