Quantum-inspired Multi-objective Evolutionary Algorithm Research And Its Application

Quantum Inspired Evolutionary Algorithm (QEA) is a type of evolutionary algorithm based on principles of Quantum Computing (QC). Based on the concepts and principles of QC, QEA uses Q-bit string as the probabilistic representation of solutions and Q-gates as variation operators to drive evolutionary searching. Compared with conventional EAs, QEA can balance between exploration and exploitation better. Additionally it is characterized by small population size, rapid convergence and strong global search capability.As what the research results show, normal QEA has better performances than traditional EAs on many problems, but it is weak in solving some complex optimization problems because it tends to run into local optima, especially applying to MOP. Hereby, a novel Quantum-inspired Multi-objective Evolutionary Algorithm (QMEA) is proposed based on the R&N-epsilon gate. In order to get more efficient convergence, the major work of this paper is to improve the rotation gate. Integrating schemas of population coding by Q-bit string and several excellent strategies in evolutionary multi-objective optimization, we construct a new algorithm for solving MOP, which has small evolutionary population size, and more efficiently convergence. On the point of theory, we prove the convergence of the algorithm based on the partial set theory and probability theory. Meanwhile, a series of experiment results on the 0/1 knapsack problem show that QMEA can be more close to the Pareto front in same running time, and its Pareto solutions has better distribution.On the point of application, we applied the algorithm to the water resource allocation. Based on QMEA, a reasonable method has been proposed. Its application for water resource allocation in a area shows that the proposed method is feasible.On the point of application, we applied the algorithm to the water resource allocation. Based on QMEA, a reasonable method has been proposed. Its application for water resource allocation in Changsha, Xiangtan and Zhuzhou area shows that the proposed method is feasible.