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Improved Hybrid Quantum Genetic Algorithm

Xi Wang

Published Jan 1, 2008• 0 citations
OpenAlex

Abstract

This paper proposes an Improved Hybrid Quantum Genetic Algorithm(IHQGA).First,the quantum crossover is used which can maintain the relatively good gene blocks.Second,the strategies of updating quantum gate using qubit phase approach and adjusting search grid adaptively are introduced.Third,the similar Newton method is introduced as a local searching scheme,which is characterized by rapid convergence,good global searching capability and short computing time.Test results of complex functions and application example demonstrate that the algorithm is superior to conventional genetic algorithms and quantum genetic algorithm in quality and efficiency.

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This paper proposes an Improved Hybrid Quantum Genetic Algorithm(IHQGA).First,the quantum crossover is used which can maintain the relatively good gene blocks.Second,the strategies of updating quantum gate using qubit phase approach and adjusting search grid adaptively are introduced.Third,the similar Newton method is introduced as a local searching scheme,which is characterized by rapid convergence,good global searching capability and short computing time.Test results of complex functions and application example demonstrate that the algorithm is superior to conventional genetic algorithms and quantum genetic algorithm in quality and efficiency.
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.
Quantum computing is a new interdisciplinary science combining information science and quantum mechanics. This paper presents a hybrid quantum genetic algorithm based on spin, which implements quantum crossover on quantum individuals, which is beneficial to retain relatively good gene segments. The strategy of updating quantum gate and adaptively adjusting search grid by using quantum bit phase method; In this paper, the critical properties of quantum Heisenberg model with mixed spins on simple cubic lattice are studied by using the mean field approximation of two spin groups, and the hybrid quantum genetic algorithm based on spins is applied to solve knapsack problem. At present, many problems in the fields of industry and financial investment can be transformed into backpack problems to solve. The effectiveness of spin-based hybrid quantum genetic algorithm in solving knapsack problem has been proved by several groups of experiments.
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.
Aimed at the problem of large storage capacity and easily falling into local optimum,a novel improved quantum genetic algorithm is presented.The algorithm adopts an angle-coding method to reduce the storage space of chromosomes.For the quantum chromosomes are distributed averagely in space of initial value,small interval method is used to initialize quanta swarm.It uses the improved quantum rotation gates to renew the population and realizes adaptive search by the adjustment strategy of dynamic quantum step and uses the operation of quantum crossover and quantum mutation to prevent the premature problem.Through the typical multi-peak function optimization test,it shows that the algorithm has the faster convergence rate,the stronger global optimization ability and the shorter computing time.The algorithm can be used for multi-peak function optimization problem.
Based on quantum genetic algorithm(QGA),a novel improved quantum genetic algorithm(NIQGA)to solve combinatorial optimization problem is proposed.To make full use of interference and entanglement characteristics of quantum state,dynamic step length in adjustment of angle of quantum gate,quantum crossover operation and quantum mutation operation are introduced,therefore high efficiency for optimization is achieved.Two typical combinatorial optimization problems—0/1 knapsack problem and route selection problem,are adopted to confirm the performance of NIQGA.Experimental results show that compared with GA and QGA,NIQGA is characterized by fast convergence rate and excellent capability on global optimization,especially better performance for combinatorial optimization problem with less correlation of genes.