SLSL-QPSO: Quantum-behaved particle swarm optimization with short-lived swarm layers

被引：2

作者：

Liang, Kang ^{[1
]}

Zhang, Xiukai ^{[2
]}

Krakhmalev, Oleg ^{[3
]}

机构：

[1] Shanghai Polytech Univ, Engn Training & Innovat Educ Ctr, Shanghai 201209, Peoples R China

[2] Shanghai Polytech Univ, Sch Comp & Informat Engn, Shanghai 201209, Peoples R China

[3] Financial Univ Govt Russian Federat, Dept Data Anal & Machine Learning, 4th Veshnyakovsky Passage 4, Moscow 109456, Russia

来源：

SOFTWAREX | 2023年 / 24卷

关键词：

Optimization; Particle swarm optimization; Quantum-behaved particle swarm optimization; DESIGN; ALGORITHM;

D O I：

10.1016/j.softx.2023.101536

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

SLSL-QPSO is a software that can find the optimal value of a function. It improves over the Quantum-behaved Particle Swarm Optimization (QPSO) algorithms by leveraging the concept of living and death as swarm layers like the parameter optimization in the Optimized PSO (OPSO) but without the super swarm, the Le ' vy mutation, the scoped contradiction-expansion coefficient, and the selection of effective layers. Experimental results demonstrate that SLSL-QPSO has superior performance in finding better optimal than QPSO and several other variants thus providing a competitive solution to optimization problems.

引用

页数：7

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