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International Conference on Natural Computation (ICNC)

Quantum-inspired genetic algorithm based on phase encoding

Xiande Liu, Xiaoming Liu

Published Jul 1, 2013• 8 citations
Publisher

Abstract

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 paper. 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, 2π], 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.

DOI
10.1109/icnc.2013.6818017

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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 paper. 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, 2π], 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.
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.
Due to the 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.The optimization process is performed in[0,2π]~n,which has nothing to do with specific issues,therefore,the proposed method has good adaptability for a variety of optimization problems.In application of function extremum optimization,the simulation results show that the approach is superior to either common quantum genetic algorithm or simple genetic algorithm in both search capability and optimization efficiency.
Aiming at the shortcoming of ant colony optimization which was only suitable for the discrete optimization and the problem of slow convergence,a suitable quantum ant colony optimization algorithm for continuous optimization was proposed.The locations of ant were directly encoded by the phase of qubits in the proposed algorithm.First,the destination to move was determined according to the select probability constructed by the pheromone information and heuristic information,then the qubits of ant were updated by quantum rotation gates to achieve ant moving,and mutated by quantum Pauli-Z gates to increase diversity of ants.Finally,the pheromone information and the heuristic information were updated in the new location of ants.As the optimization is performed in[0,2π]~n,which has nothing to do with the specific issues,hence,the proposed method has good adaptability for a variety of optimization problems. With applications of function extremum and clustering optimization,the simulation results show that the proposed algorithm is superior to the common ant colony optimization algorithm and the standard genetic algorithm in both search capability and optimization efficiency.
Aiming at the shortcoming of ant colony optimization which was only suitable for the discrete optimization and the problem of slow convergence,this paper proposed a suitable quantum ant colony optimization algorithm for continuous optimization. The locations of ant were directly encoded by the phase of qubits in the proposed algorithm. First,determined the destination to move according to the select probability constructed by the pheromone information and heuristic information,and then updated the qubits of ant by quantum rotation gates to achieve ant moving,and mutated by quantum Pauli-Z gates to increase diversity of ants. Finally,updated the pheromone information and the heuristic information in the new location of ants. As performed the optimization in [0,2π]n ,which had nothing to do with the specific issues,hence,the proposed method had good adaptability for a variety of optimization problems. With applications of function extremum and controller parameters optimization,the simulation results show that the proposed algorithm is superior to the continuous quantum ant colony algorithm and the standard genetic algorithm in both search capability and optimization efficiency.
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.
Optimization method is important in engineering design and application. Quantum genetic algorithm has the characteristics of good population diversity, rapid convergence and good global search capability and so on. It combines quantum algorithm with genetic algorithm. A novel quantum genetic algorithm is proposed, which is called Variable-boundary-coded Quantum Genetic Algorithm (vbQGA) in which qubit chromosomes are collapsed into variable-boundary-coded chromosomes instead of binary-coded chromosomes. Therefore much shorter chromosome strings can be gained. The method of encoding and decoding of chromosome is first described before a new adaptive selection scheme for angle parameters used for rotation gate is put forward based on the core ideas and principles of quantum computation. Eight typical functions are selected to optimize to evaluate the effectiveness and performance of vbQGA against standard Genetic Algorithm (sGA) and Genetic Quantum Algorithm (GQA). The simulation results show that vbQGA is significantly superior to sGA in all aspects and outperforms GQA in robustness and solving velocity, especially for multidimensional and complicated functions.