The method of calculating rotation angle of quantum rotation gate plays an important role to the performance of quantum evolutionary algorithm (QEA). This paper proposes an improved quantum evolutionary algorithm (IQEA), whose core is that a new approach of adaptive calculating rotation angle of quantum rotation gate is designed on the basis of the probability amplitude ratio of the corresponding states. Rapid convergence and good global search capability characterize the performance of IQEA. Based on a typical combinatorial optimization problem - 0/1 knapsack problems, the influence of the relative parameter to the performance of IQEA is demonstrated, and then comparing experiments have been done. The results show that IQEA is superior to the previous quantum evolutionary algorithm.
The method of calculating rotation angle of quantum rotation gate plays an important role to the performance of quantum evolutionary algorithm (QEA). This paper proposes an improved quantum evolutionary algorithm (IQEA), whose core is that a new approach of adaptive calculating rotation angle of quantum rotation gate is designed on the basis of the probability amplitude ratio of the corresponding states. Rapid convergence and good global search capability characterize the performance of IQEA. Based on a typical combinatorial optimization problem - 0/1 knapsack problems, the influence of the relative parameter to the performance of IQEA is demonstrated, and then comparing experiments have been done. The results show that IQEA is superior to the previous quantum evolutionary algorithm.
The method of calculating the rotational angle of a quantum rotary gate palys an important role in the performance of quantum evolutionary algorithm(QEA). An improved quantum evolutionary algorithm(IQEA) is proposed, which adaptively calculates the rotational angle of quantum rotary gate based on the probability amplitude ratio of the corresponding states. Rapid convergence and good global search capability characterize the performance of IQEA. The influence of the relative parameter to the performance of IQEA is demonstrated on 0/1 knapsack problem, and then IQEA is applied to a real distribution routing problem, the results show that IQEA is superior to the previous quantum evolutionary algorithm and other conventional evolutionary algorithms.
Based on the probability amplitude ratio of the corresponding states of quantum bits, the rotation angle of quantum rotation gate is calculated adaptively and a new quantum rotation gate is constructed. A novel quantum evolutionary algorithms (NQEA), whose core is that the designed quantum rotation gate is used as evolutionary strategy is introduced, rapid convergence and good global search capability characterize the performance of NQEA, and NQEA is able to possess the two characteristics of exploration and exploitation, simultaneously. NQEA is applied on the physical distribution vehicle routing problem - a typical combinatorial optimization problem, the results show that NQEA is superior to the previous quantum evolutionary algorithm as well as other conventional genetic algorithms
In this paper, Quantum Rotate Gate is improved, which is the main operation in the Population Update of the traditional Quantum Evolutionary Algorithm. A new rotation angle is defined, preventing the algorithm from easily falling into local optimum state in the middle and late term. Based on the characteristics of TSP, a modified quantum rotate gate is proposed in this paper to adaptively adjust the rotation angle, according to the evolution generations and the adapt to degree of the value to adaptive dynamic adjustment of the rotation angle, resulting a better global search capability. At the same time, in order to prevent the extramalization of the probability amplitudes and falling into local optimal algorithm, this paper adopted the H ε gate on the probability amplitude of the rotation to make the corrective manipulation. The comparative experimental results showed that the algorithm's stability and accuracy have been greatly improved in solving the TSP problem, compared with the conventional quantum evolutionary algorithm.
In this paper, quantum rotate gate is improved, which is the main operation in the population update of the traditional quantum evolutionary algorithm. A new rotation angle is defined, preventing the algorithm from easily falling into local optimum state in the middle and late term. Based on the characteristics of TSP, a modified quantum rotate gate is proposed in this paper to adaptively adjust the rotation angle, according to the evolution generations and the adapt to degree of the value to adaptive dynamic adjustment of the rotation angle, resulting a better global search capability. At the same time, in order to prevent the extramalisation of the probability amplitudes α and β falling into local optimal algorithm, this paper adopted the Hε gate on the probability amplitude of the rotation to make the corrective manipulation. The comparative experimental results showed that the algorithm's stability and accuracy have been greatly improved in solving the TSP problem, compared with the conventional quantum evolutionary algorithm.
In this paper, quantum rotate gate is improved, which is the main operation in the population update of the traditional quantum evolutionary algorithm. A new rotation angle is defined, preventing the algorithm from easily falling into local optimum state in the middle and late term. Based on the characteristics of TSP, a modified quantum rotate gate is proposed in this paper to adaptively adjust the rotation angle, according to the evolution generations and the adapt to degree of the value to adaptive dynamic adjustment of the rotation angle, resulting a better global search capability. At the same time, in order to prevent the extramalisation of the probability amplitudes α and β falling into local optimal algorithm, this paper adopted the Hε gate on the probability amplitude of the rotation to make the corrective manipulation. The comparative experimental results showed that the algorithm's stability and accuracy have been greatly improved in solving the TSP problem, compared with the conventional quantum evolutionary algorithm.
According to the characteristics of combinatorial optimization problems,a novel improved quantum evolutionary algorithm for combinatorial optimization problems(NIQEA) is proposed.The NIQEA initializes population based on the idea of niche cooperative evolutionary,increasing the diversity of individuals,and adopts the dynamic adjustment strategy to adjust the quantum rotation corner,speeding up convergence rate,and uses the cross strategy of excellent individuals to get crossover operation,enhancing local search ability.To validate its performance,the NIQEA is applied to solve the typical combinatorial optimization problems: two examples of multidimensional 0/1 knapsack problem,and the results show that the NIQEA is feasible and effective.