A Self-adaptive Multipeak Artificial Immune Genetic Algorithm
Abstract: Genetic algorithm is
a global probability search algorithm developed by simulating the biological
natural selection and genetic evolution mechanism and it has excellent global
search ability, however, in practical applications, premature convergence
occurs easily in the genetic algorithm. This paper proposes an self-adaptive
multi-peak immune genetic algorithm (SMIGA) and this algorithm integrates
immunity thought in the biology immune system into the evolutionary process of
genetic algorithm, uses self-adaptive dynamic vaccination and provides a
downtime criterion, the selection strategy of immune vaccine and the
construction method of immune operators so as to promote the population develop
towards the optimization trend and suppress the degeneracy phenomenon in the
optimization by using the feature information in a selective and purposive
manner. The simulation experiment shows that the method of this paper can
better solve the optimization problem of multi-peak functions, realize global
optimum search, overcome the prematurity problem of the antibody population and
improve the effectiveness and robustness of optimization.
Author: Qingzhao Li, Fei Jiang
Journal Code: jptkomputergg160227
