A novel self-organizing quantum evolutionary algorithm for multi-objective optimization

In this study, a self-organizing quantum evolutionary algorithm for multi-objective optimization (MSQEA) is proposed. Because of the quantum dynamic mechanism all the subpopulations may move concurrently in a force-field until all of them reach their equilibrium states. We estimate the performance of algorithm. The efficiency of the approach has been illustrated by applying to 0/1 Multi-objective knapsack problems. The results show that MSQEA can yield improvement in solution quality.