Quantum-inspired evolutionary algorithm for continuous space optimization based on Bloch coordinates of qubits

A novel quantum-inspired evolutionary algorithm is proposed based on the Bloch coordinates of quantum bits (qubits) in this paper. The chromosome is comprised of qubits whose Bloch coordinates comprise gene chain. The quantum chromosomes are updated by quantum rotation gates, and are mutated by quantum non-gates. For the rotation direction of quantum rotation gates, a simple determining method is proposed. For the rotation and mutation of qubits, two new operators are constructed based on Bloch coordinates of qubits. In this algorithm, the Bloch coordinates of each qubit are regarded as three paratactic genes, each chromosome contains three gene chains, and each gene chain represents an optimization solution, which can accelerate the convergence process for the same number of chromosomes. By two application examples of function extremum and neural network weights optimization, the simulation results show that the approach is superior to common quantum evolutionary algorithm and simple genetic algorithm in both search capability and optimization efficiency.