Abstract
A novel real-coded immune quantum evolutionary algorithm for multi-modal function optimization (MRIQEA) is proposed. By niching methods population is divided into subpopulations automatically, local search is carried by the immune mechanism, each subpopulation can obtain precise solutions, and then the population can maintain all optimal solutions. Because of the quantum evolutionary algorithm with intrinsic parallelism it can maintain quite nicely the population diversity than the classical evolutionary algorithm, because of the adaptive immune operator and real representation for the chromosome it can converge to all optimal solutions rapidly. The technique for improving the performance of MRIQEA has been described and its superiority is shown by some simulation experiments in this paper.
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