A Hybrid Algorithm based on Invasive Weed Optimization and Particle Swarm Optimization for Global Optimization

In this paper, an effective combination of two Metaheuristic algorithms, namely Invasive Weed Optimization and the Particle Swarm Optimization, has been proposed. This hybridization called as HIWOPSO, consists of two main phases of Invasive Weed Optimization (IWO) and Particle Swarm Optimization (PSO). Invasive weed optimization is the nature- inspired algorithm which is inspired by colonial behavior of weeds. Particle Swarm Optimization is a swarm base Algorithm that uses the swarm intelligence to guide the solution to the goal. IWO algorithm is the algorithm which is not benefit from swarm intelligence and PSO converges to the local optimums quickly. In order to benefit from swarm intelligence and avoidance from trapping in local solutions, new hybrid algorithm IWO and PSO has been proposed. To obtain the required results, the experiment on a set of benchmark functions was performed and compared with other algorithms. The findings based on the non-parametric tests and statistical analysis showed that HIWOPSO is a more preferable and effective method in solving the high-dimensional functions.

@article{Hosseini2016,
  title     = {A Hybrid Algorithm based on Invasive Weed Optimization and Particle Swarm Optimization for Global Optimization},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {7},
  number    = {10},
  year      = {2016},
  publisher = {The Science and Information Organization},
  author    = {Zeynab Hosseini and Ahmad Jafarian},
  doi       = {10.14569/IJACSA.2016.071040},
  url       = {https://doi.org/10.14569/IJACSA.2016.071040}
}