Quantum-Inspired Distributed Memetic Algorithm Guanghui Zhang*, Wenjing Ma, Keyi Xing, Lining Xing, and Kesheng Wang Abstract: This paper proposed a novel distributed memetic evolutionary model, where four modules distributed exploration, intensified exploitation, knowledge transfer, and evolutionary restart are coevolved to maximize their strengths and achieve superior global optimality. Distributed exploration evolves three independent populations by heterogenous operators. Intensified exploitation evolves an external elite archive in parallel with exploration to balance global and local searches. Knowledge transfer is based on a point-ring communication topology to share successful experiences among distinct search agents. Evolutionary restart adopts an adaptive perturbation strategy to control search diversity reasonably. Quantum computation is a newly emerging technique, which has powerful computing power and parallelized ability. Therefore, this paper further fuses quantum mechanisms into the proposed evolutionary model to build a new evolutionary algorithm, referred to as quantum-inspired distributed memetic algorithm (QDMA). In QDMA, individuals are represented by the quantum characteristics and evolved by the quantum-inspired evolutionary optimizers in the quantum hyperspace. The QDMA integrates the superiorities of distributed, memetic, and quantum evolution. Computational experiments are carried out to evaluate the superior performance of QDMA. The results demonstrate the effectiveness of special designs and show that QDMA has greater superiority compared to the compared state-of-the-art algorithms based on Wilcoxon’s rank-sum test. The superiority is attributed not only to good cooperative coevolution of distributed memetic evolutionary model, but also to superior designs of each special component. Key words: distributed evolutionary algorithm; memetic algorithm; quantum-inspired evolutionary algorithm; quantum distributed memetic algorithm 1 Introduction Although quantum computation is a newly emerging technique, it has achieved widespread attention from the scientific community due to its powerful computing power and outstanding parallelized ability in tackling various specialized problems [1−3] . So far, many studies on quantum computation have progressed actively, especially in the design of quantum algorithms. In the existing quantum algorithms, one of the main design ideas is to integrate superior characteristics of quantum computation into the architecture of some specific evolutionary algorithm (EA). Such algorithms are referred to as quantum EA (QEA). Compared to traditional EAs, QEA is characterized by principles of quantum computation such as concepts of qubits and superposition of states. Based on qubit representation and evolution, QEA can imitate the quantum computation process to achieve strong competitiveness • Guanghui Zhang is with the School of Information Science and Technology and the Hebei Key Laboratory of Agricultural Big Data, Hebei Agricultural University, Baoding 071001, China. E-mail: zhangguanghui@stu.xjtu.edu.cn. • Wenjing Ma is with the School of Information Science and Technology, Hebei Agricultural University, Baoding 071001, China. E-mail: wjma2022@hotmail.com. • Keyi Xing is with the State Key Laboratory for Manufacturing System Engineering and the Systems Engineering Institute, Xi’an Jiaotong University, Xi’an 710049, China. E-mail: kyxing@mail.xjtu.edu.cn. • Lining Xing is with the School of Electronic, Xidian University, Xi’an 710071, China. E-mail: lnxing@xidian. edu.cn. • Kesheng Wang is with the Department of Production and Quality Engineering, Norwegian University of Science and Technology, Trondheim 7491, Norway. E-mail: kesheng. wang@ntnu.no. * To whom correspondence should be addressed. Manuscript received: 2022-08-28; revised: 2022-10-02; accepted: 2022-10-13 COMPLEX SYSTEM MODELING AND SIMULATION ISSN 2096-9929 05/06 pp 334−353 Volume 2, Number 4, December 2022 DOI: 10.23919/CSMS.2022.0021 © The author(s) 2022. The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).