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Abolfazl Shirazi


I am a Postdoctoral Researcher at BCAM - Basque Center for Applied Mathematics. In 2016, I was awarded the La Caixa Fellowship Grant for my doctoral studies and in 2021 I obtained my Ph.D. degree with “Sobresaliente Cum Laude” distinction award from the University of the Basque Country UPV/EHU. My research interests are Astrodynamics and Machine Learning and my activities mainly involve spacecraft trajectory optimization, evolutionary computations, space dynamics and control, numerical simulation, orbital mechanics, meta-heuristics, and software development for space simulation.


Orbital Mechanics

Orbit Propagation
Orbital Maneuvers
Trajectory Optimization
Interplanetary Transfers

Machine Learning

Continuous Optimization
Evolutionary Algorithms

Spacecraft Dynamics

Spacecraft Guidance
Satellite Attitude Control
Space Mission Analysis
G.N.C Systems


3D Visualization
Software Development
3D Design & Modeling
Realtime Simulation

Book Chapter

Time-Varying Lyapunov Control Laws with Enhanced Estimation of Distribution Algorithm for Low-Thrust Trajectory Design
Shirazi, A., Holt, H., Armellin, R. and Baresi, N.
Modeling and Optimization in Space Engineering (2023)
DOI: 10.1007/978-3-031-24812-2_14
Enhancements in evolutionary optimization techniques are rapidly growing in many aspects of engineering, specifically in astrodynamics and space trajectory optimization and design. In this chapter, the problem of optimal design of space trajectories is tackled via an enhanced optimization algorithm within the framework of Estimation of Distribution Algorithms (EDAs), incorporated with Lyapunov and Q-law feedback control methods. First, both a simple Lyapunov function and a Q-law are formulated in Classical Orbital Elements (COEs) to provide a closed-loop low-thrust trajectory profile. The weighting coefficients of these controllers are approximated with various degrees of Hermite interpolation splines. Following this model, the unknown time series of weighting coefficients are converted to unknown interpolation points. Considering the interpolation points as the decision variables, a black-box optimization problem is formed with transfer time and fuel mass as the objective functions. An enhanced EDA is proposed and utilized to find the optimal variation of weighting coefficients for minimum-time and minimum-fuel transfer trajectories. The proposed approach is applied in some trajectory optimization problems of Earth-orbiting satellites. Results show the efficiency and the effectiveness of the proposed approach in finding optimal transfer trajectories. A comparison between the Q-law and simple Lyapunov controller is done to show the potential of the potential of the EEDA in enabling the simple Lyapunov controller to recover the finer nuances explicitly given within the analytical expressions in the Q-law.

Featured Articles

EDA++: Estimation of Distribution Algorithms with Feasibility Conserving Mechanisms for Constrained Continuous Optimization
Shirazi, A., Ceberio, J. and Lozano, J.A.
IEEE Transactions on Evolutionary Computation (2022)
DOI: 10.1109/TEVC.2022.3153933
Handling non-linear constraints in continuous optimization is challenging, and finding a feasible solution is usually a difficult task. In the past few decades, various techniques have been developed to deal with linear and non-linear constraints. However, reaching feasible solutions has been a challenging task for most of these methods. In this paper, we adopt the framework of Estimation of Distribution Algorithms (EDAs) and propose a new algorithm (EDA++) equipped with some mechanisms to deal with non-linear constraints. These mechanisms are associated with different stages of the EDA, including seeding, learning and mapping. It is shown that, besides increasing the quality of the solutions in terms of objective values, the feasibility of the final solutions is guaranteed if an initial population of feasible solutions is seeded to the algorithm. The EDA with the proposed mechanisms is applied to two suites of benchmark problems for constrained continuous optimization and its performance is compared with some state-of-the-art algorithms and constraint handling methods. Conducted experiments confirm the speed, robustness and efficiency of the proposed algorithm in tackling various problems with linear and non-linear constraints.

Spacecraft trajectory optimization: A review of models, objectives, approaches and solutions
Shirazi, A., Ceberio, J. and Lozano, J.A.
Progress in Aerospace Sciences 102 (2018): 76-98
DOI: 10.1016/j.paerosci.2018.07.007
This article is a survey paper on solving spacecraft trajectory optimization problems. The solving process is decomposed into four key steps of mathematical modeling of the problem, defining the objective functions, development of an approach and obtaining the solution of the problem. Several subcategories for each step have been identified and described. Subsequently, important classifications and their characteristics have been discussed for solving the problems. Finally, a discussion on how to choose an element of each step for a given problem is provided.

  • ashirazi@bcamath.org
  • ashirazi@homasim.com
  • Tel: +34 946 567 842
  • Fax: +34 946 567 843
    Mazarredo, 14, 48009
    Bilbao, Spain
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