Enzyme action optimizer: a novel bio-inspired optimization algorithm

Al-Alnemer, Loai

doi:10.1007/s11227-025-07052-w

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Enzyme action optimizer: a novel bio-inspired optimization algorithm

2025-04-01

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Al-Alnemer, Loai

Tino, Peter

DOI：10.1007/s11227-025-07052-w

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Abstract

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This paper presents the enzyme action optimization (EAO) algorithm, a novel bio-inspired optimization algorithm designed to simulate the adaptive enzyme mechanism in biological systems. EAO employs a novel strategy that dynamically balances between exploration and exploitation to efficiently navigate and optimize complex, multi-dimensional search spaces. EAO has been tested over diverse benchmark datasets, including the 23 classical benchmark functions, IEEE CEC2017, CEC2022 benchmark functions, where it has been compared with 14 recent and highly cited optimizers. The results show the superior performance of EAO over the compared optimizers in terms of finding the optimal solution, convergence speed, robustness, and overall performance. Furthermore, EAO was applied to solve five engineering design problems and demonstrated excellent performance results. The source code of EAO is publicly available for both MATLAB at: https://www.mathworks.com/matlabcentral/fileexchange/170296-enzyme-action-optimizer-a-novel-bio-inspired-optimization and PYTHON at: https://github.com/AliRodan/Enzyme-Action-Optimizer.

Keywords:

Metaheuristic

Optimization

Heuristic

Enzyme action optimizer

EAO

Engineering design problems

Particle swarm optimization

Swarm intelligence optimization

Artificial intelligence

Global optimization