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Convergence performance comparison of quantum-inspired multi-objective evolutionary algorithms

机译:量子启发式多目标进化算法的收敛性能比较

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摘要

In recent research, we proposed a general framework of quantum-inspired multi -objective evolutionary algorithms (QMOEA) and gave one of its sufficient convergence conditions to the Pareto optimal set. In this paper, two Q-gate operators, H_e gate and R&N_C gate, are experimentally validated as two Q-gate paradigms meeting the convergence condition. The former is a modified rotation gate, and the latter is a combination of rotation gate and NOT gate with the specified probability. To investigate their effectiveness and applicability, several experiments on the multi-objective 0/1 knapsack problems are carried out. Compared to two typical evolutionary algorithms and the QMOEA only with rotation gate, the QMOEA with H_e gate and R&N_e gate have more powerful convergence ability in high complex instances. Moreover, the QMOEA with R&N_e gate has the best convergence in almost all of the experimental problems. Furthermore, the appropriate ε value regions for two Q-gates are verified.
机译:在最近的研究中,我们提出了一个由量子启发的多目标进化算法(QMOEA)的通用框架,并为Pareto最优集合提供了其充分的收敛条件之一。在本文中,两个Q门运算符H_e gate和R&N_C gate被实验验证为满足收敛条件的两个Q门范例。前者是修改后的旋转门,而后者是具有指定概率的旋转门和非门的组合。为了研究其有效性和适用性,对多目标0/1背包问题进行了一些实验。与两种典型的进化算法和仅具有旋转门的QMOEA相比,具有H_e门和R&N_e门的QMOEA在高复杂情况下具有更强大的收敛能力。此外,带有R&N_e门的QMOEA在几乎所有实验问题中都具有最佳收敛性。此外,验证了两个Q门的适当ε值区域。

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