Multiscale Quantum Harmonic Oscillator Algorithm With Guiding Information For Single Objective Optimization

The multiscale quantum harmonic oscillator algorithm (MQHOA) is a competitive heuristic optimization algorithm that has been successfully implemented in many applications. This paper proposes a novel way to optimize MQHOA to further improve its performance. The idea is to use the historical information in the evolutionary iterative process of the algorithm to derive a direction in the quantum harmonic oscillator (QHO) process and as a multi scale in the M process, then take the guidance information as the parameter to generate a new solution. The combination of these processes, i.e., MQHOA combined with the guidance information, is called GI-MQHOA. The experimental results show that the guidance information is of great significance for the exploration and exploitation of MQHOA. The proposed algorithm was evaluated on the CEC2014 test suite, and shown to be comparable to other state-of-the-art swarm intelligence and heuristic algorithms. The principle of using guiding information is simple and effective and can be easily transplanted to other heuristic algorithms.