Modeling of Hybrid Henry Gas Solubility Optimization Algorithm with Deep Learning-Based LED Driver System

Light emitting diodes (LEDs) have become an effective lighting solution because of the characteristics energy efficiency, flexible controllability and extended lifetime. They find use in numerous systems for residents, industries, enterprises street applications. The efficiency trustworthiness LED considerably based on thermal mechanical loading improved several degradation schemes respective interfaces. complication limits theoretic interpretation core reasons luminous variation or formation direct correlation among aging output. Therefore, this paper designs a new hybrid Henry gas solubility optimization with deep learning (HHGSO-DL) algorithm driver system design. presented HHGSO-DL technique mainly concentrates derivation empirical relationships design parameters, outcomes product. In technique, bidirectional long short-term memory (BiLSTM) is executed examining relationship its hyperparameters can be tuned by HHGSO algorithm. work, derived integration traditional HGSO oppositional-based (OBL) concept. performance investigated chip packaging luminaire loading. extensive results demonstrate promising over other state-of-the-art approaches.