PhD Fellowship position in Quantum-Inspired Evolutionary Algorithm for Multi-Objective Integrative Optimization
We are looking for a highly motivated individual to join as a PhD Fellow at Oslo Metropolitan We seek a highly motivated individual to join as a PhD Fellow at our Artificial Intelligence Group, Oslo Metropolitan University, Norway. This fully funded position focuses on Quantum-Inspired Evolutionary Algorithms. This is a fantastic opportunity to join our dynamic Artificial Intelligence research group and work on cutting-edge AI and quantum computing research.
Application Deadline: 20 January 2025
For more details about the position, including the benefits, application process and requirements, and to apply, please visit:
https://www.oslomet.no/en/work/job-openings/phd-fellowship-position-in-quantum-inspired-evolutionary-algorithm-for-multi-objective-integrative-optimization
Area of Research
The primary objective of this project is to formulate and implement a multi-objective quantum-inspired EAs (QEA) tailored specifically for classical computers, focusing on addressing the prevalent challenges in the domain of multi-objective integrative optimization (MIO) problems. Real-world optimization problems, prevalent in industries, are often complex, involving different interrelated optimization problems with multiple interconnected and conflicting objectives. Most of these involved independent optimization problems are interrelated, and combining them into a global integrative optimization problem is therefore necessary. This proposal considers formulating an MIO problem by combining k optimization problems, resulting in k objective functions. As a result, instead of seeking a single solution, the approach provides a set of alternatives (Pareto-optimal front) that reflect the trade-off between the objectives resulting from the MIO, allowing decision-makers to choose based on their preferences. This practical approach is expected to enhance the decision-making process in industries significantly.
Recently, the emergence of quantum-inspired EAs (QEAs) has opened up new avenues for enhancing the effectiveness of EAs by striking a better balance between exploration and exploitation. Drawing inspiration from quantum mechanics, QEAs integrate concepts such as superposition, quantum parallelism, entanglement, interference, coherence, and measurement into the existing EA framework. Recent advancements have underscored the significant advantages of QEAs over classical EAs, demonstrating success in solving complex NP-hard problems that were previously deemed computationally intractable for classical computers. However, existing QEAs are typically designed for single optimization problems and exhibit optimal efficiency on specialized quantum hardware rather than classical computers. They also encounter challenges in maintaining coherence and leveraging entanglement for efficient exploration, necessitating further exploration of quantum operators and encoding schemes that can adapt to diverse problem structures and objective functions.
This project aims to bridge this gap by developing a novel multi-objective QEA that is specifically designed for classical computing environments. By utilizing quantum-inspired techniques, the objective is to provide industries with a practical and efficient solution for tackling real-world complex multi-objective optimization challenges in areas such as manufacturing and logistics.
Contact: If you would like more information about the position, feel free to contact the Principal Supervisor: Kazi Shah Nawaz Ripon, email: kazi.ripon[at]oslomet.no