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.

翻译：本文首次提出了中途逃逸优化（HEO）算法，这是一种量子启发的元启发式算法，旨在以高效的收敛速度解决具有崎岖景观和高维特征的通用优化问题。本研究对HEO的性能与现有优化算法（包括粒子群优化（PSO）、遗传算法（GA）、人工鱼群算法（AFSA）、灰狼优化器（GWO）和量子行为粒子群优化（QPSO））进行了全面的比较评估。主要分析涵盖了14个维度为30的基准函数，证明了HEO在探索通用优化景观方面的有效性和适应性，并为其性能提供了有价值的见解。在压力容器设计和管柱设计中对HEO的测试，推断出其在实际应用中的可行性和潜力。在Osmancik-97和Cammeo水稻分类中的进一步验证证明了HEO的有效性，并取得了更高的准确率记录。