An Improved Quantum Inspired Particle Swarm Optimization for Forest Cover Prediction

Forest cover prediction plays a crucial role in assessing and managing natural resources, biodiversity, and environmental sustainability. Traditional optimization algorithms have been employed for this task, but their effectiveness and efficiency in handling complex forest cover prediction problems are limited. This paper presents a novel approach, Annealing Lévy Quantum Inspired Particle Swarm Optimization (ALQPSO) that combines principles from quantum computing, particle swarm optimization; annealing, and Lévy distribution to enhance the accuracy and efficiency of forest cover prediction models by significant feature selection. The proposed algorithm utilizes quantum-inspired operators, such as quantum rotation gate, superposition, and entanglement, to explore the search space effectively and efficiently. By leveraging the principle of Lévy distribution and annealing, ALQPSO facilitated the exploration of multiple potential solutions simultaneously, leading to improved convergence speed and enhanced solution quality. To evaluate the performance of ALQPSO for forest cover prediction, experiments are conducted on the forest cover dataset. Initially, exploratory data analysis is performed to determine the nature of features. Thereafter, feature selection is performed through the proposed ALQPSO algorithm and compared with Quantum-based PSO (QPSO) and its variants. The experiments are conducted on all potential fields to identify the best among them. The experimental analysis demonstrates that ALQPSO outperforms traditional algorithms in terms of prediction accuracy, convergence speed, and solution quality (in terms of a number of features), highlighting its efficacy in addressing complex forest cover prediction problems.