Resources
Resources
This is a new metaheuristic called the Most Valuable Player Algorithm (MVPA) for solving optimization problems.
The developed algorithm is inspired from sport where players form teams, then these players compete collectively (in teams) in order to win the championship and they compete also individually in order to win the MVP trophy.
The MVPA is a very competitive optimization algorithm, it converges rapidly (with smaller number of functions evaluations) and more successfully (with higher overall success percentage) than many other optimization algorithms.
Because of their several advantages like simplicity, flexibility and adaptability, nature inspired (NI) optimization algorithms have attracted significant attention for solving complex optimization problems. Source of inspiration of NI are multiple. The aim of this work is to propose a new NI optimization algorithm inspired from the Electrostatic Discharge (ESD) event. Tested on a large set of benchmarks and compared with several well-known optimization algorithms the ESDA is found to be a very competitive algorithm.
1. Bouchekara, Houssem R.E.H: 'Electrostatic discharge algorithm: a novel nature-inspired optimisation algorithm and its application to worst-case tolerance analysis of an EMC filter', IET Science, Measurement & Technology, 2019, 13, (4), p. 491-499, DOI: 10.1049/iet-smt.2018.5194. https://digital-library.theiet.org/content/journals/10.1049/iet-smt.2018.5194
The BHBO algorithm is a population-based method that evolves the created population towards the optimal solution via a mechanism inspired from the black hole phenomenon.
1. Bouchekara, H. R. E. H. (2013). Optimal design of electromagnetic devices using a black-Hole-Based optimization technique. IEEE Transactions on Magnetics, 49(12). doi:10.1109/TMAG.2013.2277694
2. Bouchekara, H. R. E. H. (2014). Optimal power flow using black-hole-based optimization approach. Applied Soft Computing, 24, 879–888. doi:10.1016/j.asoc.2014.08.056
3. Smail, M. K., Bouchekara, H. R. E. H., Pichon, L., Boudjefdjouf, H., Amloune, A., & Lacheheb, Z. (2016). Non-destructive diagnosis of wiring networks using time domain reflectometry and an improved black hole algorithm. Nondestructive Testing and Evaluation. doi:10.1080/10589759.2016.1200576
The Improved Chaotic Electromagnetic Field Optimization (ICEFO) algorithm is based on the Electromagnetic Field Optimization (EFO) algorithm, chaotic maps and on a new mechanism.
1. Bouchekara, H. Neural Comput & Applic (2019) https://doi.org/10.1007/s00521-019-04298-3. https://doi.org/10.1007/s00521-019-04298-3
The well-known Particle Swarm Optimization (PSO) algorithm is implemented here.
1. Smail, M. K., Bouchekara, H. R. E. H., Pichon, L., Boudjefdjouf, H., & Mehasni, R. (2014). Diagnosis of wiring networks using Particle Swarm Optimization and Genetic Algorithms. Computers & Electrical Engineering. doi:10.1016/j.compeleceng.2014.07.002. https://doi.org/10.1016/j.compeleceng.2014.07.002
The optimal power flow (OPF) problem is the backbone tool for power system operation. The objective of the OPF problem is to determine the optimal operating state of a power system by optimizing a particular objective while satisfying certain operating constraints.
This code is an illustration on how to solve the OPF problem using a metaheuristic.
1. Bouchekara, H. R. E. H. (2014). Optimal power flow using black-hole-based optimization approach. Applied Soft Computing, 24, 879–888. https://doi.org/10.1016/j.asoc.2014.08.056
2. Bouchekara, H. R. E. H., Abido, M. A., & Boucherma, M. (2014). Optimal power flow using Teaching-Learning-Based Optimization technique. Electric Power Systems Research, 114, 49–59. https://doi.org/10.1016/j.epsr.2014.03.032
3. Bouchekara, H. R. E. H., Abido, M. A., Chaib, a. E., & Mehasni, R. (2014). Optimal power flow using the league championship algorithm: A case study of the Algerian power system. Energy Conversion and Management, 87, 58–70. https://doi.org/10.1016/j.enconman.2014.06.088
4. Bouchekara, H. R. E.-H., & Abido, M. A. (2014). Optimal Power Flow Using Differential Search Algorithm. Electric Power Components and Systems, 42(15), 1683–1699. https://doi.org/10.1080/15325008.2014.949912
5. Bouchekara, H. R. E. H., Chaib, A. E., Abido, M. A., & El-Sehiemy, R. A. (2016). Optimal power flow using an Improved Colliding Bodies Optimization algorithm. Applied Soft Computing, 42, 119–131. https://doi.org/10.1016/j.asoc.2016.01.041
6. Chaib, A. E., Bouchekara, H. R. E. H., Mehasni, R., & Abido, M. A. (2016). Optimal power flow with emission and non-smooth cost functions using backtracking search optimization algorithm. International Journal of Electrical Power & Energy Systems, 81, 64–77. https://doi.org/10.1016/j.ijepes.2016.02.004
7. Houssem El-Hana Bouchekara, H. R., Abido, M. A., & Chaib, A. E. (2016). Optimal Power Flow Using an Improved Electromagnetism-like Mechanism Method. Electric Power Components and Systems, 44(4). https://doi.org/10.1080/15325008.2015.1115919
8. Bouchekara, H. R. E. H., Chaib, A. E., & Abido, M. A. (2016). Optimal power flow using GA with a new multi-parent crossover considering: prohibited zones, valve-point effect, multi-fuels and emission. Electrical Engineering. https://doi.org/10.1007/s00202-016-0488-9
9. Berrouk, F., Bouchekara, H. R. E. H., Chaib, A. E., Abido, M. A., Bounaya, K., & Javaid, M. S. (2018). A new multi-objective Jaya algorithm for solving the optimal power flow problem. Journal of Electrical Systems, 14(3), 165–181. https://www.researchgate.net/profile/Houssem_Bouchekara/publication/327624186_A_New_Multi-objective_Jaya_Algorithm_for_Solving_the_Optimal_Power_Flow_Problem/links/5bac5c66a6fdccd3cb7682b3/A-New-Multi-objective-Jaya-Algorithm-for-Solving-the-Optimal-Power-Flow-Problem.pdf?origin=publication_detail
10. Bouchekara, H. Neural Comput & Applic (2019). Solution of the optimal power flow problem considering security constraints using an improved chaotic electromagnetic field optimization algorithm. https://doi.org/10.1007/s00521-019-04298-3
Multi-objective Jaya algorithm noted as MOJaya is a new optimization algorithm based on SPEA2 (improving strength Pareto evolutionary algorithm) and Jaya algorithm.
1. Berrouk, F., Bouchekara, H. R. E. H., Chaib, A. E., Abido, M. A., Bounaya, K., & Javaid, M. S. (2018). A new multi-objective Jaya algorithm for solving the optimal power flow problem. Journal of Electrical Systems, 14(3), 165–181. https://www.researchgate.net/profile/Houssem_Bouchekara/publication/327624186_A_New_Multi-objective_Jaya_Algorithm_for_Solving_the_Optimal_Power_Flow_Problem/links/5bac5c66a6fdccd3cb7682b3/A-New-Multi-objective-Jaya-Algorithm-for-Solving-the-Optimal-Power-Flow-Problem.pdf?origin=publication_detail
The EM is a flexible and effective population based metaheuristic to search for the optimal solution of global optimization problems, by Birbil and Fang in 2003. It is based on the attraction–repulsion principle of the electromagnetism theory where the population is considered as electrically charged particles spread inside the solution space.
1. Bouchekara, H. R. E. H., Smail, M. K., & Dahman, G. (2013). Diagnosis of Multi-Fault Wiring Network Using Time-Domain Reflectometry and Electromagnetism-Like Mechanism. Electromagnetics, 33(2), 131–143. https://doi.org/10.1080/02726343.2013.756291
2. Bouchekara, H. (2013). Electromagnetic Device Optimization Based on Electromagnetism-Like Mechanism. Applied Computational Electromagnetics Society, 28(3), 241–248. https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=15&ved=2ahUKEwjyhqKM6ujjAhWHHRQKHbD4AIoQFjAOegQIBxAC&url=https%3A%2F%2Faces-society.org%2Fincludes%2Fdownloadpaper.php%3Fof%3DACES_Journal_June_2017_Paper_3%26nf%3D17-6-3&usg=AOvVaw2pfmsIeQdmqnXqpC6sK1si
3. Houssem Rafik El-Hana Bouchekara, Mohammad Ali Abido & Alla Eddine Chaib (2016): Optimal Power Flow Using an Improved Electromagnetism-like Mechanism Method, Electric Power Components and Systems, https://doi.org/10.1080/15325008.2015.1115919
The Improved Electromagnetism-like Mechanism Optimization Algorithm (IEM) is an improved version of the Electromagnetism-like Mechanism Optimization Algorithm (EM).
1. Bouchekara, H. R. E. H., Smail, M. K., & Dahman, G. (2013). Diagnosis of Multi-Fault Wiring Network Using Time-Domain Reflectometry and Electromagnetism-Like Mechanism. Electromagnetics, 33(2), 131–143. https://doi.org/10.1080/02726343.2013.756291
2. Bouchekara, H. (2013). Electromagnetic Device Optimization Based on Electromagnetism-Like Mechanism. Applied Computational Electromagnetics Society, 28(3), 241–248. https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=15&ved=2ahUKEwjyhqKM6ujjAhWHHRQKHbD4AIoQFjAOegQIBxAC&url=https%3A%2F%2Faces-society.org%2Fincludes%2Fdownloadpaper.php%3Fof%3DACES_Journal_June_2017_Paper_3%26nf%3D17-6-3&usg=AOvVaw2pfmsIeQdmqnXqpC6sK1si
3. Houssem Rafik El-Hana Bouchekara, Mohammad Ali Abido & Alla Eddine Chaib (2016): Optimal Power Flow Using an Improved Electromagnetism-like Mechanism Method, Electric Power Components and Systems, https://doi.org/10.1080/15325008.2015.1115919