An adaptive quantum genetic algorithm based on Bloch sphere is proposed based on the quantum genetic algorithm which is based on Bloch coordinates of qubits.The algorithm uses two ways to select a part of the Bloch sphere for searching:along the warp direction and weft direction.The paper proves that the two methods are able to contain all the solutions of the continuous optimization problem in theory,and proposes a method of approximately equal-area to search the selected Bloch sphere,and derives the inverse relationship between the two-phase.The chromosomes mutation is modified to meet the restrictions of selected region.The simulation results show that the approach is equal to quantum genetic algorithm based on Bloch coordinates of qubits in search capability,but the optimization efficiency is significantly improved.
An adaptive quantum genetic algorithm based on Bloch sphere is proposed based on the quantum genetic algorithm which is based on Bloch coordinates of qubits.The algorithm uses two ways to select a part of the Bloch sphere for searching:along the warp direction and weft direction.The paper proves that the two methods are able to contain all the solutions of the continuous optimization problem in theory,and proposes a method of approximately equal-area to search the selected Bloch sphere,and derives the inverse relationship between the two-phase.The chromosomes mutation is modified to meet the restrictions of selected region.The simulation results show that the approach is equal to quantum genetic algorithm based on Bloch coordinates of qubits in search capability,but the optimization efficiency is significantly improved.
Due to the frequent decoding operations,the efficiency of optimization is severely reduced when the binary quantum genetic algorithm based on qubits measure is applied to the continuous space optimization.To solve this problem,a quantum genetic algorithm based on phase encoding is proposed.In this method,the chromosomes are encoded by the phase of qubits,evolved by quantum rotation gates,and mutated by quantum Pauli-Z gates.The optimization process is performed in[0,2π]~n,which has nothing to do with specific issues,therefore,the proposed method has good adaptability for a variety of optimization problems.In application of function extremum optimization,the simulation results show that the approach is superior to either common quantum genetic algorithm or simple genetic algorithm in both search capability and optimization efficiency.
The current quantum evolution algorithms have slow convergence rate and poor robustness. In order to overcome the two shortages, a novel self-adaptive quantum genetic algorithm is proposed. Firstly, the new algorithm adopts an encoding method which is based on the Bloch spherical coordinates. Secondly, in the process of searching the optimal solution, a self-adaptive factor is introduced to reflect the relative change rates which are relative to the difference of the best individual's objective fitness between the parent generation and the child generation. The convergence rate and direction of the algorithm can be improved by adjusting the factor. The rules of updating the rotation angle and are constructed. Finally, using hadamard gate of the quantum in the mutation strategy, it can enhance the diversity of population. The simulation results of the optimizing problem of the multidimensional complex functions show that the new algorithm has not only avoided effectively the premature and improved the convergence rate, but also boosted strikingly efficiency and stability robustness of the algorithm.
In order to solve the problems of optimization efficiency and inferior local search of quantum genetic algorithm whose coding is based on bloch coordinates,this paper proposes two improvements,combines local search with global search which is based on comparative population,and expands the search space from one sphere to three-sphere according to the characteristics of three chains.With the application of function extreme optimization of multi-variables,simulation results show that the improved algorithm has lower generation,higher efficiency,diverse populations,and it proves that the improvement is efficient.
Due to frequent decoding operations, the efficiency of optimization is severely reduced when the binary quantum genetic algorithm based on qubits measure is applied to the continuous space optimization.To solve this problem, a quantum genetic algorithm based on phase encoding is proposed.In this method, the chromosomes are encoded by the phase of qubits, evolved by quantum rotation gates, and mutated by quantum Pauli-Z gates.As the optimization process is performed in 0,2πn, which has nothing to do with specific issues, therefore, the proposed method has good adaptability for a variety of optimization problems.With application of function extremum optimization, the simulation results show that the approach is superior to common quantum genetic algorithm and simple genetic algorithm in both search capability and optimization efficiency.
Due to frequent decoding operations, the efficiency of optimization is severely reduced when the binary quantum genetic algorithm based on qubits measure is applied to the continuous space optimization. To solve this problem, a quantum genetic algorithm based on phase encoding is proposed in this paper. In this method, the chromosomes are encoded by the phase of qubits, evolved by quantum rotation gates, and mutated by quantum Pauli-Z gates. As the optimization process is performed in [0, 2π], which has nothing to do with specific issues, therefore, the proposed method has good adaptability for a variety of optimization problems. With application of function extremum optimization, the simulation results show that the approach is superior to common quantum genetic algorithm and simple genetic algorithm in both search capability and optimization efficiency.
Function optimization is an important problem in the fields of mathematics and computer science, with wide applications in various real-world domains. Quantum genetic algorithm is a global, parallel, and efficient search method used to solve function optimization and search problems. The Bloch Quantum Evolution Algorithm (BQEA) based on quantum bit Bloch sphere coordinates greatly expands the proportion of optimal solutions, but also expands the search space, making the algorithm prone to falling into local optima. This article proposes a new improved Bloch Elite Quantum Genetic Algorithm (BEQGA) by increasing the number of elite individuals and improving the optimization method of fixed rotation angles. Through multiple simulation experiments, it has been proven that BEQGA has a certain improvement in search ability compared to BQEA, and this has been validated in the research of key technologies for high-precision flexible bag feeding packaging equipment.