Improved Particle Swarm Optimization Algorithm and its Application Based on the Aggregation Degree
2013 ◽
Vol 427-429
◽
pp. 1934-1938
Keyword(s):
The aim is to improve the convergence of the algorithm, and increase the population diversity. Adaptively particles of groups fallen into local optimum is adjusted in order to realize global optimal. by judging groups spatial location of concentration and fitness variance. At the same time, the global factors are adjusted dynamically with the action of the current particle fitness. Four typical function optimization problems are drawn into simulation experiment. The results show that the improved particle swarm optimization algorithm is convergent, robust and accurate.
2020 ◽
2013 ◽
Vol 5
(7)
◽
pp. 533-540
2012 ◽
Vol 532-533
◽
pp. 1429-1433
2008 ◽
Vol 44
(6)
◽
pp. 1046-1049
◽
2012 ◽
Vol 538-541
◽
pp. 2658-2661
2018 ◽
Vol 6
(4)
◽
pp. 3980-3986