Performance Analysis of Multi-Scale Quantum Harmonic Oscillator Global Optimization Algorithm in Combinatorial Optimization Problems

The multi-scale quantum harmonic oscillator algorithm (MQHOA) is a novel optimization algorithm based on the wave function of one-dimensional quantum harmonic oscillator. The process for solving traveling salesman problem (TSP) using MQHOA is proposed, and the physical meanings and theoretical convergence process of MQHOA are analyzed. The experiments for 12 groups of typical TSP data show that the neighborhoods generated on Gaussian distribution are better than those on random distribution. MQHOA for TSP is better than simulated annealing algorithm on the ratio of getting precise route and the average shortest distance. The comparison with other algorithms also proves the good performance of MQHOA. The performance about regular city data set has also been researched. The experiments results prove that MQHOA is an excellent algorithm to solve combinatorial optimization problems.