Flower Pollination Algorithm Applications in Optimization Problems

The Flower Pollination Algorithm (FPA) is a nature-inspired metaheuristic that models the pollination behaviour of flowering plants. It combines global and local search mechanisms, drawing on cross-pollination to explore the solution space and self-pollination to refine candidate solutions. Since its inception, FPA has been employed across a wide range of optimisation problems, from engineering design to data analytics. In structural and control-system design, FPA variants have achieved high-quality solutions with reduced computational effort, while in power-system management they have balanced load dispatch and network reconfiguration tasks. In computational contexts, hybridisation with differential evolution, neural networks and other heuristic strategies has improved convergence speed and mitigated premature trapping in local optima. The algorithm’s balanced search dynamics make it well suited to high-dimensional, multimodal landscapes, and its adaptability allows the incorporation of problem-specific knowledge. Recent advances focus on adaptive parameter control, archive-based diversity maintenance and hybridisation schemes, thereby broadening FPA’s applicability to real-world tasks such as robot path planning, photovoltaic parameter estimation and multi-criteria scheduling, underlining its global significance and practical utility.

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Recent studies have introduced a range of enhancements to the original FPA to address challenges in convergence and solution quality. One enhanced variant integrates cosine cross-generation differential evolution to guide local search, employing cosine inertia weights alongside external archiving and adaptive parameter adjustment. This approach demonstrated superior performance on benchmark functions and was successfully applied to robot path planning, yielding low-cost, high-efficiency trajectories with enhanced robustness to environmental disturbances. Another study proposed a modified FPA for photovoltaic model parameter estimation, incorporating a dynamic switch probability and a step-size function to balance exploration and exploitation. When applied to cell and module parameter identification, this variant achieved improved precision and convergence, outperforming conventional FPA across multiple environmental scenarios. Additionally, a comprehensive review of FPA in electrical power-system optimisation highlighted applications in unit commitment, economic dispatch and network reconfiguration. This synthesis underscored the algorithm’s strengths in handling non-linear, combinatorial tasks with fewer iterations than alternative metaheuristics and detailed hybrid schemes that enhance global search capabilities.

Flower Pollination Algorithm Applications in Optimization Problems publication trend

The graph below shows the total number of articles in flower pollination algorithm applications in optimization problems across all publications each year (not limited to Nature Index journals).

Technical terms

Flower Pollination Algorithm: A metaheuristic inspired by natural pollination processes, combining global and local search strategies.

Metaheuristic algorithm: A high-level procedure designed to find near-optimal solutions for complex optimisation problems.

Global search: Exploration of the broader solution space to avoid premature convergence on suboptimal solutions.

Local search: Fine-tuning of candidate solutions in the neighbourhood of promising points.

Convergence speed: The rate at which an algorithm approaches its optimal or near-optimal solution.

Benchmark function: Standardised test problems used to evaluate and compare optimisation algorithm performance.

References

1. A Flower Pollination Optimization Algorithm Based on Cosine Cross-Generation Differential Evolution. Sensors (2023).
2. PV Model Parameter Estimation Using Modified FPA With Dynamic Switch Probability and Step Size Function. IEEE Access (2021).
3. Applications of Flower Pollination Algorithm in Electrical Power Systems: A Review. IEEE Access (2021).