TY - GEN
T1 - Evaluation of different horizon lengths in single-agent finite impulse response optimizer
AU - Ab Rahman, Tasiransurini
AU - Ibrahim, Zuwairie
AU - Aziz, Nor Hidayati Abdul
AU - Aziz, Nor Azlina Ab
AU - Mohamad, Mohd Saberi
AU - Shapiai, Mohd Ibrahim
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5/15
Y1 - 2019/5/15
N2 - Single-agent Finite Impulse Response Optimizer (SAFIRO) is a newly single solution-based metaheuristic optimization algorithm which mimics the work procedure of the ultimate unbiased finite impulse response (UFIR) filter. In a real UFIR filter, the horizon length, N plays an important role to obtain the optimal estimation. In SAFIRO, N represents the repetition number of estimation part that needs to be done in finding an optimal solution. In the original SAFIRO, N = 4 is assigned. In this study, the effect of N towards the performance of SAFIRO is evaluated by assigning N between the range of 4 to 10. The CEC 2014 benchmark test suite is used for performance evaluations. Statistical analysis using the nonparametric Friedman test was performed to observe the performance. Experimental results show that N is a function dependent parameter where for certain functions, SAFIRO performs better with a larger value of N. However, for certain functions, SAFIRO performs better with a minimum value of N.
AB - Single-agent Finite Impulse Response Optimizer (SAFIRO) is a newly single solution-based metaheuristic optimization algorithm which mimics the work procedure of the ultimate unbiased finite impulse response (UFIR) filter. In a real UFIR filter, the horizon length, N plays an important role to obtain the optimal estimation. In SAFIRO, N represents the repetition number of estimation part that needs to be done in finding an optimal solution. In the original SAFIRO, N = 4 is assigned. In this study, the effect of N towards the performance of SAFIRO is evaluated by assigning N between the range of 4 to 10. The CEC 2014 benchmark test suite is used for performance evaluations. Statistical analysis using the nonparametric Friedman test was performed to observe the performance. Experimental results show that N is a function dependent parameter where for certain functions, SAFIRO performs better with a larger value of N. However, for certain functions, SAFIRO performs better with a minimum value of N.
KW - Horizon length
KW - Optimization
KW - SAFIRO
UR - http://www.scopus.com/inward/record.url?scp=85066972210&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85066972210&partnerID=8YFLogxK
U2 - 10.1109/ICCISci.2019.8716455
DO - 10.1109/ICCISci.2019.8716455
M3 - Conference contribution
AN - SCOPUS:85066972210
T3 - 2019 International Conference on Computer and Information Sciences, ICCIS 2019
BT - 2019 International Conference on Computer and Information Sciences, ICCIS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Conference on Computer and Information Sciences, ICCIS 2019
Y2 - 3 April 2019 through 4 April 2019
ER -