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A Novel Bio-Inspired Optimization Algorithm Based on Mantis Shrimp Survival Tactics

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  • José Alfonso Sánchez Cortez

    (Instituto Politécnico Nacional, CICATA-Altamira, Km. 14.5 Carretera Tampico-Puerto Industrial Altamira, Altamira 89600, Tamaulipas, Mexico)

  • Hernán Peraza Vázquez

    (Instituto Politécnico Nacional, CICATA-Altamira, Km. 14.5 Carretera Tampico-Puerto Industrial Altamira, Altamira 89600, Tamaulipas, Mexico)

  • Adrián Fermin Peña Delgado

    (Departamento de Mecatrónica y Energías Renovables, Universidad Tecnológica de Altamira, Boulevard de los Ríos Km. 3+100, Puerto Industrial Altamira, Altamira 89608, Tamaulipas, Mexico)

Abstract

This paper presents a novel meta-heuristic algorithm inspired by the visual capabilities of the mantis shrimp ( Gonodactylus smithii ), which can detect linearly and circularly polarized light signals to determine information regarding the polarized light source emitter. Inspired by these unique visual characteristics, the Mantis Shrimp Optimization Algorithm (MShOA) mathematically covers three visual strategies based on the detected signals: random navigation foraging, strike dynamics in prey engagement, and decision-making for defense or retreat from the burrow. These strategies balance exploitation and exploration procedures for local and global search over the solution space. MShOA’s performance was tested with 20 testbench functions and compared against 14 other optimization algorithms. Additionally, it was tested on 10 real-world optimization problems taken from the IEEE CEC2020 competition. Moreover, MShOA was applied to solve three studied cases related to the optimal power flow problem in an IEEE 30-bus system. Wilcoxon and Friedman’s statistical tests were performed to demonstrate that MShOA offered competitive, efficient solutions in benchmark tests and real-world applications.

Suggested Citation

  • José Alfonso Sánchez Cortez & Hernán Peraza Vázquez & Adrián Fermin Peña Delgado, 2025. "A Novel Bio-Inspired Optimization Algorithm Based on Mantis Shrimp Survival Tactics," Mathematics, MDPI, vol. 13(9), pages 1-54, May.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:9:p:1500-:d:1647911
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    References listed on IDEAS

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    1. Aijun Zhu & Zhanqi Gu & Cong Hu & Junhao Niu & Chuanpei Xu & Zhi Li, 2021. "Political optimizer with interpolation strategy for global optimization," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-40, May.
    2. Yenny Villuendas-Rey & José L. Velázquez-Rodríguez & Mariana Dayanara Alanis-Tamez & Marco-Antonio Moreno-Ibarra & Cornelio Yáñez-Márquez, 2021. "Mexican Axolotl Optimization: A Novel Bioinspired Heuristic," Mathematics, MDPI, vol. 9(7), pages 1-20, April.
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