TY - JOUR
T1 - A bio-inspired method for mathematical optimization inspired by arachnida salticidade
AU - Peraza-Vázquez, Hernán
AU - Peña-Delgado, Adrián
AU - Ranjan, Prakash
AU - Barde, Chetan
AU - Choubey, Arvind
AU - Morales-Cepeda, Ana Beatriz
N1 - Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022/1/1
Y1 - 2022/1/1
N2 - This paper proposes a new meta-heuristic called Jumping Spider Optimization Algorithm (JSOA), inspired by Arachnida Salticidae hunting habits. The proposed algorithm mimics the behavior of spiders in nature and mathematically models its hunting strategies: search, persecution, and jumping skills to get the prey. These strategies provide a fine balance between exploitation and exploration over the solution search space and solve global optimization problems. JSOA is tested with 20 well-known testbench mathematical problems taken from the literature. Further studies include the tuning of a Proportional-Integral-Derivative (PID) controller, the Selective harmonic elimination problem, and a few real-world single objective bound-constrained numerical optimization problems taken from CEC 2020. Additionally, the JSOA’s performance is tested against several wellknown bio-inspired algorithms taken from the literature. The statistical results show that the proposed algorithm outperforms recent literature algorithms and is capable to solve challenging real-world problems with unknown search space.
AB - This paper proposes a new meta-heuristic called Jumping Spider Optimization Algorithm (JSOA), inspired by Arachnida Salticidae hunting habits. The proposed algorithm mimics the behavior of spiders in nature and mathematically models its hunting strategies: search, persecution, and jumping skills to get the prey. These strategies provide a fine balance between exploitation and exploration over the solution search space and solve global optimization problems. JSOA is tested with 20 well-known testbench mathematical problems taken from the literature. Further studies include the tuning of a Proportional-Integral-Derivative (PID) controller, the Selective harmonic elimination problem, and a few real-world single objective bound-constrained numerical optimization problems taken from CEC 2020. Additionally, the JSOA’s performance is tested against several wellknown bio-inspired algorithms taken from the literature. The statistical results show that the proposed algorithm outperforms recent literature algorithms and is capable to solve challenging real-world problems with unknown search space.
KW - Bio-inspired algorithm
KW - Constrained optimization
KW - Global optimization
KW - Meta-heuristics
UR - http://www.scopus.com/inward/record.url?scp=85122010243&partnerID=8YFLogxK
U2 - 10.3390/math10010102
DO - 10.3390/math10010102
M3 - Artículo
AN - SCOPUS:85122010243
SN - 2227-7390
VL - 10
JO - Mathematics
JF - Mathematics
IS - 1
M1 - 102
ER -