| CDEPSO: a bi-population hybrid approach for dynamic optimization problems JK Kordestani, A Rezvanian, MR Meybodi Applied intelligence 40, 682-694, 2014 | 66 | 2014 |
| A novel framework for improving multi-population algorithms for dynamic optimization problems: A scheduling approach JK Kordestani, AE Ranginkaman, MR Meybodi, P Novoa-Hernández Swarm and evolutionary computation 44, 788-805, 2019 | 50 | 2019 |
| A novel hybrid adaptive collaborative approach based on particle swarm optimization and local search for dynamic optimization problems A Sharifi, JK Kordestani, M Mahdaviani, MR Meybodi Applied Soft Computing 32, 432-448, 2015 | 50 | 2015 |
| An adaptive bi-flight cuckoo search with variable nests for continuous dynamic optimization problems JK Kordestani, HA Firouzjaee, MR Meybodi Applied Intelligence, 1-21, 2018 | 47 | 2018 |
| LADE: learning automata based differential evolution M Mahdaviani, JK Kordestani, A Rezvanian, MR Meybodi International Journal on Artificial Intelligence Tools 24 (06), 1550023, 2015 | 31 | 2015 |
| An improved differential evolution algorithm using learning automata and population topologies JK Kordestani, A Ahmadi, MR Meybodi Applied intelligence 41, 1150-1169, 2014 | 31 | 2014 |
| An efficient oscillating inertia weight of particle swarm optimisation for tracking optima in dynamic environments JK Kordestani, A Rezvanian, MR Meybodi Journal of Experimental & Theoretical Artificial Intelligence 28 (1-2), 137-149, 2016 | 26 | 2016 |
| Cuckoo search with composite flight operator for numerical optimization problems and its application in tunneling HA Firouzjaee, JK Kordestani, MR Meybodi Engineering Optimization 49 (4), 597-616, 2017 | 23 | 2017 |
| Cellular teaching-learning-based optimization approach for dynamic multi-objective problems A Birashk, JK Kordestani, MR Meybodi Knowledge-Based Systems 141, 148-177, 2018 | 20 | 2018 |
| A note on the exclusion operator in multi-swarm PSO algorithms for dynamic environments J Kazemi Kordestani, MR Meybodi, AM Rahmani Connection Science, 1-25, 2019 | 18 | 2019 |
| New measures for comparing optimization algorithms on dynamic optimization problems JK Kordestani, A Rezvanian, MR Meybodi Natural Computing 18 (4), 705-720, 2019 | 13 | 2019 |
| A note on the paper “A multi-population harmony search algorithm with external archive for dynamic optimization problems” by Turky and Abdullah AE Ranginkaman, JK Kordestani, A Rezvanian, MR Meybodi Information Sciences 288, 12-14, 2014 | 13 | 2014 |
| Self-adaptive multi-population genetic algorithms for dynamic resource allocation in shared hosting platforms A Shirali, J Kazemi Kordestani, MR Meybodi Genetic Programming and Evolvable Machines 19, 505-534, 2018 | 9 | 2018 |
| A two-level function evaluation management model for multi-population methods in dynamic environments: hierarchical learning automata approach J Kazemi Kordestani, MR Meybodi, AM Rahmani Journal of Experimental & Theoretical Artificial Intelligence 33 (1), 1-26, 2021 | 5 | 2021 |
| An Introduction to Learning Automata and Optimization J Kazemi Kordestani, M Razapoor Mirsaleh, A Rezvanian, MR Meybodi Advances in Learning Automata and Intelligent Optimization, 1-50, 2021 | 2 | 2021 |
| Advances in Learning Automata and Intelligent Optimization JK Kordestani Springer International Publishing, 2021 | 2 | 2021 |
| Application of Sub‐Population Scheduling Algorithm in Multi‐Population Evolutionary Dynamic Optimization JK Kordestani, MR Meybodi Evolutionary Computation in Scheduling, 169-211, 2020 | 2 | 2020 |
| Cellular Automata, Learning Automata, and Cellular Learning Automata for Optimization J Kazemi Kordestani, M Razapoor Mirsaleh, A Rezvanian, MR Meybodi Advances in Learning Automata and Intelligent Optimization, 75-125, 2021 | 1 | 2021 |
| An overview of multi-population methods for dynamic environments J Kazemi Kordestani, M Razapoor Mirsaleh, A Rezvanian, MR Meybodi Advances in Learning Automata and Intelligent Optimization, 253-286, 2021 | 1 | 2021 |
| Learning Automata for Online Function Evaluation Management in Evolutionary Multi-population Methods for Dynamic Optimization Problems J Kazemi Kordestani, M Razapoor Mirsaleh, A Rezvanian, MR Meybodi Advances in Learning Automata and Intelligent Optimization, 287-321, 2021 | | 2021 |
M. R. MeybodiProfessor of CEIT Dept, Amirkabir University of Technology, Tehran, IranVerified email at aut.ac.ir
