Orthogonal Learning Rafflesia Optimization Algorithm with Quasi-Affine Transformation Evolutionary and Its Application in Wireless Sensor Networks

The Rafflesia Optimization Algorithm (ROA) is an optimization algorithm that mimics the growth cycle of the Rafflesia. Building upon the ROA, this study introduces a novel heuristic algorithm called Orthogonal Learning Quasi-Affine Transformation Evolutionary Rafflesia Optimization Algorithm (OLQROA). The QUATRE method and the Orthogonal Learning approach are combined in the OLQROA algorithm. Compare OLQROA with ROA algorithm, improved ROA algorithms, and other three mature algorithms using CEC2017 benchmark function. The outcomes of the experiments show that OLQROA works better than the aforementioned algorithms. Additionally, OLQROA is used to apply three-dimensional wireless sensor coverage, producing superior results in comparison to the afore-mentioned algorithms.