Optimization Problems and Algorithms

Abstract: Over the last two decades, nature-inspired stochastic optimization techniques have been widely used to solve a variety of problems in both science and industry. Despite the popularity and impact of such methods, optimization of real-world problems involves addressing a large number of difficulties such as local solutions, multiple objectives, constraints, expensive objective function, noisy objective function, dynamic search space, uncertainties, etc. In this talk, the state-of-the-art and recent advances in addressing such difficulties during optimization will be briefly presented. Bio: Associate Professor Seyedali Mirjalili is the director of the Centre for Artificial Intelligence Research and Optimization at Torrens University Australia. He is internationally recognized for his advances in Swarm Intelligence and Optimization, including the first set of algorithms from a synthetic intelligence standpoint – a radical departure from how natural systems are typically understood – and a systematic design framework to reliably benchmark, evaluate, and propose computationally cheap robust optimization algorithms. He has published over 200 publications with over 22,000 citations and an H-index of 53. As the most cited researcher in Robust Optimization, he is in the list of 1% highly-cited researchers and named as one of the most influential researchers by the world by Web of Science in 2019 and 2020. Dr. Mirjalili is a senior member of IEEE and an associate editor of several leading AI journals including Neurocomputing, Applied Soft Computing, Advances in Engineering Software, Applied Intelligence, and IEEE Access. His research interests include Robust Optimization, Engineering Optimization, Multi-objective Optimization, Swarm Intelligence, Evolutionary Algorithms, and Artificial Neural Networks. He is working on the application of multi-objective and robust meta-heuristic optimization techniques as well.