Ali Sadollah

University of Science and Culture

Dr. Ali Sadollah received his Ph.D. in Mechanical Engineering, from the University of Malaya, Kuala Lumpur, Malaysia, in 2013. He has served as a postdoctoral research fellow at different universities such as Korea University and Nanyang Technological University, Singapore. He was also a guest assistant professor at Sharif University of Technology, Tehran, Iran. Currently, he is an assistant professor at the University of Science and Culture, Tehran, Iran, as well as the vice president of research and technology and head of the international cooperation office. He is ranked among the top 2 and 1 percent of the world’s highly cited scholars, according to Stanford University, California, USA. He is the inventor of two metaheuristic optimization methods: the water cycle algorithm and the neural network algorithm. His research interests include algorithm development, optimization and metaheuristics, applications of soft computing methods in engineering, computational solid mechanics, finite element method, and cold roll forming process. Dr. Sadollah has published four books and contributed to more than 150 scientific studies. He has been invited to participate in numerous international and national events.

3books edited

2chapters authored

Latest work with IntechOpen by Ali Sadollah

Traditional models struggle to cope with complexity, noise, and the existence of a changing environment, while Computational Intelligence (CI) offers solutions to complicated problems as well as reverse problems. The main feature of CI is adaptability, spanning the fields of machine learning and computational neuroscience. CI also comprises biologically-inspired technologies such as the intellect of swarm as part of evolutionary computation and encompassing wider areas such as image processing, data collection, and natural language processing. This book aims to discuss the usage of CI for optimal solving of various applications proving its wide reach and relevance. Bounding of optimization methods and data mining strategies make a strong and reliable prediction tool for handling real-life applications.

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