Abstract
Multiscale quantum harmonic oscillator algorithm (MQHOA) is an optimization algorithm based on quantum behavior. It transforms the function optimization into solving the ground state wave function through the transformation of SchrOdinger equation and utilizes the processes of energy level stabilization, energy level transition and scale adjustment to search the optimal solution of the objective function. Original MQHOA algorithm accelerates the search process by replacing the worst solution with the mean value in the process of energy level transition. This operation may reduce diversity and lead to prematurity. In order to improve the population diversity of the probes, special elite strategy and random replacement mechanism are used to improve the search efficiency. Experiments on benchmark test functions show that the improved algorithm is more effective than the original algorithm and other common algorithms.
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