Parallel Improved Quantum Evolutionary Algorithm for Complex Optimization Problems

Jincheng Zhang

This paper explores the integration of quantum computation with evolutionary algorithms to accelerate the search for optimal solutions to complex optimization problems. We propose a novel hybrid framework that utilizes quantum circuits to enhance the exploration of the solution space, enabling parallel processing and potentially dramatically improving convergence rates compared to traditional evolutionary algorithms. The core mechanism focuses on leveraging superposition and entanglement to represent and manipulate the solution space, leading to a significant acceleration of optimization processes. The potential impact of this approach is substantial for tackling challenging problems across diverse domains, including logistics, finance, and materials science.

Maurizio Fiasché

André Vargas Abs da Cruz, Carlos Roberto Hall Barbosa, Marco Aurélio C. Pacheco +1 more

Yongqiang Zhang, Guihong Li

This paper combines quantum evolutionary algorithm with the available evolutionary algorithms, uses quantum computing parallelism to search solution space efficiently, uses the traditional evolutionary algorithm to optimize the result observed by quantum population and then uses the optimized result to guide the generation of some individuals of quantum population. It maintains the population diversity of quantum evolutionary algorithm and avoids premature. Meanwhile it adopts the different evolutionary algorithms to combine with quantum evolutionary algorithm to solve different problems, which will improve the adaptability of algorithm.

Jincheng Zhang

This paper introduces the Adaptive Quantum Evolutionary Algorithm (AQEA), a novel quantum evolutionary algorithm designed to optimize complex systems by dynamically adjusting parameter and structural configurations. Traditional evolutionary algorithms often lack inherent adaptability, while AQEA leverages quantum evolution to achieve automated optimization and adaptation. The core mechanism involves a quantum evolution process that iteratively refines system parameters and structures based on the system's state, resulting in improved performance and robustness. This paper details the algorithm's architecture, key operational steps, and preliminary results demonstrating its effectiveness in a simulated optimization problem. The core claim is that AQEA offers a significant improvement in optimization efficiency and adaptability compared to existing evolutionary algorithms.

Bin Cao, Shanshan Fan, Jianwei Zhao +3 more