Quantum-Inspired Evolutionary Algorithm Based on Estimation Of Distribution

This paper proposed a novel quantum inspired evolutionary algorithm (called EQEA) based on estimation of distribution. In EQEA, the solution made from distribution model of candidate solutions collapsing from quantum probabilistic model is called estimated solution. Combining with group strategy and adaptive rotation gate, quantum probability amplitude is updated referring to the corresponding estimated solution and the best solution in its group. Rapid convergence and good global search capability characterize the performance of EQEA even if the population size is 1. Experiments on solving a class of combinatorial optimization problems show that EQEA performs better than QEA and EDA.