A novel multi-objective particle swarm optimization algorithm based on invasive weed optimization

作者：Zhang, Xuncai; Wang, Xiaoxiao; Niu, Ying; Cui, Guangzhao

来源：Journal of Computational and Theoretical Nanoscience, 2016, 13(6): 3902-3908.

DOI：10.1166/jctn.2016.5225

• Chaotic map
• Convergence speed
• Invasive weed optimization
• Local optimizations
• Multi-objective particle swarm optimization algorithms

In this paper, a novel chaos multi-objective particle swarm optimization algorithm is presented. This algorithm stems from two basic algorithms, named particle swarm optimization (PSO) and invasive weed optimization (IWO) respectively. Based on convergence speed and the solutions'optimization of this two algorithms, the performance of the proposed method improves excellent performance of weeds. What's more, chaotic maps greatly reduce possibility on jumping into local optimization. The performance of the proposed method is evaluated by four common two-objective problems. The simulation results demonstrate that the improved algorithm overwhelms the previously published results.

• 出版日期2016
• 单位郑州大学