A Quantum-Inspired Optimization Framework for Engineering Decision Support Systems
Year: 2026
Type: conference-paper
Abstract: Purpose: The development of engineering decision support systems requires effective optimization techniques to deal with complex optimization problems such as energy management, logistics optimization, and resource management. The most popular classical optimization techniques, such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), are often less effective in terms of optimization performance and are often likely to suffer from slow optimization speed and premature convergence to local optima when dealing with highdimensional optimization search space. The quantum-inspired optimization (QIO) techniques are an emerging class of optimization techniques that attempt to mimic some principles of quantum computing on classical computers to overcome the limitations of classical optimization techniques. The purpose of this research is to develop a quantum-inspired optimization framework to overcome the limitations of classical optimization techniques in engineering decision support systems. Method: The proposed optimization framework incorporates a probabilistic amplitude representation of solutions, rotation-based evolutionary optimization, and simulated annealing acceptance criteria. The optimization framework was tested on two optimization scenarios: a highdimensional optimization problem inspired by a Rastrigin-like function representing energy management optimization and a multi-objective optimization problem inspired by logistics optimization with the goal of minimizing transportation costs and time. The optimization performance and effectiveness of the proposed optimization framework were compared with classical optimization techniques such as GA and PSO. Findings: The proposed optimization framework outperforms traditional methods such as GA and PSO in terms of speed and quality of results. In the high-dimensional problem, the proposed framework achieved an optimal value of 38.96, which is better compared to the results of PSO, which achieved 69.42, and GA, which achieved 137.22. In the multi-objective problem, the proposed framework achieved Pareto results, which show better performance and effectiveness compared to the traditional methods. Conclusion: The optimization results indicate that the quantum-inspired optimization framework is an effective optimization tool that can be used to overcome the limitations of classical optimization techniques in engineering decision support systems. The proposed optimization framework provides a link between classical optimization techniques and emerging quantum optimization techniques while being able to run on classical computers.
Authors: Guler, Ferdi, Selamoglu, Batuhan
Institution: Istanbul Arel University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7172494826