TY - GEN
T1 - A multi-swarm evolutionary framework based on a feedback mechanism
AU - Cheng, Ran
AU - Sun, Chaoli
AU - Jin, Yaochu
PY - 2013
Y1 - 2013
N2 - Most evolutionary algorithms, including particle swarm optimization (PSO) algorithms, involve at least one population (swarm) to realize information exchange or information sharing among different individuals. To enhance the algorithms' global search ability, several multi-swarm PSO algorithms have been proposed. In this paper, a novel multi-swarm evolutionary framework based on a feedback mechanism is introduced. The framework consists of a search operator similar to those in PSO and a mutation strategy, on the top of the feedback mechanism. The framework is compared with a multi-swarm PSO and the canonical PSO on a few widely used benchmarks to demonstrate its performance.
AB - Most evolutionary algorithms, including particle swarm optimization (PSO) algorithms, involve at least one population (swarm) to realize information exchange or information sharing among different individuals. To enhance the algorithms' global search ability, several multi-swarm PSO algorithms have been proposed. In this paper, a novel multi-swarm evolutionary framework based on a feedback mechanism is introduced. The framework consists of a search operator similar to those in PSO and a mutation strategy, on the top of the feedback mechanism. The framework is compared with a multi-swarm PSO and the canonical PSO on a few widely used benchmarks to demonstrate its performance.
UR - http://www.scopus.com/inward/record.url?scp=84881602534&partnerID=8YFLogxK
U2 - 10.1109/CEC.2013.6557639
DO - 10.1109/CEC.2013.6557639
M3 - Conference article published in proceeding or book
AN - SCOPUS:84881602534
SN - 9781479904549
T3 - 2013 IEEE Congress on Evolutionary Computation, CEC 2013
SP - 718
EP - 724
BT - 2013 IEEE Congress on Evolutionary Computation, CEC 2013
T2 - 2013 IEEE Congress on Evolutionary Computation, CEC 2013
Y2 - 20 June 2013 through 23 June 2013
ER -