Octopus Optimization Algorithm (OOA)

Octopus Optimization Algorithm for solving the global and constraint optimization from engineering structural design problems

This paper constructs an exploration mechanism inspired by the hunting behaviors of marine octopuses, along with an exploitation mechanism based on their mating behaviors. These mechanisms aim to balance convergence speed and solution accuracy using a specially designed stochastic regulatory factor. This paper develops a nature-swarm phenomenon-based search strategy and mathematical model, named the Octopus Optimization Algorithm (OOA), by simulating processes of octopuses searching for potential prey, escaping natural predators, attacking prey, and mating behaviors. In addition, inspired by the water-spraying recoil and transient acceleration phenomenon, a recoil motion-based stochastic feedback mechanism is proposed by designing a unique recoil operator to achieve information exchange in different search spaces. To demonstrate the universal applicability of the proposed OOA algorithm, we qualitatively analyzed swarm convergence and swarm search behaviors, population diversity, exploration and exploitation performance on multiple benchmarks covering unimodal, multimodal, fixed-dimensional, and composite functions and quantitatively verified convergence, effectiveness, significance, robustness, population diversity, exploration and exploitation efficiency, progressive scalability, and parameter sensitivity on the CEC2017 suites with 10, 30, 50, and 100 dimensions. The nonparametric test significance results show the proposed OOA algorithm demonstrates statistically significant advantages in computational performance and scalability.

Main Paper: Kaiguang Wang, Laith Abualigah, Aseel Smerat, Jiahang Li, Xiangjuan Wu, Hao Liu, Zhongshi Shao, Seyedali Mirjalili, A nature recoil mechanism-based Octopus Optimization Algorithm for solving the global and constraint optimization from engineering structural design problems, Journal of Computational Design and Engineering, 2025;, qwaf139, https://doi.org/10.1093/jcde/qwaf139

Github:kaiguangnxu/OOA