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Journal of Southwest Jiaotong University

Quantum Evolution Algorithm Based on Descending Search

MA Shu-xia

Published Jan 1, 2004• 1 citation
OpenAlex

Abstract

To raise global search capacity and convergent speed, a new evolution algorithm, based- descending search quantum evolution algorithm, was put forward on the basis of the quantum evolution algorithm (QEA) and the hybrid genetic algorithm. In the proposed algorithm, the descending search theory of optimization principles is applied, so the iterative effect, only relying on quantum gate, of QEA is improved to speed up the convergent speed, and the possibility of individual retrogression in the evolution process is reduced. The simulation result of a typical function shows that this algorithm has a good convergence performance and global search capacity.

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To raise global search capacity and convergent speed, a new evolution algorithm, based- descending search quantum evolution algorithm, was put forward on the basis of the quantum evolution algorithm (QEA) and the hybrid genetic algorithm. In the proposed algorithm, the descending search theory of optimization principles is applied, so the iterative effect, only relying on quantum gate, of QEA is improved to speed up the convergent speed, and the possibility of individual retrogression in the evolution process is reduced. The simulation result of a typical function shows that this algorithm has a good convergence performance and global search capacity.
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.
New methods are joined into the quantum genetic algorithm to solve the defects of poor local search ability and more iterative times.The algorithm is the core of a new quantum rotation gate adjustment strategy,which updates the population,so it has better diversity than the classical genetic algorithm,rapid convergence and good global search capacity characterize the performance of the quantum genetic algorithm.According to the test of typical complex function,the results show that the algorithm to the quality of opti-mization and efficiency are superior to traditional genetic algorithm and general quantum genetic algorithm.
In this paper, a novel quantum genetic algorithm is proposed. This algorithm compares the probability expectation of the quantum chromosome with the best binary solution to determine rotation angle of rotation gate. Different individual in population evolve with different rate to complete local search and global search simultaneously. Hε gate is used to prevent the algorithm from premature convergence. After analyzing the algorithm and its global convergence, applying this approach to the optimization of function extremum, and comparing with the simple genetic algorithm and the quantum genetic algorithm, the simulation result illustrates that the algorithm has the characteristic of quick convergence speed and high solution precision.
The genetic algorithm is an efficient optimization tool for its independence of problems, intrinsic parallelism and inherent learning capacity; however, it has some disadvantages such as slow convergence and prematurity. In this paper, a novel algorithm, called the quantum genetic algorithm—QGA, is proposed based on the combination of the quantum theory with the evolutionary theory. By adopting the qubit chromosome as a representation, QGA can represent a linear superposition of solutions due to its probabilistic representation. The random observation simulating the quantum collapse can bring diverse individuals, and the evolution of quantum chromosome can also pilot the evolution. So it has better diversity than the classical genetic algorithm. Rapid convergence and good global search capacity characterize the performance of QGA. The paper not only proves the global convergence of the QGA, but also some simulation experiments are shown to prove its superiority over other algorithms.
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.
The quantum genetic algorithm (QGA) is derived from the integration of quantum computation and genetic algorithm. It is characterized with advantages like strong global optimization ability, fast convergence speed and small population size. In this paper, the principle, method and basic flow of quantum genetic algorithm (QGA) are introduced, and the research progress of QGA in recent years is reviewed, including the coding extension of theoretical basis, the innovation of operators, the rotation angle of quantum gates, the optimization of complex high-dimensional functions, and hybrid algorithms, and then he application status of QGA is also discussed. Finally, the future development direction of QGA is put forward.