LSFQPSO: quantum particle swarm optimization with optimal guided Levy flight and straight flight for solving optimization problems

As a metaheuristic algorithm, particle swarm optimization (PSO) has two main disadvantages. Firstly, it needs to set many parameters, which is not conducive to finding the optimal parameters of the model to be optimized. Secondly, it is easy to fall into the trap of local optimal. Motivated by concepts in quantum mechanics and PSO, quantum-behaved particle swarm optimization (QPSO) was proposed having better global search ability. However, QPSO is deficient in solving high-dimensional problems and performs poorly in adaptability. In this paper, in order to better solve the high-dimensional problems and more applicable to real-world optimization problems, two strategies of Levy flight (LF) and straight flight (SF) are introduced. An improved quantum particle swarm optimization with Levy flight and straight flight (LSFQPSO) is proposed. The proposed LSFQPSO algorithm is tested on 22 classic benchmark functions and three engineering optimization problems. The obtained results are compared with seven metaheuristic algorithms and evaluated according to Friedman rank test. The experiments show that LSFQPSO algorithm provides better results with superior performance in most tests compared with seven well-known algorithms, especially in solving high-dimensional problems. What's more, the proposed LSFQPSO algorithm also shows good performance in solving real-world engineering design optimization problems.