What question did this study set out to answer?

The research aims to optimize conical solar distillers using a novel hybrid algorithm that integrates quantum and reinforcement learning techniques.

April 19, 2026Open Access

Quantum-Inspired Hybrid Bald Eagle-Ukari Algorithm with Reinforcement Learning for Performance Optimization of Conical Solar Distillers with Sand-Filled Copper Fins: A Novel Bio-Inspired Approach

Key Points

The research aims to optimize conical solar distillers using a novel hybrid algorithm that integrates quantum and reinforcement learning techniques.
Developed a Quantum-Inspired Hybrid Bald Eagle-Ukari Algorithm with Reinforcement Learning (QI-HBEUA-RL).
Tested seven distiller configurations under controlled solar radiation and ambient temperature.
Compared QI-HBEUA-RL with six state-of-the-art multi-objective optimizers.
Validated algorithm on benchmark problems and real-world applications.
Achieved a daily productivity of 7.75 L/m2/day, a 61.46% improvement over conventional designs.
Measured thermal efficiency of 61.9% and exergy efficiency of 4.02%.
QI-HBEUA-RL showed 7.42% improvement in hypervolume indicator and 29.35% reduction in inverted generational distance.

Abstract

This study introduces a novel Quantum-Inspired Hybrid Bald Eagle-Ukari Algorithm with Reinforcement Learning (QI-HBEUA-RL) for comprehensive optimization of conical solar distillers equipped with sand-filled copper conical fins. The proposed algorithm synergistically combines quantum computing principles (superposition and entanglement), bio-inspired metaheuristics (Bald Eagle Search and Ukari Algorithm), and reinforcement learning mechanisms to achieve unprecedented optimization performance in complex thermal-hydraulic systems. The QI-HBEUA-RL framework employs quantum-encoded population representation, enabling simultaneous exploration of multiple solution states, while reinforcement learning dynamically adjusts algorithmic parameters based on search landscape characteristics and historical performance data. Experimental validation tested seven distiller configurations in El-Oued, Algeria, under controlled conditions (7.85 kWh/m2/day solar radiation, 42.2 °C ambient temperature). The optimal configuration of copper conical fins with 14 g sand at 0 cm spacing achieved: daily productivity of 7.75 L/m2/day (+61.46% improvement over conventional design), thermal efficiency of 61.9%, exergy efficiency of 4.02%, and economic payback period of 5.8 days. Comprehensive algorithm comparison against six state-of-the-art multi-objective optimizers (NSGA-II, MOEA/D, MOPSO, MOGWO, MOHHO) across 30 independent runs demonstrated statistically significant superiority (p < 0.001, Wilcoxon test). QI-HBEUA-RL achieved 7.42% improvement in hypervolume indicator, 29.35% reduction in inverted generational distance, and 19.49% better solution spacing. Generalization validation on seven benchmark problems (ZDT1-6, DTLZ2, DTLZ7) and three renewable energy applications confirmed algorithm robustness across diverse problem types. Three real-world case studies, remote village water supply (238:1 benefit–cost), industrial facility (100% energy reduction), and emergency relief (740× cost savings) validate practical implementation viability. This research advances solar thermal desalination technology and multi-objective optimization methodologies, providing validated solutions for sustainable freshwater production in water-scarce regions.

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Cite This Study

Loey et al. (Fri,) studied this question.

synapsesocial.com/papers/69e47440010ef96374d8ffc3 https://doi.org/https://doi.org/10.3390/ai7040145

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