TY - GEN
T1 - Adaptive particle swarm optimizer with nonextensive schedule
AU - Anastasiadis, Aristoklis D.
AU - Georgoulas, George
AU - Magoulas, George
AU - Tzes, Anthony
PY - 2007
Y1 - 2007
N2 - This paper introduces a class of adaptive particle swarm optimization (PSO) methods that build on the theory of nonextensive statistical mechanics. These methods combine the traditional position update rule with an annealing schedule that is based on the nonextensive entropy. Comparative experiments conducted on benchmark functions, have showed that the tested algorithms outperform the standard PSO.
AB - This paper introduces a class of adaptive particle swarm optimization (PSO) methods that build on the theory of nonextensive statistical mechanics. These methods combine the traditional position update rule with an annealing schedule that is based on the nonextensive entropy. Comparative experiments conducted on benchmark functions, have showed that the tested algorithms outperform the standard PSO.
KW - Global search
KW - Nonextensive statistical mechanics
KW - Particle swarm optimizer
KW - Swarm intelligence
UR - http://www.scopus.com/inward/record.url?scp=34548083412&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34548083412&partnerID=8YFLogxK
U2 - 10.1145/1276958.1276982
DO - 10.1145/1276958.1276982
M3 - Conference contribution
AN - SCOPUS:34548083412
SN - 1595936971
SN - 9781595936974
T3 - Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference
SP - 168
BT - Proceedings of GECCO 2007
T2 - 9th Annual Genetic and Evolutionary Computation Conference, GECCO 2007
Y2 - 7 July 2007 through 11 July 2007
ER -