The current quantum evolution algorithms based on the Bloch spherical coordinates have slow convergence rate and poor robustness.Aiming at the two shortages,a new self-adaptive Quantum Genetic Algorithm(QGA) which is based on the characteristic of Fibonacci sequence is proposed.In the process of searching the optimal solution,a self-adaptive factor A is introduced to reflect the relative change rate which is 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.It is constructed the rule of updating the rotation angle △_φ and △θ which is based on Fibonacci sequence by studying its properties.Using the new algorithm to deal with the multidimensional complex functions,theoretical analysis of algorithm time complexity and the simulation results show that the new algorithm improves the convergence rate,efficiency and stability robustness.
The current quantum evolution algorithms based on the Bloch spherical coordinates have slow convergence rate and poor robustness.Aiming at the two shortages,a new self-adaptive Quantum Genetic Algorithm(QGA) which is based on the characteristic of Fibonacci sequence is proposed.In the process of searching the optimal solution,a self-adaptive factor A is introduced to reflect the relative change rate which is 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.It is constructed the rule of updating the rotation angle △_φ and △θ which is based on Fibonacci sequence by studying its properties.Using the new algorithm to deal with the multidimensional complex functions,theoretical analysis of algorithm time complexity and the simulation results show that the new algorithm improves the convergence rate,efficiency and stability robustness.
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 make full use of the quantum characteristics of the quantum state in the algorithm,and improve the search efficiency,reduce storage space,a new quantum genetic algorithm called 3D-AQGA was proposed.The algorithm describes quantum bit as a pair of angles in 3D spherical coordinate,makes full use of the quantum space motion characteristics,and introduces a kind of adaptive scheme to calculate the rotation angle size and direction which not only makes the process of chromosome's update and variation simplified,but also improves quantum characteristics,storage properties and time performance of the algorithm greatly.The simulation results show that the efficiency of the algorithm and the search ability are superior to the simple genetic algorithm and common quantum genetic algorithm.
In order to save computation time and avoid falling into local optima in quantum genetic algorithm (QGA), this paper proposes a quantum genetic algorithm based on Boltzmann selection mechanism (B_QGA). The proposed algorithm has several novel characteristics. Firstly, Quantum bits are encoded by angle and introduce the constant factor, aiming at enhancing calculated amount. Secondly, a new dynamic rotation angle adjustment strategy is presented to achieve updated chromosome, and individuals directly evolve to the current optimal solution with linear increasing rate, which greatly improve the rate of convergence and the ability of global search. Thirdly, instead of roulette selection mode, Boltzmann selection mechanism is employed for avoiding the local optimal problem. Finally, for the selected offspring's individuals, Hadamard door is proposed to perform mutation operation to sustain stability and diversity of population. The experiment results on three complicated continuous functions show the proposed algorithm is able to find the best solution in very short time and has excellent global search ability comparing to quantum genetic algorithm (QGA) and genetic algorithm (GA).
Intelligent optimization algorithm based on quantum behavior is the hot spot of intelligent computation. Quantum clone genetic algorithm has many shortcomings such as: low efficiency, poor population diversity, slow convergence speed, easy to trap in local minimums, blindness in global optimal searching direction and so on. An improved quantum clone genetic algorithm (IQCGA) is proposed in this paper. The algorithm have many merits: (1) Adopting the nicking probability division initialization strategy, population diversity is enhanced; (2) Introducing the quantum whole interference crossover, the information is spread in the whole population, so it helps to avoid trapping in local minimums, and accelerates convergence speed; (3) Making use of adaptation strategy in quantum rotate gate updating, searching speed for optimal solution is accelerated; (4) In order to avoid premature and evolution stagnation, the superior individual whole crossover quantum catastrophe strategy is adopted, which helps the population to search the objective solution in different directions. IQCGA is composted of initialization stage and iteration evolution stage. Furthermore, theory analysis is given to prove the convergence. Finally, Simulation results prove that the algorithms can maintain the population diversity, reduce calculation complexity, accelerate searching speed for optimal solution, and avoid trapping in local minimums. So it has the better searching optimal ability and convergence speed.
Aiming at the issue that quantum genetic algorithm is not suitable for continuons function optimization,an improved quantum genetic algorithm is proposed.This algorithm directly compares the quantum chromosomes with the current best solution to determine the rotating angle of the rotating gate.Every individual in population evolves with different rates to complete local search and global search simultaneously.Mutation operation is used to prevent the premature convergence.After analyzing the algorithm and its global convergence,this algorithm is applied to solve the function extremum and to optimize the PID controller parameters,and it is compared with the standard genetic algorithm and the quantum genetic algorithm.Experimental results illustrate that the proposed algorithm has better performance.
Aiming at the problems that the Quantum Genetic Algorithm(QGA) easily falls into local extremum,an improved QGA algorithm of quantum revolving gate is proposed.In order to achieve the goal of optimization solving,quantum bit probability amplitude is applied to the chromosome encoding,and quantum rotation gate is used to chromosome updating.The theoretical derivation proves that the proposed algorithm converges with probability 1,and the algorithm converges strongly to 1?e.Comparing with double QGA,simulation results show that the algorithm increases complexity,the average time extension,but convergence times increase,convergence times of the function 1 increases from 3 to 7,and convergence times of the function 2 increases from 8 to 9.