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Dianzi xuebao

A Novel Quantum Genetic Algorithm and Its Application

Gexiang Zhang

Published Jan 1, 2004• 49 citations
OpenAlex

Abstract

This paper proposes a novel quantum genetic algorithm (NQGA),whose core is that the strategies of updating quantum gate using qubit phase comparison approach and adjusting search grid adaptively are introduced.NQGA is characterized by rapid convergence,good global search capability and short computing time.Test results of complex functions and application example demonstrate NQGA is superior to conventional genetic algorithms in quality and efficiency.

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This paper proposes a novel quantum genetic algorithm (NQGA),whose core is that the strategies of updating quantum gate using qubit phase comparison approach and adjusting search grid adaptively are introduced.NQGA is characterized by rapid convergence,good global search capability and short computing time.Test results of complex functions and application example demonstrate NQGA is superior to conventional genetic algorithms 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.
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.
In this paper, a novel Quantum Genetic Algorithm (QGA) called Parallel Probability-gate Quantum Genetic Algorithm (PPQGA) is proposed.The main points of PPQGA are that a new chromosome representation called qubit representation, a new updating gate called probability-gate and a novel evolutionary strategy being used in update procedure are introduced.Based on the concepts and principles of quantum computing introduced, PPQGA is characterized by rapid convergence, good global search capability and the ability of possessing exploitation simultaneously.The experimental results of several special functions show that PPQGA can speedup the migration of the top individuals of subpopulations and is superior to other several genetic algorithms greatly in quality and 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 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.
Aiming at the disadvantages of the fixed rotation angle in quantum genetic algorithm (QGA), in this paper, the particle swarm optimization (PSO) was used to update the angle of quantum rotation gate in stead of the old one that locates the angle of quantum rotation gate by a designed query table. According to the experimental results, the novel quantum genetic algorithm (NQGA) with new updating method has got a higher global search capability than common QGA. The NQGA was used to the load distribution optimization of tandem cold rolling. The comparison data show that the NQGA is an effective method for load distribution optimization of tandem cold rolling.
Based on the probability amplitude ratio of the corresponding states of quantum bits, the rotation angle of quantum rotation gate is calculated adaptively and a new quantum rotation gate is constructed. A novel quantum evolutionary algorithms (NQEA), whose core is that the designed quantum rotation gate is used as evolutionary strategy is introduced, rapid convergence and good global search capability characterize the performance of NQEA, and NQEA is able to possess the two characteristics of exploration and exploitation, simultaneously. NQEA is applied on the physical distribution vehicle routing problem - a typical combinatorial optimization problem, the results show that NQEA is superior to the previous quantum evolutionary algorithm as well as other conventional genetic algorithms