Behnam Mohammad Hasani Zade
Affiliations:- Shahid Bahonar University of Kerman, Iran
According to our database1,
Behnam Mohammad Hasani Zade
authored at least 19 papers
between 2019 and 2025.
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Bibliography
2025
J. Supercomput., January, 2025
An improved beluga whale optimization using ring topology for solving multi-objective task scheduling in cloud.
Comput. Ind. Eng., 2025
2024
Clust. Comput., November, 2024
Neural Process. Lett., February, 2024
2023
An improved Caledonian crow learning algorithm based on ring topology for security-aware workflow scheduling in cloud computing.
Peer Peer Netw. Appl., November, 2023
A new hyper-heuristic based on ant lion optimizer and Tabu search algorithm for replica management in cloud environment.
Artif. Intell. Rev., September, 2023
2022
PPO: a new nature-inspired metaheuristic algorithm based on predation for optimization.
Soft Comput., 2022
A new feature extraction technique based on improved owl search algorithm: a case study in copper electrorefining plant.
Neural Comput. Appl., 2022
Improved red fox optimizer with fuzzy theory and game theory for task scheduling in cloud environment.
J. Comput. Sci., 2022
A two-stage scheduler based on New Caledonian Crow Learning Algorithm and reinforcement learning strategy for cloud environment.
J. Netw. Comput. Appl., 2022
2021
J. Supercomput., 2021
Frontiers Comput. Sci., 2021
SAEA: A security-aware and energy-aware task scheduling strategy by Parallel Squirrel Search Algorithm in cloud environment.
Expert Syst. Appl., 2021
Multi-objective scheduling technique based on hybrid hitchcock bird algorithm and fuzzy signature in cloud computing.
Eng. Appl. Artif. Intell., 2021
2020
Soft Comput., 2020
Simul. Model. Pract. Theory, 2020
A multi-objective optimized replication using fuzzy based self-defense algorithm for cloud computing.
J. Netw. Comput. Appl., 2020
New hybrid method for attack detection using combination of evolutionary algorithms, SVM, and ANN.
Comput. Networks, 2020
2019
Hybrid task scheduling strategy for cloud computing by modified particle swarm optimization and fuzzy theory.
Comput. Ind. Eng., 2019