Artificial bee colony algorithm based on adaptive local information sharing
Ryo Takano, Hiroyuki Satō, Keiki Takadama · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2018
This paper focuses on Artificial Bee Colony (ABC) algorithm in dynamic optimization problems (DOPs), and proposes the improvements for ABC algorithm in DOPs as ABC algorithm based on adaptive local information sharing (ABC-alis). To investigate the tracking ability to dynamic change of ABC-alis, it is compared the improved algorithm to two cases of dynamic change. These two cases are "Case 1: Periodic Change" and "Case 2: Continuing Change". The experimental results revealed that the following implications: (1) ABC-alis can adapt with various dynamic changes; (2) ABC-alis has high tracking performance against continuous change.