Particle Swarm Optimization (PSO) methods for dynamic function optimization are studied in this paper. We compare dynamic variants of standard PSO and Hierarchical PSO (H-PSO) on different dynamic benchmark functions. Moreover, a new type of hierarchical PSO, called Partitioned H-PSO (PH-PSO), is proposed. In this algorithm the hierarchy is partitioned into several sub-swarms for a limited number of generations after a change occurred. Different methods for determining the time when to rejoin the hierarchy and how to handle the topmost sub-swarm are discussed. The test results show that H-PSO performs significantly better than PSO on all test functions and that the PH-PSO algorithms often perform best on multimodal functions where changes are not too severe.
CITATION STYLE
Janson, S., Middendorf, M., Raidl, G., Cagnoni, S., Branke, J., Corne, D., … Squillero, G. (2004). Applications of Evolutionary Computing (Vol. 3005, pp. 513–524). Retrieved from http://www.springerlink.com/content/rdw8795v53r13jrf/
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