Mehdi Neshat

Mr Mehdi Neshat

PhD Student

School of Mechanical Engineering

Faculty of Engineering, Computer and Mathematical Sciences

I received my bachelor and master degrees from Yazd Univesity (Software Engineering,2005) and IAUM (Mashhad,2008) in Artificial Intelligence. I have been awarded a PhD scholarship (ASI) in the University of Adelaide (Australia) from 2017 to 2020. My main research interests are in investigating and optimizing the real engineering problems such as Wave Energy Converters placement optimization and Water Distribution Systems by bio-inspired optimization techniques like Evolutionary Algorithms and Swarm optimization methods.

1. Optimizing the Wave Energy Converters Positions, Control and Shape parameters

2. Optimizing the Water Distribution Systems design

3. Forecasting the wind speed in wind farms

4. Forecasting and modelling the power output of the wind turbines

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

  • Education
    Date Institution name Country Title
    2005 - 2008 Islamic Azad University, Mashhad(IAUM) Iran Master
    2001 - 2005 Yazd University Iran Bachelor

  • Journals
    Year Citation
    2020 Neshat, M., Alexander, B., Sergiienko, N. Y., & Wagner, M. (2020). A new insight into the Position Optimization of Wave Energy Converters by a Hybrid Local Search.. Swarm and Evolutionary Computation, 59, 1-18.
    DOI
    2020 Sergiienko, N. Y., Neshat, M., Silva, L. S. D., Alexander, B., & Wagner, M. (2020). Design optimisation of a multi-mode wave energy converter.. CoRR, abs/2001.08966.
    2020 Neshat, M., Nezhad, M. M., Abbasnejad, E., Tjernberg, L. B., Garcia, D. A., Alexander, B., & Wagner, M. (2020). An Evolutionary Deep Learning Method for Short-term Wind Speed Prediction: A Case Study of the Lillgrund Offshore Wind Farm.. CoRR, abs/2002.09106.
    2020 Neshat, M., Nezhad, M. M., Abbasnejad, E., Groppi, D., Heydari, A., Tjernberg, L. B., . . . Wagner, M. (2020). Hybrid Neuro-Evolutionary Method for Predicting Wind Turbine Power Output.. CoRR, abs/2004.12794.
    2020 Neshat, M., Alexander, B., & Wagner, M. (2020). A hybrid cooperative co-evolution algorithm framework for optimising power take off and placements of wave energy converters.. Inf. Sci., 534, 218-244.
    2019 Neshat, M., Alexander, B., & Simpson, A. R. (2019). Covariance Matrix Adaptation Greedy Search Applied to Water Distribution System Optimization.. CoRR, abs/1909.04846.
    2016 Neshat, M., Tabatabi, M., Zahmati, E., & Shirdel, M. (2016). A hybrid fuzzy knowledge-based system for forest fire risk forecasting. International Journal of Reasoning-based Intelligent Systems, 8(3-4), 132-154.
    DOI
    2016 Neshat, M., & Ahmadi, M. (2016). Recognising the kind of cloud using a new fuzzy knowledge-based system. International Journal of Reasoning-based Intelligent Systems, 8(3-4), 168-180.
    DOI Scopus1
    2016 Pourahmad, A., Neshat, M., & Hasani, M. (2016). Using LibQUAL model for improving the level of students' satisfaction from quality of services in academic libraries: a case study in North Khorasan Province, Iran. Journal of Information and Knowledge Management, 15(1), 1650011-1-1650011-12.
    DOI Scopus4
    2016 Neshat, M., Pourahmad, A., & Hasani, M. (2016). Designing an adaptive neuro fuzzy inference system for prediction of customers satisfaction. Journal of Information and Knowledge Management, 15(4), 1650037.
    DOI
    2016 Neshat, M., Sepidname, G., Mehri, E., & Zalimoghadam, A. (2016). The review of soft computing applications in humanitarian demining robots design. Indian Journal of Science and Technology, 9(4), 1-13.
    DOI Scopus3
    2015 Neshat, M., Sepidname, G., Eizi, A., & Amani, A. (2015). A new skin color detection approach based on fuzzy expert system. Indian Journal of Science and Technology, 8(21).
    DOI Scopus8
    2015 Neshat, M., & Sepidname, G. (2015). A new hybrid optimization method inspired from swarm intelligence: Fuzzy adaptive swallow swarm optimization algorithm (FASSO). Egyptian Informatics Journal, 16(3), 339-350.
    DOI Scopus4
    2014 Neshat, M., Sepidnam, G., Sargolzaei, M., & Toosi, A. (2014). Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications. Artificial Intelligence Review, 42(4), 965-997.
    DOI Scopus185 WoS140
    2013 Neshat, M. (2013). FAIPSO: Fuzzy adaptive informed particle swarm optimization. Neural Computing and Applications, 23(SUPPL1), 95-116.
    DOI Scopus15 WoS12
    2013 Neshat, M., Sepidnam, G., & Sargolzaei, M. (2013). Swallow swarm optimization algorithm: A new method to optimization. Neural Computing and Applications, 23(2), 429-454.
    DOI Scopus40 WoS24
    2012 Neshat, M., Adeli, A., Sepidnam, G., & Sargolzaei, M. (2012). Predication of concrete mix design using adaptive neural fuzzy inference systems and fuzzy inference systems. International Journal of Advanced Manufacturing Technology, 63(1-4), 373-390.
    DOI Scopus9 WoS7
    2012 Neshat, M., Sargolzaei, M., Masoumi, A., & Najaran, A. (2012). A New kind of PSO: Predator particle swarm optimization. International Journal on Smart Sensing and Intelligent Systems, 5(2), 521-539.
    DOI Scopus4
    2012 Neshat, M., Adeli, A., Sepidnam, G., Sargolzaei, M., & Toosi, A. (2012). A review of Artificial Fish Swarm Optimization methods and applications. International Journal on Smart Sensing and Intelligent Systems, 5(1), 107-148.
    DOI Scopus26
    2012 Neshat, M., Sargolzaei, M., Najaran, A., & Adeli, A. (2012). The new method of adaptive CPU scheduling using Fonseca and Fleming's Genetic Algorithm. Journal of Theoretical and Applied Information Technology, 37(1), 1-16.
    Scopus7
    2012 Neshat, M., Yazdi, S., Yazdani, D., & Sargolzaei, M. (2012). A new cooperative algorithm based on PSO and K-means for data clustering. Journal of Computer Science, 8(2), 188-194.
    DOI Scopus22

  • Conference Papers
    Year Citation
    2020 Neshat, M., Alexander, B., Sergiienko, N. Y., & Wagner, M. (2020). Optimisation of large wave farms using a multi-strategy evolutionary framework. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference (GECCO'20) Vol. abs/2003.09594 (pp. 1150-1158). New York: Association for Computing Machinery.
    DOI
    2019 Neshat, M., Abbasnejad, E., Shi, Q., Alexander, B., & Wagner, M. (2019). Adaptive neuro-surrogate-based optimisation method for wave energy converters placement optimisation. In T. Gedeon, K. W. Wong, & M. Lee (Eds.), Proceedings of the 26th International Conference on Neural Information Processing (ICONIP 2019), as published in Lecture Notes in Computer Science (Neural Information Processing Proceedings, Part II) Vol. 11954 (pp. 353-366). Switzerland: Springer Nature.
    DOI Scopus3
    2019 Neshat, M., Alexander, B., Sergiienko, N., & Wagner, M. (2019). A hybrid evolutionary algorithm framework for optimising power take off and placements of wave energy converters. In GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference Vol. abs/1904.07043 (pp. 1293-1301). New York: ACM.
    DOI Scopus4 WoS1
    2018 Neshat, M., Alexander, B., Wagner, M., & Xia, Y. (2018). A detailed comparison of meta-heuristic methods for optimising wave energy converter placements. In H. E. Aguirre, & K. Takadama (Eds.), GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference (pp. 1318-1325). Kyoto: ACM.
    DOI Scopus8
    2012 Adeli, A., Ghorbani-Rad, A., Zomorodian, M., Neshat, M., & Mozaffari, S. (2012). Improving nearest neighbor classification using particle swarm optimization with novel fitness function. In N. Nguyen, K. Hoang, & P. Jedrzejowicz (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 7654 LNAI (pp. 365-372). Ho Chi Minh City, VIETNAM: SPRINGER-VERLAG BERLIN.
    DOI Scopus4 WoS3
    2012 Adeli, A., Sinaee, M., Zomorodian, J., & Neshat, M. (2012). Harmony-based feature selection to improve the nearest neighbor classification. In N. Meghanathan, & M. Wozniak (Eds.), ACM International Conference Proceeding Series (pp. 12-18). ACM.
    DOI
    2011 Neshat, M., & Adeli, A. (2011). Designing a fuzzy expert system to predict the concrete mix design. In IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Proceedings (pp. 80-85). Univ Ottawa, Ottawa, CANADA: IEEE.
    DOI Scopus7 WoS3
    2010 Neshat, M. (2010). A hybrid method in informed search: Fuzzy simplified memory-bounded a* approach. In Proceedings - 2010 International Conference on Computational Intelligence and Communication Networks, CICN 2010 (pp. 105-109).
    DOI
    2010 Rezaei, M., Neshat, M., & Yazdi, H. (2010). A new frequency dependent resistor for modeling skin effect of wire and echo cancellation by PSO. In V. Mahadevan, & Z. Jianhong (Eds.), 2010 The 2nd International Conference on Computer and Automation Engineering, ICCAE 2010 Vol. 1 (pp. 573-577). Singapore, SINGAPORE: IEEE.
    DOI Scopus2 WoS1
    2010 Neshat, M., & Zadeh, A. (2010). Hopfield neural network and fuzzy hopfield neural network for diagnosis of liver disorders. In 2010 IEEE International Conference on Intelligent Systems, IS 2010 - Proceedings (pp. 162-167). IEEE.
    DOI Scopus12
    2010 Neshat, M., & Rezaei, M. (2010). AIPSO: Adaptive informed particle swarm optimization. In 2010 IEEE International Conference on Intelligent Systems, IS 2010 - Proceedings (pp. 438-443). IEEE.
    DOI Scopus4
    2010 Adeli, A., & Neshat, M. (2010). A Fuzzy Expert System for heart disease diagnosis. In S. Ao, O. Castillo, C. Douglas, D. Feng, & J. Lee (Eds.), Proceedings of the International MultiConference of Engineers and Computer Scientists 2010, IMECS 2010 (pp. 134-139). Hong Kong, PEOPLES R CHINA: INT ASSOC ENGINEERS-IAENG.
    Scopus97 WoS16
    2009 Neshat, M., & Yaghobi, M. (2009). Designing a Fuzzy Expert System of Diagnosing the Hepatitis B Intensity Rate and Comparing it with Adaptive Neural Network Fuzzy System. In S. Ao, C. Douglas, W. Grundfest, & J. Burgstone (Eds.), WCECS 2009: WORLD CONGRESS ON ENGINEERING AND COMPUTER SCIENCE, VOLS I AND II (pp. 797-+). San Francisco, CA: INT ASSOC ENGINEERS-IAENG.
    WoS4
    2009 Neshat, M., & Yaghobi, M. (2009). FESHDD: Fuzzy expert system for hepatitis B diseases diagnosis. In ICSCCW 2009 - 5th International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control.
    DOI Scopus3
    2008 Neshat, M., Yaghobi, M., & Naghibi, M. (2008). Designing an expert system of liver disorders by using neural network and comparing it with parametric and nonparametric system. In 2008 5th International Multi-Conference on Systems, Signals and Devices, SSD'08 (pp. 202-208). Amman, JORDAN: IEEE.
    DOI Scopus6
    2008 Neshat, M., Yaghobi, M., Naghibi, M., & Esmaelzadeh, A. (2008). Fuzzy expert system design for diagnosis of liver disorders. In C. Zhao, C. Wu, Y. Wang, & Q. Liu (Eds.), Proceedings - 2008 International Symposium on Knowledge Acquisition and Modeling, KAM 2008 (pp. 252-256). Wuhan, PEOPLES R CHINA: IEEE COMPUTER SOC.
    DOI Scopus40 WoS21

  • Working Paper
    Year Citation
    2020 Neshat, M., Alexander, B., & Wagner, M. (2020). A hybrid cooperative co-evolution algorithm framework for optimising power take off and placements of wave energy converters. ELSEVIER SCIENCE INC.
    DOI
  • Position: PhD Student
  • Phone: 83134729
  • Email: mehdi.neshat@adelaide.edu.au
  • Campus: North Terrace
  • Building: Ingkarni Wardli, floor 4
  • Room: 4 52
  • Org Unit: School of Computer Science
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