To improve search ability and optimization efficiency and avoid premature convergence for artificial fish school algorithm(AFSA),a novel quantum AFSA for continuous space optimization was proposed.Artificial fishes were encoded by the phase of quantum bit,evolved by quantum rotation gates,and mutated by quantum Pauli—Z gate.The simulation results showed that the approach is superior to either common AFSA or simple genetic algorithm in both search capability and optimization efficiency.
To improve search ability and optimization efficiency and avoid premature convergence for artificial fish school algorithm(AFSA),a novel quantum AFSA for continuous space optimization was proposed.Artificial fishes were encoded by the phase of quantum bit,evolved by quantum rotation gates,and mutated by quantum Pauli—Z gate.The simulation results showed that the approach is superior to either common AFSA or simple genetic algorithm in both search capability and optimization efficiency.
In order to improve the global search ability and the convergence speed of the Artificial Fish Swarm Algorithm (AFSA), a novel Quantum Artificial Fish Swarm Algorithm (QAFSA) which is based on the concepts and principles of quantum computing, such as the quantum bit and quantum gate is proposed in this paper. The position of the Artificial Fish (AF) is encoded by the angle in [0, 2π] based on the qubit's polar coordinate representation in the 2-dimension Hilbert space. The quantum rotation gate is used to update the position of the AF in order to enable the AF to move and the quantum non-gate is employed to realize the mutation of the AF for the purpose of speeding up the convergence. Rapid convergence and good global search capacity characterize the performance of QAFSA. The experimental results prove that the performance of QAFSA is significantly improved compared with that of standard AFSA.
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.
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.
To improve search ability and optimization efficiency and to avoid premature con- vergence for particle swarms optimization,a novel quantum particle swarm optimization for continuous space optimization is proposed.The positions of particles are encoded by the prob- ability amplitudes of quantum bits,the movements of particles are performed by quantum rotation gates,which achieve particles searching.The mutations of particles are performed by quantum non-gate,which increase the diversity Of particles.As each quantum bit contains two probability amplitudes,each particle occupies two positions in space.Hence,the process of searching is accelerated.The experimental results show that the algorithm proposed is superior to the basic particle swarms optimization.
To enhance the performance of the intelligent optimization algorithm, a new model of performing search on the Bloch sphere is proposed. Then, by integrating the model into the artificial fish swarm optimization, we present a quantum-inspired artificial fish swarm optimization algorithm. In proposed method, the fishes are encoded with the qubits described on the Bloch sphere. The vector product theory is adopted to establish the axis of rotation, and the Pauli matrices are used to construct the rotation matrices. The four fish behaviors, such as moving, tracking, capturing, aggregating, are achieved by rotating the current qubit about the rotation axis to the target qubit on the Bloch sphere. The Bloch coordinates of qubit can be obtained by measuring with the Pauli matrices, and the optimization solutions can be presented through the solution space transformation. The highlight advantages of this method are the ability to simultaneously adjust two parameters of a qubit and automatically achieve the best match between two adjustment quantities, which may accelerate the optimization process. The experimental results show that the proposed method obviously outperforms the classical one in convergence speed and achieves better levels for some benchmark functions.