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

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Listed:
  • 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)

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.

Suggested Citation

  • Hernán Peraza-Vázquez & Adrián Peña-Delgado & Prakash Ranjan & Chetan Barde & Arvind Choubey & Ana Beatriz Morales-Cepeda, 2021. "A Bio-Inspired Method for Mathematical Optimization Inspired by Arachnida Salticidade," Mathematics, MDPI, vol. 10(1), pages 1-32, December.
    • Handle: RePEc:gam:jmathe:v:10:y:2021:i:1:p:102-:d:713313
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    References listed on IDEAS

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    1. Y.C. Ho & D.L. Pepyne, 2002. "Simple Explanation of the No-Free-Lunch Theorem and Its Implications," Journal of Optimization Theory and Applications, Springer, vol. 115(3), pages 549-570, December.
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