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.

该论文提出了半途遁地优化（HEO）算法，一种新颖的量子启发式元启发式算法，旨在解决具有复杂优化问题的崎岖地形和高维度的问题，具有高效的收敛速度。研究对 HEO 的性能进行了全面比较评估，包括与已有的优化算法（包括粒子群优化、遗传算法、人工鱼群算法、灰狼优化器和量子行为粒子群优化）的对比。主要分析涵盖了 30 维度的 14 个基准函数，证明了 HEO 在导航复杂的优化地形中的有效性和适应性，并为其性能提供了有价值的见解。在旅行推销员问题（TSP）中的简单测试也表明了其在实时应用中的可行性。