Halfway Escape Optimization: A Quantum-Inspired Solution for General Optimization Problems

This paper first proposes the Halfway Escape Optimization (HEO) algorithm, a quantum-inspired metaheuristic designed to address general optimization problems characterized by rugged landscapes and high-dimensionality with an efficient convergence rate. The study presents a comprehensive comparative evaluation of HEO's performance against established optimization algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Fish Swarm Algorithm (AFSA), Grey Wolf Optimizer (GWO), and Quantum behaved Particle Swarm Optimization (QPSO). The primary analysis encompasses 14 benchmark functions with dimension 30, demonstrating HEO's effectiveness and adaptability in navigating general optimization landscapes and providing valuable insights into its performance. The test of HEO in Pressure Vessel Design and Tubular Column Design infers its feasibility and potential in real-time applications. Further validation in Osmancik-97 and Cammeo Rice Classification proves the effectiveness of HEO and achieves a higher accuracy record.