Q_escos: A Quantum-Inspired Evolutionary Algorithm for Complex Optimization Problems

This paper introduces a novel evolutionary algorithm called Q_escos, which is inspired by quantum mechanics principles to enhance the performance of traditional evolutionary algorithms. This algorithm integrates Quantum Superposition Alignment and Quantum-Inspired Simulation mechanisms to improve convergence speed and reduce the risk of getting trapped in local optima. By leveraging quantum encoding and adaptive mutation and selection operators, Q_escos creates a more optimal distribution of candidate solutions, leading to improved accuracy and stability in the search process. In this study, we evaluate the performance of Q_escos on various optimization problems, including multi-modal optimization, standard benchmark functions, and real-world engineering applications. Experimental results demonstrate that Q_escos outperforms conventional evolutionary approaches such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE) in terms of accuracy, convergence speed, and robustness against local optima. Statistical analysis further confirms that due to its quantum-inspired mechanisms, Q_escos can provide superior performance in complex optimization problems compared to classical methods. These findings highlight the potential of Q_escos for applications in industrial design optimization, artificial intelligence, and combinatorial problem-solving.

کلیدواژه ها:

Quantum Evolutionary Algorithm ، Optimization ، Quantum Superposition ، Adaptive Mutation ، Evolutionary Computation ، Q_escos.