A hybrid IWO/PSO algorithm for fast and global optimization

Abstract

This paper presents a hybrid optimization algorithm which originates from Invasive Weed Optimization (IWO) and Particle Swarm Optimization (PSO). Based on the novel and distinct qualifications of IWO and PSO, we introduce IWO/PSO algorithm and try to combine their excellent features in this extended algorithm. The efficiency of this algorithm both in the case of speed of convergence and optimality of the results are compared with IWO, PSO, and some other evolutionary algorithms through a number of common multi-dimensional benchmark functions. Finally, a practical problem consisting design and optimization of an adaptive controller for a surge tank is simulated. The experimental results show that the proposed algorithm can be successfully employed as a fast and global optimization method for a variety of theoretical or practical purposes.