Fruit Fly Optimization Algorithms for Complex Problem Solving

Fruit Fly Optimization Algorithms (FOAs) are bio-inspired metaheuristic techniques modelled on the foraging behaviour of fruit flies. In their natural environment, fruit flies employ a two-stage process of olfactory detection and visual localisation to locate food sources. Mimicking this, FOAs initialise a swarm of virtual “flies” that randomly explore a problem’s solution space using a simulated sense of smell (osphresis) and then converge towards promising regions via a simulated vision step. The simplicity of the basic algorithm, combined with low computational overhead, has led to its widespread adoption in diverse domains such as structural design, antenna synthesis, machine-learning parameter tuning and unmanned aerial vehicle path planning. Yet, the original FOA may suffer from premature convergence or entrapment in local optima when tackling high-dimensional or highly non-convex problems. To address these challenges, researchers have introduced enhancements including chaotic maps, immune-inspired mechanisms, orthogonal crossover, quantum-behavioural search operators and hybrid strategies. These developments strengthen global exploration, improve convergence precision and expand the algorithm’s applicability to real-world engineering and data-driven problems.

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Recent work has focused on integrating advanced search mechanisms to bolster both exploration and exploitation. A quantum-behaviour‐based FOA incorporates a quantum potential well around the swarm’s location, allowing virtual flies to follow wave-function-driven probabilistic moves rather than classical steps. This approach accelerates convergence and reduces trapping in suboptimal regions; its performance has been validated in three-dimensional UAV path-planning scenarios, achieving smoother and more efficient flight trajectories. In structural optimisation, an FOA variant inspired by immune system principles employs self–non-self recognition and memory-forgetting processes to manage large-scale truss design problems. The algorithm demonstrates marked improvements in stability and computational efficiency over the standard FOA. Furthermore, in the field of antenna array synthesis, an advanced FOA blends orthogonal crossover, quantum selection and simulated annealing within an adaptive expansion–contraction framework. This hybrid strategy enhances population diversity, maintains balanced selection pressure and outperforms several leading evolutionary algorithms on both linear and circular subarray synthesis tasks.

Fruit Fly Optimization Algorithms for Complex Problem Solving publication trend

The graph below shows the total number of articles in fruit fly optimization algorithms for complex problem solving across all publications each year (not limited to Nature Index journals).

Technical terms

Fruit Fly Optimization Algorithm (FOA): A swarm intelligence metaheuristic inspired by the olfactory and visual foraging behaviour of fruit flies.

Swarm Intelligence: A computational paradigm based on the collective behaviour of decentralised, self-organised systems.

Osphresis: The simulated sense of smell process in FOA used to evaluate candidate solutions.

Exploration: The algorithm’s capacity to search widely across the solution space.

Exploitation: The algorithm’s ability to refine solutions within a promising region.

Quantum Potential Well: A search mechanism that uses quantum theory principles to introduce probabilistic moves and enhance convergence.

References

1. Advanced Fruit Fly Optimization Algorithm and Its Application to Irregular Subarray Phased Array Antenna Synthesis. IEEE Access (2019).
2. Quantum Behavior-Based Enhanced Fruit Fly Optimization Algorithm with Application to UAV Path Planning. International Journal of Computational Intelligence Systems (2020).