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中文摘要：
量子进化算法采用多个简单概率模型并行搜索的框架结构，从而可尝试引入有效的多模型学习机制以提高算法的探索能力．文中将全面学习的思想引入多量子概率模型的学习，提出基于全面学习的量子分布估计算法．在该算法中，模型的每个分量都可以向不同的目标学习，使得量子概率模型有可能较为全面地从已知较优解中提取知识，以尽可能全面地描述解空间中好的区域，有效提高算法求解复杂优化问题的能力．在典型0-1背包问题上的比较实验充分验证该算法的有效性和先进性．
英文摘要：
Quantum Inspired Evolutionary Algorithm （QIEA） adopts the framework that multiple simple probabilistic models parallel explore and it can improve its exploration ability consequently by introducing more effective multi-model learning mechanism. In this paper, the idea of comprehensive learning is introduced into the learning of multiple quantum probabilistic models, and Comprehensive Learning Quantum-inspired Estimation of Distribution Algorithm （CLQEDA） is proposed. In CLQEDA, the comprehensive learning is implemented by allowing each component of model to learn from different target solutions. It makes the quantum probabilistic model possible to relatively comprehensively extract knowledge from the known better solutions, then as comprehensively as possible to describe the good area in solution space and effectively improve the performance of the algorithm on complicated optimization problems. The advance and effectiveness of CLQEDA are testified via comparison experiments on classical 0-1 knapsack problems.
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