Abstract 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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2010, Vol. 23 
