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) also infers its feasibility in real-time applications.

翻译：本文首次提出中途逃逸优化（HEO）算法，这是一种新颖的量子启发式元启发方法，旨在解决以崎岖地形和高维性为特征的复杂优化问题，同时保持高效的收敛率。研究对HEO与已建立的优化算法（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））的性能进行了全面的比较评估。主要分析涵盖14个维度为30的基准函数，证明了HEO在导航复杂优化景观中的有效性和适应性，并为其性能提供了有价值的见解。HEO在旅行商问题（TSP）上的简单测试也推断出其在实时应用中的可行性。