1. Phung, K., E. Ogunshile and M. Aydin, 2023. Error-type-A novel set of software metrics for software fault prediction. IEEE Access, 11: 30562-30574.
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2. Durgut, R., M.E. Aydin and A. Rakib, 2022. Transfer learning for operator selection: A reinforcement learning approach. Algorithms, Vol. 15. 10.3390/a15010024.
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3. Durgut, R. and M.E. Aydin, 2022. Multi Strategy Search with Crow Search Algorithm. In: Optimisation Algorithms and Swarm Intelligence [Working Title], Vakhania, N. (Ed.), IntechOpen, London, UK, .
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4. Ullah, S., M.H. Malik, M.F. Tuysuz, M. Hasnain and M.E. Aydin, 2021. Max-gain relay selection scheme for wireless networks. Eng. Sci. Technol. Int. J., 24: 183-191.
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5. Sabuncu, I. and M.E. Aydin, 2021. Pandemic Management With Social Media Analytics. In: Data Science Advancements in Pandemic and Outbreak Management, Asimakopoulou, E. and N. Bessis (Eds.), IGI Global, Hershey, Pennsylvania, ISBN-13: 9781799867364, pp: 78-107.
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6. Durgut, R., M.E. Aydin and I. Atli, 2021. Adaptive operator selection with reinforcement learning. Inf. Sci., 581: 773-790.
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7. Durgut, R. and M.E. Aydin, 2021. Reinforcement Learning-Based Adaptive Operator Selection. In: Optimization and Learning, Dorronsoro, B., L. Amodeo, M. Pavone and P. Ruiz (Eds.), Springer, Cham, Switzerland, ISBN: 978-3-030-85671-7, pp: 29-41.
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8. Durgut, R. and M.E. Aydin, 2021. Adaptive binary artificial bee colony algorithm. Appl. Soft Comput., Vol. 101. 10.1016/j.asoc.2020.107054.
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9. Aydin, M.E. and R. Keleş, 2021. A multi agent-based approach for energy efficient water resource management. Comput. Ind. Eng., Vol. 151. 10.1016/j.cie.2020.106679.
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10. Afzalov, A., J. He, A. Lotfi and M.E. Aydin, 2021. Multi-Agent Path Planning Approach Using Assignment Strategy Variations in Pursuit of Moving Targets. In: Agents and Multi-Agent Systems: Technologies and Applications 2021, Jezic, G., J. Chen-Burger, M. Kusek, R. Sperka, R.J. Howlett and L.C. Jain (Eds.), Springer, Singapore, ISBN: 978-981-16-2993-8, pp: 451-463.
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11. Yiğit, V., N.N. Demir, H. Alidrisi and M.E. Aydin, 2020. Elicitation of the factors affecting electricity distribution efficiency using the fuzzy AHP method. Mathematics, Vol. 9. 10.3390/math9010082.
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12. Walker-Roberts, S., M. Hammoudeh, O. Aldabbas, M. Aydin and A. Dehghantanha, 2020. Threats on the horizon: Understanding security threats in the era of cyber-physical systems. J. Supercomputing, 76: 2643-2664.
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13. Tuysuz, M.F. and M.E. Aydin, 2020. QoE-based mobility-aware collaborative video streaming on the edge of 5G. IEEE Trans. Ind. Inf., 16: 7115-7125.
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14. Durgut, R., Y.Y. Baydilli and M.E. Aydin, 2020. Feature Selection with Artificial Bee Colony Algorithms for Classifying Parkinson’s Diseases. Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference, 28 May 2020 Springer International Publishing, 338-351.
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15. Damaj, I., M. Elshafei, M. El-Abd and M.E. Aydin, 2020. An analytical framework for high-speed hardware particle swarm optimization. Microprocess. Microsyst., Vol. 72. 10.1016/j.micpro.2019.102949.
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16. DÊğenci, M. and M.E. Aydin, 2020. A honeybees-inspired heuristic algorithm for numerical optimisation. Neural Comput. Appl., 32: 12311-12325.
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17. Comșa L.S., S. Zhang, M. Aydin, P. Kuonen, R. Trestian and G. Ghinea, 2019. A comparison of reinforcement learning algorithms in fairness-oriented OFDMA schedulers. Information, Vol. 10. 10.3390/info10100315.
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18. Comşa, I.S., S. Zhang, M.E. Aydin, P. Kuonen, R. Trestian and G. Ghinea, 2019. Guaranteeing User Rates With Reinforcement Learning in 5G Radio Access Networks. In: Next-Generation Wireless Networks Meet Advanced Machine Learning Applications, Comşa, I.S. and R. Trestian (Eds.), IGI Global, Hershey, Pennsylvania, ISBN-13: 9781522574583, pp:163-198.
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19. Alidrisi, H., M.E. Aydin, A.O. Bafail, R. Abdulal and S.A. Karuvatt, 2019. Monitoring the performance of petrochemical organizations in Saudi Arabia using data envelopment analysis. Mathematics, Vol. 7. 10.3390/math7060519.
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20. DÊg̃enci, M. and M.E. Aydin, 2018. Diversifying Search in Bee Algorithms for Numerical Optimisation. In: Computational Collective Intelligence, Nguyen, N.T., E. Pimenidis, Z. Khan and B. Trawiński, Springer, Cham, Switzerland, ISBN: 978-3-319-98445-2, pp: 132-144.
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21. Comsa, I.S., S. Zhang, M.E. Aydin, P. Kuonen, Y. Lu, R. Trestian and G. Ghinea, 2018. Towards 5G: A reinforcement learning-based scheduling solution for data traffic management. IEEE Trans. Netw. Serv. Manage., 15: 1661-1675.
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22. Aydin, M.E., E. Oztemel, D. Nashawati, M. Odeh and A. Mansour, 2018. An agent-based approach for strategic alignment in enterprise systems: A cancer care case. 2018 1st International Conference on Cancer Care Informatics (CCI). 19-21 November 2018 Institute Electrical Electronics Engineers, 1-7.
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23. Şenyiğit, E., M. DÊğenci, M.E. Aydin and M. Zeydan, 2013. Heuristic-based neural networks for stochastic dynamic lot sizing problem. Appl. Soft Comput., 13: 1332-1339.
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24. Aydin, M.E., R. Kwan, C. Leung, C. Maple and J. Zhang, 2013. A hybrid swarm intelligence algorithm for multiuser scheduling in HSDPA. Appl. Soft Comput., 13: 2990-2996.
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25. Aydin, M.E., R. Kwan and J. Wu, 2013. Multiuser scheduling on the LTE downlink with meta-heuristic approaches. Phys. Commun., 9: 257-265.
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26. Aydin, M.E., 2012. Coordinating metaheuristic agents with swarm intelligence. J. Intell. Manuf., 23: 991-999.
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27. Bessis, N., E. Assimakopoulou, M.E. Aydin and F. Xhafa, 2011. Utilizing Next Generation Emerging Technologies for Enabling Collective Computational Intelligence in Disaster Management. In: Next Generation Data Technologies for Collective Computational Intelligence, Bessis, N., E. Assimakopoulou, M.E. Aydin and F. Xhafa (Eds.), Springer, Berlin, Heidelberg, ISBN: 978-3-642-20343-5, pp: 503-526.
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28. Aydin, M.E., R. Kwan, J. Wu and J. Zhang, 2011. Multiuser Scheduling on the LTE Downlink with Simulated Annealing. IEEE Conference on Vehicular Technology (VTC), 15-18 May 2011 Institute Electrical Electronics Engineers, 1-5.
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29. Aydin, M.E., N. Bessis, E. Asimakopoulou, F. Xhafa and J. Wu, 2011. Scanning Environments with Swarms of Learning Birds: A Computational Intelligence Approach for Managing Disasters. International Conference on Advanced Information Networking and Applications (AINA), 22-25 March 2011 Institute of Electrical and Electronics Engineers, 332-339.
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30. Chan, K.Y., C.K. Kwong, H. Jiang, M.E. Aydin and T.C. Fogarty, 2010. A new orthogonal array based crossover, with analysis of gene interactions, for evolutionary algorithms and its application to car door design. Expert Syst. Appl., 37: 3853-3862.
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31. Aydin, M.E., 2010. Collaboration of heterogenous metaheuristic agents. 2010 Fifth International Conference on Digital Information Management (ICDIM), 05-08 July 2010 Institute Electrical Electronics Engineers, 540-545.
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32. Kwan, R., M.E. Aydin, C. Leung and J. Zhang, 2009. Multiuser scheduling in high speed downlink packet access. IET Commun., 3: 1363-1370.
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33. Dereli, T., S.U. Seckiner, G.S. Das, H. Gokcen and M.E. Aydin, 2009. An exploration of the literature on the use of 'swarm intelligence-based techniques' for public service problems. Eur. J. Ind. Eng., 3: 379-423.
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34. Chan, K.Y., K.W. Chan, G.T.Y. Pong, M.E. Aydin, T.C. Fogarty and S.H. Ling, 2009. A statistics-based genetic algorithm for quality improvements of power supplies. Eur. J. Ind. Eng., 3: 468-492.
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35. Aydin, M.E., R. Kwan, C. Leung and J. Zhang, 2009. Multiuser Scheduling in HSDPA with Particle Swarm Optimization. In: Applications of Evolutionary Computing, Giacobini, M., A. Brabazon, S. Cagnoni, G.A. Caro and A. Ekárt et al (Eds.), Springer, Berlin, Heidelberg, ISBN: 978-3-642-01128-3, pp:71-80.
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36. Kwan, R., M.E. Aydin, C. Leung and J. Zhang, 2008. Multiuser Scheduling in HSDPA using Simulated Annealing. International Wireless Communications and Mobile Computing Conference, IWCMC, 06-08 August 2008 Institute Electrical Electronics Engineers, 236-241.
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37. Chan, K.Y., T.C. Fogarty, M.E. Aydin, S.H. Ling and H.H.C. Iu, 2008. Genetic algorithms with dynamic mutation rates and their industrial applications. Int. J. Comp. Intell. Appl., 7: 103-128.
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38. Aydin, M.E., R. Kwan, C. Leung and J. Zhang, 2008. A Particle Swarm Optimization Algorithm for Multiuser Scheduling in HSDPA. In: Ant Colony Optimization and Swarm Intelligence, Dorigo, M., M. Birattari, C. Blum, M. Clerc, T. Stützle and A.F.T. Winfield (Eds.), Springer, Berlin, Heidelberg, ISBN: 978-3-540-87526-0, pp: 395-396.
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39. Aydin, M.E. and M. Sevkli, 2008. Sequential and Parallel Variable Neighborhood Search Algorithms for Job Shop Scheduling. In: Metaheuristics for Scheduling in Industrial and Manufacturing Applications, Xhafa, F. and A. Abraham (Eds.), Springer, Berlin Heidelberg, ISBN: 978-3-540-78984-0, pp:125-144.
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40. Yang, J., M.E. Aydin, J. Zhang and C. Maple, 2007. UMTS base station location planning: A mathematical model and heuristic optimisation algorithms. IET Commun., 1: 1007-1014.
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41. Yang, J., J. Zhang, M.E. Aydin and J.Y. Wu, 2007. Optimisation of WCDMA radio networks with consideration of link-level performance factors. Int. J. Mob. Netw. Des. Innovation, 2: 26-33.
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42. Yang, J., J. Zhang, M.E. Aydin and J.Y. Wu, 2007. A Novel Programming Model and Optimisation Algorithms for WCDMA Networks. IEEE Conference on Vehicular Technology (VTC), 22-25 April 2007 Institute Electrical Electronics Engineers, 1182-1187.
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43. Sevkli, M. and M.E. Aydin, 2007. Parallel variable neighbourhood search algorithms for job shop scheduling problems. IMA J. Manage. Math., 18: 117-133.
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44. Zhang, J., J. Yang and M.E. Aydin, 2006. A Study on Programming Model and Heuristic Optimization Algorithms for WCDMA Radio Networks. International Conference on Communications and Networking in China (CHINACOM), 25-27 October 2006 Institute Electrical Electronics Engineers, 1-6.
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45. Sevkli, M. and M.E. Aydin, 2006. A Variable Neighbourhood Search Algorithm for Job Shop Scheduling Problems. In: Evolutionary Computation in Combinatorial Optimization, Gottlieb, J. and G.R. Raidl, Springer, Berlin, Heidelberg, ISBN: 978-3-540-33178-0, pp: 261-271.
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46. Kwong, C.K., K.Y. Chan, M.E. Aydin and T.C. Fogarty, 2006. An orthogonal array based genetic algorithm for developing neural network based process models of fluid dispensing. Int. J. Prod. Res., 44: 4815-4836.
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