A Bio-Inspired Method for Mathematical Optimization Inspired by Arachnida Salticidade
Hernán Peraza-Vázquez,
Adrián Peña-Delgado,
Prakash Ranjan,
Chetan Barde,
Arvind Choubey,
Ana Beatriz Morales-Cepeda
Affiliations
Hernán Peraza-Vázquez
Instituto Politécnico Nacional, Research Center for Applied Science and Advanced Technology (CICATA), km.14.5 Carretera Tampico-Puerto Industrial Altamira, Altamira 89600, Tamaulipas, Mexico
Adrián Peña-Delgado
Departamento de Mecatrónica y Energías Renovables, Universidad Tecnológica de Altamira, Boulevard de los Ríos km.3 + 100, Puerto Industrial Altamira, Altamira 89601, Tamaulipas, Mexico
Prakash Ranjan
Department of Electronics and Communication Engineering, Indian Institute of Information Technology Bhagalpur, Bhagalpur 813210, Bihar, India
Chetan Barde
Department of Electronics and Communication Engineering, Indian Institute of Information Technology Bhagalpur, Bhagalpur 813210, Bihar, India
Arvind Choubey
Department of Electronics and Communication Engineering, Indian Institute of Information Technology Bhagalpur, Bhagalpur 813210, Bihar, India
Ana Beatriz Morales-Cepeda
Division of Graduate Studies and Research, Instituto Tecnológico de Ciudad Madero (TecNM), Juventino Rosas y Jesús Urueta s/n, Col. Los Mangos, Cd. Madero 89318, Tamaulipas, Mexico
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 well-known 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.
