An Improved Chaos Quantum Immune Algorithm for Power Generation Expansion Planning

Abstract

In this paper, a new chaos quantum immune algorithm is proposed, which combines the ergodicity of chaos search and the efficiency of quantum computation into the immune optimization algorithm. In this algorithm, antibodies in the algorithm population are encoded by quantum bits and replaced by quantum revolving gate. At the same time, in order to evolve the quantum bits of the corresponding phase, we introduce two different chaotic variables into the quantum revolving gate. Among them, local search is realized by using a small number of excellent clones; the excellent clone with relatively large amplitude realizes global search. The convergence of this method is verified. At the end of the paper, a simulation example is used to show that compared with the existing methods, The improved immune algorithm has a great improvement in the efficiency of solving the problem, at the same time, it also improves the convergence efficiency.