Multi-Scale Quantum Harmonic Oscillator Behaved Algorithm with Three-Stage Perturbation for High-Dimensional Expensive Problems

Quantum perturbation plays an important role in quantum movement. This paper proposes a three-stage perturbation (TSP) framework to enhance the performance of multi-scale quantum harmonic oscillator algorithm (MQHOA). Three perturbations are adopted in the population initialization process with opposition-based learning (OBL), in quantum harmonic oscillator (QHO) process with ensemble of three differential evolution (DE) strategies and in multi-scale (M) process with a rollback mechanism to enhance the diversity of the particles and prevent from falling into local optima. The proposed approach has been evaluated on several high-dimensional expensive problems from 50-D to 500-D. The empirical data are compared with recent MQHOA variants and some state-of-the-art similar metaheuristic algorithms (MAs). The experimental results reveal the superiority or competitiveness of the the proposed approach.