Hirad Assimi

Research Interests

Artificial Intelligence Engineering Design Neural, Evolutionary and Fuzzy Computation Optimisation Renewable Power

Hirad Assimi

Grant Funded Researcher A

School of Electrical and Mechanical Engineering

College of Engineering and Information Technology

Eligible to supervise Masters and PhD (as Co-Supervisor) - email supervisor to discuss availability.

I believe "if we want to make progress fast, we can do it alone; If we want to go far, we have to do it together". I am a multidisciplinary design engineer with the goal of increasing value and productivity while lowering operational costs and saving the planet. I have a diverse technical background and a passion for blending academic knowledge into real-world complex optimisation problems to conduct research with impact and facilitate collaboration between academic research and industrial applications.

My PhD was part of the Integrated Mining Consortium, in which we dealt with real-world problems to improve mining technologies and solve a common problem in stockpile management. I am currently a researcher at the Mine Operational Vehicles Electrification (MOVE) project funded by the Future Battery Industries Cooperative Research Centre (FBICRC) and led by the University of Adelaide. Our project provides the Australian mining industry with a suite of decision-making tools and guidelines that will aid their transition towards battery-supported vehicles and associated stationary machinery in their mining operations.

 

Date Position Institution name
2021 - ongoing Grant Funded Researcher University of Adelaide

Language Competency
English Can read, write, speak, understand spoken and peer review
Persian Can read, write, speak, understand spoken and peer review

Date Institution name Country Title
2018 - 2023 University of Adelaide Australia PhD in Computer Science
2014 - 2017 University of Guilan Iran Master of Science in Mechanical Engineering
2009 - 2014 University of Guilan Iran Bachelor of Science in Mechanical Engineering

Year Citation
2025 Ranjbar, H., Assimi, H., Pourmousavi Kani, S. A., & Soong, W. L. (2025). Frequency-Constrained Autonomous Microgrid Planning for Mining Industry Applications. Applied Energy, 369, 126201-1-126201-17.
DOI Scopus1 WoS1
2025 Ranjbar, H., Assimi, H., Islam, S. M. M., Pourmousavi, S. A., & Soong, W. L. (2025). Optimal planning of renewable-based mining microgrids: a comparative study of multi-objective evolutionary algorithms. Optimization Letters, 24 pages.
DOI Scopus3 WoS3
2024 Assimi, H., Hashemian Ataabadi, S. N., Islam, S. M. M., Soong, W. L., & Pourmousavi, S. A. (2024). Toward Underground Mobile Fleet Electrification: Three essential steps to make a real change. IEEE Electrification Magazine, 12(1), 16-26.
DOI Scopus4 WoS4
2021 Mallipeddi, R., Gholaminezhad, I., Saeedi, M. S., Assimi, H., & Jamali, A. (2021). Robust controller design for systems with probabilistic uncertain parameters using multi-objective genetic programming. Soft Computing, 25(1), 233-249.
DOI Scopus4 WoS5
2019 Assimi, H., Jamali, A., & Nariman-zadeh, N. (2019). Multi-objective sizing and topology optimization of truss structures using genetic programming based on a new adaptive mutant operator. Neural Computing and Applications, 31(10), 5729-5749.
DOI Scopus28 WoS23
2018 Assimi, H., & Jamali, A. (2018). A hybrid algorithm coupling genetic programming and Nelder–Mead for topology and size optimization of trusses with static and dynamic constraints. Expert Systems with Applications, 95, 127-141.
DOI Scopus46 WoS36
2017 Assimi, H., Jamali, A., & Nariman-zadeh, N. (2017). Sizing and topology optimization of truss structures using genetic programming. Swarm and Evolutionary Computation, 37, 90-103.
DOI Scopus64 WoS47
2017 Gholaminezhad, I., Jamali, A., & Assimi, H. (2017). Multi-objective reliability-based robust design optimization of robot gripper mechanism with probabilistically uncertain parameters. Neural Computing and Applications, 28(S1), 659-670.
DOI Scopus20 WoS18
2016 Gholaminezhad, I., Assimi, H., Jamali, A., & Vajari, D. A. (2016). Uncertainty quantification and robust modeling of selective laser melting process using stochastic multi-objective approach. International Journal of Advanced Manufacturing Technology, 86(5-8), 1425-1441.
DOI Scopus26 WoS20

Year Citation
2020 Khayyam, H., Jamali, A., Assimi, H., & Jazar, R. N. (2020). Genetic Programming Approaches in Design and Optimization of Mechanical Engineering Applications. In R. N. Jazar, & L. Dai (Eds.), Nonlinear Approaches in Engineering Applications: Automotive Applications of Engineering Problems (pp. 367-402). Cham, Switzerland: Springer International Publishing.
DOI Scopus11

Year Citation
2022 Assimi, H., Koch, B., Garcia, C., Wagner, M., & Neumann, F. (2022). Run-of-mine stockyard recovery scheduling and optimisation for multiple reclaimers. In Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing (SAC ’22) (pp. 1074-1083). New York, N.Y.: Association for Computing Machinery.
DOI Scopus3 WoS1
2022 Assimi, H., Neumann, F., Wagner, M., & Li, X. (2022). Novelty-Driven Binary Particle Swarm Optimisation for Truss Optimisation Problems. In Lecture Notes in Computer Science Vol. 13222 LNCS (pp. 111-126). Madrid, Spain: Springer International Publishing.
DOI
2021 Assimi, H., Koch, B., Garcia, C., Wagner, M., & Neumann, F. (2021). Modelling and optimization of run-of-mine stockpile recovery. In Proceedings of the 36th ACM Symposium on Applied Computing (SAC '21) (pp. 450-458). New York, N.Y.: Association for Computing Machinery.
DOI Scopus2 WoS2
2021 Assimi, H., Neumann, F., Wagner, M., & Li, X. (2021). Novelty particle swarm optimisation for truss optimisation problems. In GECCO Companion (pp. 67-68). New York, NY, USA: ACM.
DOI Scopus1 WoS1
2020 Assimi, H., Harper, O., Xie, Y., Neumann, A., & Neumann, F. (2020). Evolutionary bi-objective optimization for the dynamic chance-constrained knapsack problem based on tail bound objectives. In G. D. Giacomo, A. Catalá, B. Dilkina, M. Milano, S. Barro, A. Bugarín, & J. Lang (Eds.), Proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020), as published in Frontiers in Artificial Intelligence and Applications Vol. 325 (pp. 307-314). Amsterdam, Netherlands: IOS Press BV.
DOI Scopus22 WoS20
2019 Xie, Y., Harper, O., Assimi, H., Neumann, A., & Neumann, F. (2019). Evolutionary algorithms for the chance-constrained knapsack problem. In A. Auger, & T. Stützle (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2019, Prague, Czech Republic, July 13-17, 2019 Vol. abs/1902.04767 (pp. 338-346). Prague Czech Republic: ACM.
DOI Scopus36 WoS31
2014 Gholaminezhad, I., Jamali, A., & Assimi, H. (2014). Automated synthesis of optimal controller using multi-objective genetic programming for two-mass-spring system. In 2014 2nd Rsi Ism International Conference on Robotics and Mechatronics Icrom 2014 (pp. 41-46). Tehran, IRAN: IEEE.
DOI Scopus9 WoS7

Hans-Jürgen and Marianne Ohff Research Grant at University of Adelaide (2019)

Teaching assistant for Grand Challenges (2020, Semester 2)

Teaching assistant for Grand Challenges (2019, Semester 2)

Teaching assistant for Evolutionary Computation (2018, Semester 2)

Date Role Research Topic Program Degree Type Student Load Student Name
2024 Co-Supervisor Explainable, Transparent and Trustworthy Data-Driven, Modelling of Underground Electric Mining Trucks Doctor of Philosophy Doctorate Full Time Miss Tamsa .
2024 Co-Supervisor Explainable, Transparent and Trustworthy Data-Driven, Modelling of Underground Electric Mining Trucks Doctor of Philosophy Doctorate Full Time Miss Tamsa .
2023 Co-Supervisor Optimal Energy Storage and Infrastructure Design and Operation for Mining's Electrified Mobile Fleet Doctor of Philosophy Doctorate Full Time Mr Behnam Hashemian Ataabadi
2023 Co-Supervisor Optimal Energy Storage and Infrastructure Design and Operation for Mining's Electrified Mobile Fleet Doctor of Philosophy Doctorate Full Time Mr Behnam Hashemian Ataabadi

Connect With Me

External Profiles

Other Links