Zebrafish Optimization Algorithm for Global Optimization Problems
Pub. online: 9 July 2026
Type: Research Article
Open Access
Open Access
Received
1 January 2026
1 January 2026
Accepted
1 June 2026
1 June 2026
Published
9 July 2026
9 July 2026
Abstract
A new population-based metaheuristic called Zebrafish Optimization Algorithm (ZFO) is proposed to find the global optimum solution using foraging behaviour of zebrafish larvae (Danio rerio). In ZFO, an exploration and exploitation trade-off is realized by simulation of exploratory dispersion, collaborative shoaling and directional searching. The movement strategy incorporates a stagnation-avoiding reinitialization mechanism, ensuring that diversity is maintained and premature convergence is avoided. To demonstrate the efficiency of ZFO, 23 benchmark functions including unimodal, multimodal and fixed dimension multimodal functions are tested and their optimal solutions are searched. Results obtained by ZFO are compared with existing metaheuristic algorithms, namely Cuttlefish Optimization, Jellyfish Search, Pufferfish Optimizer and Krill Herd algorithm. The Friedman rank test and Wilcoxon signed-rank test show that ZFO yields better-quality solutions than the compared algorithms in terms of convergence speed and robustness. The novel characteristic of ZFO is that it combines biologically inspired zebrafish dynamics and adaptive diversification strategy to enhance the capability of global search.
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Biographies
Suruthi Manickavasagam
M. Suruthi is a research scholar in the School of Computer Science and Engineering at Vellore Institute of Technology, Chennai, India. Her research spans towards mathematical optimization, engineering optimization, operations research, machine learning, AI-based optimization and computational methods, reflecting both theoretical rigour and practical applicability. She works across classical linear and nonlinear programming to modern metaheuristic and swarm intelligence techniques, addressing complex real-world challenges through innovative algorithmic approaches. Her contributions to nature inspired optimization bridge traditional mathematical methods with emerging intelligent computing paradigms. As an active and prolific researcher, she continues to advance multi-domain optimization research within the broader academic community.
Ganesh Narayanan
N. Ganesh brings a wealth of experience to his role as a professor in the School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India. He is recognized as one of the Top 2% Scientists by Elsevier and Stanford University, USA. With a career spanning more than two decades in teaching, training and research, he has established himself as an authority in this field. His research interests are diverse and forward thinking, encompassing software engineering, agile software development, prediction and optimization techniques, deep learning, image processing and data analytics.
