Abstract
This paper presents a hybrid algorithm based on invasive weed optimization (IWO) and particle swarm optimization (PSO), named IW-PSO. IWO is a relatively novel numerical stochastic optimization algorithm. By incorporating the reproduction and spatial dispersal of IWO into the traditional PSO, exploration and exploitation of the PSO can be enhanced and well balanced to achieve better performance. In a set of 15 test function problem, the parameters of IW-PSO were analyzed and selected, and the computational results show that IW-PSO can effectively obtain higher quality solutions so as to avoid being trapped in local optimum, comparing with PSO and IWO.
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