Through the study of the quantum computing theory and binary coded quantum-inspired evolutionary algorithm, a new real-coded quantum-inspired evolutionary algorithm mode-double parameters real-coded quantum-inspired evolutionary algorithm mode is proposed.When it is applied to the genetic algorithm,a double parameter real-coded quantum genetic algorithm(DRQEA) is obtained.As a result,the binary to decimal conversion and complicated calculation of quantum gate in the quantum bit coded quantum genetic algorithm(QEA) is avoided. Simulation results show that compared with QEA,DRQEAhas lower time complexity,more global search ability which confirms the effectiveness of double parameters real-coded mode.
Through the study of the quantum computing theory and binary coded quantum-inspired evolutionary algorithm, a new real-coded quantum-inspired evolutionary algorithm mode-double parameters real-coded quantum-inspired evolutionary algorithm mode is proposed.When it is applied to the genetic algorithm,a double parameter real-coded quantum genetic algorithm(DRQEA) is obtained.As a result,the binary to decimal conversion and complicated calculation of quantum gate in the quantum bit coded quantum genetic algorithm(QEA) is avoided. Simulation results show that compared with QEA,DRQEAhas lower time complexity,more global search ability which confirms the effectiveness of double parameters real-coded mode.
When quantum inspired evolution algorithm(QEA)is used for the optimization of continuous functions with many local optima,it's easy to be trapped into the local deceptive optima.In this paper,a new hybrid quantum evolution algorithm is proposed to overcome the shortcoming of the QEA.The new hybrid QEA combines the merits of classic GA and QEA by using double coding mechanism(classic binary coding and quantum probabilistic coding),and combining the classical crossover and quantum probabilistic search.It not only has the global searching capacity of classic GA,but also improves the local searching capacity of algorithm by using quantum probabilistic search.Experiments on 23 test functions of diverse complexities are implemented and compared with QEA in this paper.The result indicates that the new hybrid QEA is better than QEA in both quality of final result and the convergence rate.
This paper proposes a novel evolutionary algorithm inspired by quantum computing, called a quantum-inspired evolutionary algorithm (QEA), which is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. Like other evolutionary algorithms, QEA is also characterized by the representation of the individual, evaluation function, and population dynamics. However, instead of binary, numeric, or symbolic representation, QEA uses a Q-bit, defined as the smallest unit of information, for the probabilistic representation and a Q-bit individual as a string of Q-bits. A Q-gate is introduced as a variation operator to drive the individuals toward better solutions. To demonstrate its effectiveness and applicability, experiments were carried out on the knapsack problem, which is a well-known combinatorial optimization problem. The results show that QEA performs well, even with a small population, without premature convergence as compared to the conventional genetic algorithm.
We investigate two modified Quantum Evolutionary methods for solving real value problems. The Quantum Inspired Evolutionary Algorithms (QIEA) were originally used for solving binary encoded problems and their signature features follow superposition of multiple states on a quantum bit and a rotation gate. In order to apply this paradigm to real value problems, we propose two quantum methods Half Significant Bit (HSB) and Stepwise Real QEA (SRQEA), developed using binary and real encoding respectively, while keeping close to the original quantum computing metaphor. We evaluate our approaches against sets of multimodal mathematical test functions and real world problems, using five performance metrics and include comparisons to published results. We report the issues encountered while implementing some of the published real QIEA techniques. Our methods focus on introducing and implementing new rotation gate operators used for evolution, including a novel mechanism for preventing premature convergence in the binary algorithm. The applied performance metrics show superior results for our quantum methods on most of the test problems (especially for the more complex and challenging ones), demonstrating faster convergence and accuracy.
Quantum evolutionary algorithm is a novel and efficient algorithm which inspired by the ideas of quantum computing,and it has excellent results on the computational efficiency and avoid falling into local extremum problems.Therefore,the combination of quantum mechanics and intelligent optimization algorithms will further expand the application areas of intelligent optimization algorithms to improve the optimization algorithm problem-solving ability.For this,the introduction of quantum computation to the differential evolution algorithm,we proposed a new evolutionary algorithm-quantum differential evolution algorithm.This method is the probability of quantum bits used in the real-coded chromosomes,with the quantum mutation,quantum crossover and quantum choose operation to achieve the update of chromosomal location,and using the quantum NOT gate to swap two probability amplitude of quantum bits,it can prevent the premature algorithm,while the algorithm converges faster.The function extreme value and TSP problems as an example to verify the effectiveness of the proposed algorithm.
This paper proposed a real-coded chaotic quantum-inspired genetic algorithm(RCQGA) based on the chaotic and coherent characters of Q-bits.In this algorithm,real chromosomes are inversely mapped to Qbits in the solution space.Q-bits probability guided real cross and chaos mutation are used to real chromosomes evolution and searching;Simulation shows that the proposed RCQGA not only avoids the shortcoming of binary system coding based QGA prematurity but it also reduces the optimizing complexity with faster convergence speed,more stability,more powerful optimizing ability.
In this paper, some guidelines for setting the parameters of quantum-inspired evolutionary algorithm (QEA) are presented. QEA is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. However, QEA is not a quantum algorithm, but a novel evolutionary algorithm. Like other evolutionary algorithms, QEA is also characterized by the representation of the individual, the evaluation function, and the population dynamics. From recent research on the knapsack problem, the results of QEA are better than those of CGA (conventional GA). Although the performance of QEA is excellent, there is relatively little or no research on the effects of different settings for its parameters. This paper describes some guidelines for setting these parameters. The guidelines are drawn up based on extensive experiments carried out for a class of combinatorial and numerical optimization problems. Through the guidelines, the performance of QEA can be maximized.