Cognitive Hybrid PSO/SA Combinatorial Optimization

被引：0

作者：

Brezinski, Kenneth ^{[1
]}

Ferens, Ken ^{[1
]}

机构：

[1] Univ Manitoba, Dept Elect & Comp Engn, Winnipeg, MB, Canada

来源：

PROCEEDINGS OF THE 2019 IEEE 18TH INTERNATIONAL CONFERENCE ON COGNITIVE INFORMATICS & COGNITIVE COMPUTING (ICCI*CC 2019) | 2019年

关键词：

Swarm intelligence; Simulated annealing; Global optimization; Combinatorial particle swarm optimization; PARTICLE SWARM OPTIMIZATION; GLOBAL OPTIMIZATION; ALGORITHM;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a population based simulated annealing algorithm to improve modelling of cognitive processes. Particle Swarm Optimization (PSO) is embedded within the basic Simulated Annealing (SA) algorithm to allow for multiple concurrent candidate solutions through the use of a population-driven social coefficient updating the other population members. A modified ramping strategy which balances inertial, personal and swarm coefficients is introduced. The hybrid PSO/SA algorithm was tested on the travelling salesperson problem (TSP), and was shown to outperform the individual algorithms by improving their limitations in exploration and exploitation.

引用

页码：389 / 393

页数：5

共 43 条

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