基于亲缘关系选择的粒子群优化算法  

Particle swarm optimization algorithm based on kinship selection

作　　者：管仁初[1] 贺宝润 梁艳春[1,2] 时小虎[1] GUAN Ren-chu;HE Bao-run;LIANG Yan-chun;SHI Xiao-hu(College of Computer Science and Technology,Jilin University,Changchun 130012,China;Computer Science and Technology Department,Zhuhai College of Jilin University,Zhuhai 519041,China)

机构地区：[1]吉林大学计算机科学与技术学院,长春130012 [2]吉林大学珠海学院计算机科学与技术系,广东珠海519041

出　　处：《吉林大学学报（工学版）》2022年第8期1842-1849,共8页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金面上项目(61972174,62172187);吉林省科技厅技术公关项目(20190302107GX)。

摘　　要：针对传统粒子群优化算法在解决最优化问题中存在早熟收敛和无法寻找到全局最优解问题,本文提出了一种基于亲缘关系选择的粒子群优化算法,提高了算法的全局搜索能力。此外,引入了多个种群的交流机制与各子种群之间的淘汰机制,有效避免了个体在寻优过程中陷入局部最优点。实验部分首先在单目标优化函数集上与传统的粒子群优化算法以及一些有竞争力的算法结果进行对比分析,发现算法在相同种群规模与评价次数的条件下,在准确性与搜索能力上有着明显的优势;然后,将新算法应用到桁架穹顶优化问题上,并与传统的粒子群优化算法进行了比较,求得了这一实际问题的一个可行解。Aiming at the problem that the traditional particle swarm optimization(PSO) algorithm has premature convergence and unable to find the global optimal solution in solving the optimization problem, a particle swarm optimization algorithm based on kinship selection is proposed, which improves the global search ability of the algorithm. In addition, the communication mechanism of multiple populations and the elimination mechanism between each subpopulation are introduced, which effectively avoids individuals falling into the local optimum in the process of optimization. In the experiment part, the single objective optimization function set is compared with the traditional particle swarm optimization algorithm and the results of some competitive algorithms. obvious advantages;then, the new algorithm is applied to the optimization problem of truss dome, and compared with the traditional particle swarm optimization algorithm, a feasible solution to this practical problem is obtained.

关 键 词：最优化问题 群智能算法 粒子群算法 亲缘关系选择 穹顶优化 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]