  This paper first proposes the Halfway Escape Optimization (HEO) algorithm, a
novel quantum-inspired metaheuristic designed to address complex 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 complex optimization landscapes and providing
valuable insights into its performance. The simple test of HEO in Traveling
Salesman Problem (TSP), Pressure Vessel Design and Tubular Column Design infers
its feasibility and potential weakness in real-time applications.
