Rate My Professor Ali Ahrari

Ahrari's research focuses on evolutionary algorithms, particularly multimodal and multi-objective optimization, multidisciplinary design optimization, swarm intelligence, constrained optimization, dynamic optimization, surrogate-assisted optimization, robust optimization, and noisy optimization. He has produced 28 peer-reviewed publications, 25 as first author, including four in the IEEE Transactions on Evolutionary Computation: "Revisiting Implicit and Explicit Averaging for Noisy Optimization" (2023), "Static and Dynamic Multimodal Optimization by Improved Covariance Matrix Self-Adaptation Evolution Strategy with Repelling Subpopulations" (2022), "Adaptive Multilevel Prediction Method for Dynamic Multimodal Optimization" (2021), and "A Novel Parametric Benchmark Generator for Dynamic Multimodal Optimization" (2021). Notable earlier publications include "Grenade Explosion Method—a Novel Tool for Optimization of Multimodal Functions" (2010, Applied Soft Computing, 176 citations) and "Multimodal Optimization by Covariance Matrix Self-Adaptation Evolution Strategy with Repelling Subpopulations" (2017, Evolutionary Computation, 101 citations). His work has earned him the Australian Research Council Discovery Early Career Researcher Award (DECRA, 2023, AUD 329,278) for developing an efficient computational solver for complex engineering problems. Ahrari has also won numerous international competitions, such as the IEEE CEC and GECCO multimodal optimization competitions (2016, 2017, 2020, 2022) and ISCSO structural optimization competitions (2017, 2018). He chairs the IEEE CIS Task Force on Multi-modal Optimization, serves on the editorial board of Applied Soft Computing, and leads organization of major competitions including the GECCO 2024 Benchmarking Niching Methods for Multimodal Optimization.