A two-stage quantum-behaved particle swarm optimization with skipping search rule and weight to solve continuous optimization problem

摘要: Quantum-behaved particle swarm optimization (QPSO) is a recently developed heuristic method by (PSO) algorithm based on quantum mechanics, which outperforms the search ability of original PSO. But as many other PSOs, it easy to fall into local optima for complex problems. Therefore, we propose two-stage quantum-behaved with skipping rule and mean attractor weight. The first stage uses mechanism, second evolution method. It shown that improved QPSO has better performance, because discarding worst particles enhancing diversity population. proposed (called `TSQPSO') tested several benchmark functions some real-world problems then compared PSO, SFLA, RQPSO WQPSO algorithms. experiment results show our performance than others.