This paper first proposes the Halfway Escape Optimization (HEO) algorithm, a quantum-inspired metaheuristic designed to address general optimization problems. The HEO mimics the effects between quantum such as tunneling, entanglement. After the introduction to the HEO mechansims, the study presents a comprehensive evaluation of HEO's performance against extensively-used 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 problems. The test of HEO in Pressure Vessel Design and Tubular Column Design also infers its feasibility and potential in real-time applications. Further validation of HEO in Osmancik-97 and Cammeo Rice Classification achieves a higher accuracy record.

翻译：本文首次提出半途逃逸优化（HEO）算法，这是一种受量子启发的元启发式算法，旨在解决通用优化问题。HEO模拟了量子隧穿、量子纠缠等量子效应。在介绍HEO机制后，本研究对HEO性能与广泛使用的优化算法进行了全面评估，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）以及量子行为粒子群优化（QPSO）。主要分析涵盖14个维度为30的基准函数，证明了HEO在应对通用优化问题时的有效性和适应性。HEO在压力容器设计和管柱设计中的测试也表明了其在实时应用中的可行性和潜力。在Osmancik-97和Cammeo稻米分类问题上对HEO的进一步验证取得了更高的准确率记录。