Journal of Frontiers of Computer Science and Technology ›› 2021, Vol. 15 ›› Issue (10): 2002-2014.DOI: 10.3778/j.issn.1673-9418.2007035

• Theory and Algorithm • Previous Articles     Next Articles

Population System Optimization Algorithm with Impulsive Birth and Seasonal Killing

HUANG Guangqiu, LU Qiuqin   

  1. School of Management, Xi'an University of Architecture and Technology, Xi'an 710055, China
  • Online:2021-10-01 Published:2021-09-30



  1. 西安建筑科技大学 管理学院,西安 710055


To solve some nonlinear optimization problems, a new swarm intelligence optimization algorithm, the PSO-IBSK algorithm, is proposed by using the population dynamics model with impulsive birth and seasonal killing. In this algorithm, it is assumed that a certain population is composed of several individuals with two stages, young and adult. The young individuals are generated by the impulse birth of adult individuals and become adult individuals after a period of time. To improve the overall quality of the population, it is necessary to kill some adult individuals with poor growth status seasonally. The birth operator and growth operator in the algorithm can realize instantaneous and delayed information transfer from adult to young individuals, which is helpful for searching to jump out of traps of local optimal solutions. The killing operator can periodically clear the bad adult individuals, and the death operator can remove the weak individuals randomly, the two operators can improve the exploitation ability of the algorithm. The strong operator can realize the diffusion of strong information from strong individuals to weak individuals, and the competition operator can realize the effective information exchange between the young and the adult individuals, the two operators are conducive to enhancing the exploration ability of the algorithm. The evolu-tionary operator can ensure the global convergence of the algorithm. Most of the parameters of the algorithm are determined by the population dynamics model, which is scientific. The algorithm only deals with [6‰~8%] of the number of individual features each time, 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.

Key words: swarm intelligence optimization algorithm, population dynamics, impulsive birth, seasonal killing



关键词: 群智能优化算法, 种群动力学, 脉冲出生, 季节性捕杀