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Octopus optimization algorithm: a novel single- and multi-objective optimization algorithm for optimization problems
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DOI：10.1007/s10586-025-05141-2.png)
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Abstract
En 中文
This paper proposes two novel optimization algorithms: the Octopus Optimization Algorithm (OOA) and its multi-objective version Multi-objective Octopus Optimization Algorithm (MOOA). OOA is inspired by the behavior of octopuses in nature. It is a meta-heuristic algorithm that uses the movement of octopuses to explore the search space and find the optimal solutions for optimization problems. MOOA is an extension of OOA for solving problems with multiple conflicting objectives. In MOOA, a population-grouping approach is proposed to maintain diversity and convergence when dealing with multi-objective optimization problems. Both of the proposed algorithms are tested in various experiments, and the result demonstrates the algorithms’ robustness, scalability, and effectiveness. The source code is https://github.com/Chrisong-gh/MOOA .
Keywords:
Optimization
Multi-objective
Metaheuristic
Octopus optimization algorithm
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