A novel quantum evolutionary algorithm based immune mechanism (MIQEA) for solving function optimization containing multiple global optima was proposed. By niche methods the original population was divided into subpopulations automatically, and then local search was carried by the immune mechanism in which antibody can be clone selected, immune cell can accomplish cross-mutation, memory cells can be produced and similar antibodies can be suppressed for all subpopulations, each subpopulation can obtain optimal solutions. The algorithm can maintain all optimal solutions. The quantum evolutionary algorithm with intrinsic parallelism is integrated with adaptive immune dynamic model, it not only can maintain quite nicely the population diversity than the classical evolutionary algorithm, but also can help to accelerate the convergence speed and has been able to get the global optimal and sub-optimal solutions rapidly. The convergence of the MIQEA was proved; its superiority is shown by some simulation experiments.

Quantum evolutionary algorithm,   ,  Multi-modal function optimization,   ,  Immune operator,    ,  Cross-mutation