Novel immune optimization based on lifespan mechanism for global numerical optimization problems

In order to exploit and preserve the diversity of immune optimization algorithm when solving high dimensional global optimization problems, a novel immune optimization algorithm based lifespan (LIO) model is proposed. LIO incorporates a lifespan model, local and global search procedure to improve the overall performance in solving global optimization instance. Particularly, a novel performance evaluation criterion is constructed in this paper, by which the performance of different population-based algorithms can be compared easily. In the experimental study, firstly several conventional benchmarks are used to determine the values of parameters. Next, the presented LIO is compared with several population-based algorithms. The experimental results of the LIO are significantly better than that of the conventional clonal selection algorithm (CSA) in terms of the performance evaluation criterion proposed.