Population System Optimization Algorithm with Impulsive Birth and Seasonal Killing

doi:10.3778/j.issn.1673-9418.2007035

Abstract

Abstract:

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

摘要：

为了求解一些非线性优化问题，采用具有脉冲出生和季节性捕杀的种群动力学模型提出了一种新的群智能优化算法（PSO-IBSK）。在该算法中，假设某种群由具有幼年和成年两种阶段状态的若干个体组成，幼体是由成体脉冲产生的，经过一段时间后会变成为成体。为了提升种群的整体质量，需要季节性地对一些生长状况不良的成体进行捕杀。该算法中的出生算子和成长算子可分别实现成体向幼体瞬时和延迟传递信息，有助于搜索跳出局部最优解陷阱；捕杀算子可周期性地将不良成体清除，死亡算子可将虚弱个体随机清除，该两个算子有利于提升算法的求精能力；强势算子可实现强壮个体向虚弱个体扩散强壮信息，竞争算子可实现幼年和成体之间的有效信息交换，该两个算子有利于提升算法的探索能力；进化算子可确保算法具有全局收敛性。该算法的大部分参数采用该种群动力学模型确定，具有很好的科学性；该算法每次只处理个体特征数的6‰~8%，从而使时间复杂度大幅降低。测试结果表明，该算法具有较优越的性能，适于求解维数较高的优化问题。

关键词: 群智能优化算法, 种群动力学, 脉冲出生, 季节性捕杀

HUANG Guangqiu, LU Qiuqin. Population System Optimization Algorithm with Impulsive Birth and Seasonal Killing[J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(10): 2002-2014.

黄光球, 陆秋琴. 具脉冲出生和季节性捕杀的种群系统优化算法[J]. 计算机科学与探索, 2021, 15(10): 2002-2014.

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