Population Dynamic Optimization Algorithm with Discrete Leslie Age Structure

Abstract

To solve some function optimization problems, a new group intelligent optimization algorithm, PDO-DLAS algorithm for short, is proposed by using the dynamic model of population with Leslie age structure. In this algorithm, it is assumed that a population is composed of individuals with different genders and ages. Individuals are automatically divided into several categories according to their gender and age, which greatly increases the diversity of individuals; each operator has a clear function, in which the learning operator can realize the information exchange among individuals of different genders but similar ages; the influence operator can realize the information exchange among individuals of different genders and different ages; the newborn operator can increase the number of strong individuals and the death operator can reduce the number of weak individuals; the evolutionary operator can ensure the global convergence of the algorithm; the Leslie model is used to determine the relevant parameters of the algorithm, enhancing the scientificity of parameter determination; each evolution of the algorithm only deals with the number of individual characteristics 1/250-1/10, which greatly reduces the time complexity. The test results show that the algorithm has superior performance and is suitable for solving optimization problems with high dimension.

Keywords

• swarm intelligence optimization algorithm
• leslie model
• population dynamics
• age structure