Abstract
In this paper, the performance of a multi- swarm Harris Hawk’s Optimization algorithm is reported on a set of benchmark functions provided by CEC2005. In literature, any multi-swarming technique has not been applied to Harris Hawk’s Optimization. In this algorithm, the population is designed to include a number of swarms. All the swarms follow the original algorithm of HHO; however, agents from different swarms are regrouped frequently. Regrouping is performed using random permutation and the global best among all the swarms is considered as Rabbit or Best Solution so far, after iteration. Results show significant improvements in benchmark functions
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