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Ceren Baştemur Kaya¹

¹Department of Computer Technologies, Nevşehir Vocational School, Nevşehir Hacı Bektaş Veli University, Nevşehir 50100, Türkiye.

Biomimetics (Basel, Switzerland)

|June 25, 2026

Summary

The Sunflower Optimization (SFO) algorithm, inspired by sunflowers, is increasingly used in engineering and AI. This review found hybrid and modified SFO methods are now dominant, especially for AI and data-driven optimization tasks.

Keywords:

artificial intelligence bio-inspired algorithm metaheuristic algorithm sunflower optimization

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Area of Science:

Computational Intelligence
Optimization Algorithms
Bio-inspired Computing

Background:

The Sunflower Optimization (SFO) algorithm is a metaheuristic optimization technique inspired by sunflower plant behavior.
Its simple structure and search flexibility have led to widespread adoption in engineering and artificial intelligence (AI).
A growing body of research necessitates a systematic review to understand SFO's development and applications.

Purpose of the Study:

To conduct a comprehensive and systematic review of literature utilizing the SFO algorithm.
To analyze the application domains, evolution, and variations (standard, hybrid, modified) of SFO.
To identify current trends, limitations, and future research directions for SFO.

Main Methods:

A systematic literature search was conducted using the Scopus database.
192 studies employing the SFO algorithm were selected for analysis.
Studies were categorized into eight application domains and analyzed for SFO approach variations and temporal trends.

Main Results:

SFO has been applied across diverse fields including engineering design, energy systems, machine learning, image processing, and robotics.
Hybrid and modified SFO approaches show a significant increasing trend, particularly in AI and data-driven optimization.
The review identified key strengths, limitations, and emerging research avenues for SFO.

Conclusions:

The SFO algorithm, especially its hybrid and modified forms, is a rapidly evolving and increasingly dominant optimization tool.
Future research should focus on exploring novel applications and further refining SFO methodologies.
This review offers a valuable overview of SFO's current status and future potential in computational optimization.