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Distribution state estimation (DSE) is becoming more and more important in distribution control center considering deregulation and introduction of renewable sources based distributed generations (REDGs). To realize the real-time estimation of the load and REDGs output values with limited monitoring devices in a distribution network, this paper presents a novel DSE approach by introducing a Modified Quantum-Inspired Evolutionary Algorithm (mQIEA) combining a QIEA with Greedy Randomized Adaptive Search Procedures (GRASP). Thirteen knapsack problems, which are well-known as NP-hard ones, with various items are used to compare mQIEA with four QIEA variants. IEEE34 and IEEE70 test systems with REDGs are applied to conduct DSE experiments. Results show that mQIEA outperforms four QIEA variants in terms of the solution quality and that mQIEA obtains better estimation results than eight optimization algorithms reported in the recent literature with respect to the maximally relative error, maximally absolute error, and estimation precision. [ABSTRACT FROM AUTHOR]