TY - GEN
T1 - Directional shuffled frog leaping algorithm
AU - Kong, Lingping
AU - Pan, Jeng-Shyang
AU - Chu, Shu-Chuan
AU - Roddick, John
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Shuffled frog leaping algorithm is one of the popular used optimization algorithms. This algorithm includes the local search and global search two solving modes, but in this method only the worst frog from divided group is considered for improving location. In this paper, we propose a directional shuffled frog leaping algorithm (DSFLA) by introducing the directional updating and real-time interacting concepts. A direction flag is set for a frog before moving, if the frog goes better in a certain direction, it will get better in a big probability by moving a little further along that direction. The movement counter is set for preventing the frog move forward infinite. Real-time interacting works by sharing the currently optimal positions from the other groups. There should have some similarities among the best ones, and the worst individual could be improved by using those similarities. The experimental results show that the proposed approach is a very effective method for solving test functions.
AB - Shuffled frog leaping algorithm is one of the popular used optimization algorithms. This algorithm includes the local search and global search two solving modes, but in this method only the worst frog from divided group is considered for improving location. In this paper, we propose a directional shuffled frog leaping algorithm (DSFLA) by introducing the directional updating and real-time interacting concepts. A direction flag is set for a frog before moving, if the frog goes better in a certain direction, it will get better in a big probability by moving a little further along that direction. The movement counter is set for preventing the frog move forward infinite. Real-time interacting works by sharing the currently optimal positions from the other groups. There should have some similarities among the best ones, and the worst individual could be improved by using those similarities. The experimental results show that the proposed approach is a very effective method for solving test functions.
KW - Optimization algorithm
KW - Shuffled frog leaping algorithm
KW - Swarm intelligence
UR - https://doi.org/10.1007/978-3-319-70730-3_11
UR - http://www.scopus.com/inward/record.url?scp=85034018334&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-70730-3_31
DO - 10.1007/978-3-319-70730-3_31
M3 - Conference contribution
SN - 9783319707297
T3 - Smart Innovation, Systems and Technologies
SP - 257
EP - 264
BT - Advances in Smart Vehicular Technology, Transportation, Communication and Applications - Proceedings of the 1st International Conference on Smart Vehicular Technology, Transportation, Communication and Applications
A2 - Pan, Jeng-Shyang
A2 - Wu, Tsu-Yang
A2 - Zhao, Yong
A2 - Jain, Lakhmi C.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 1st International Conference on Smart Vehicular Technology, Transportation, Communication and Applications, VTCA 2017
Y2 - 6 November 2017 through 8 November 2017
ER -