A Bio-Inspired Method for Mathematical Optimization Inspired by Arachnida Salticidade

Hernán Peraza-Vázquez, Adrián Peña-Delgado, Prakash Ranjan, Chetan Barde, Arvind Choubey and Ana Beatriz Morales-Cepeda
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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

Mathematics, 2021, vol. 10, issue 1, 1-32

Abstract: 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.

Keywords: bio-inspired algorithm; meta-heuristics; constrained optimization; global optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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