PHUA: A Phone-handling User Algorithm Inspired by Human Mobile Usage Behavior for Global Optimization

In this paper, we propose a new meta-heuristic algorithm, the Phone Operator User Algorithm (PHUA), based on the behavioral patterns of human mobile phone usage. The algorithm mimics the behavioral strategies that humans use to decide when and how to respond to mobile phone notifications. By simulating strategies such as perception triggering, priority evaluation, delayed response, mandatory inspection, do not disturb, and rest, the balance between exploration and exploitation in the global search process is optimized. We evaluate the performance of PHUA through several standard test function experiments and compare it with other classic optimization algorithms such as genetic algorithms, simulated annealing, and particle swarm optimization. Experimental results show that PHUA has good performance in solving multi-dimensional complex optimization problems. Compared with traditional algorithms, the PHUA algorithm converges faster, has stronger global search capabilities, and is better able to escape local optima. Standard benchmark functions such as Sphere, Rastrigin, and Rosenbrock were used in the experiment, and the performance was compared by indicators such as accuracy and convergence speed. Statistical significance tests (such as t-tests) confirmed the robustness and superiority of the results. The PHUA algorithm is particularly suitable for practical applications such as educational resource scheduling and adaptive learning optimization. Although the PHUA algorithm shows excellent performance, it also has limitations such as moderate computational cost and sensitivity to parameter settings.