Dimension decided Harris hawks optimization with Gaussian mutation: Balance analysis and diversity patterns

Abstract

Harris hawks optimization (HHO) is a newly developed swarm-based algorithm and the most popular optimizer in the recent year, which mimics the cooperation behavior of Harris hawks. Although the original HHO has a specific weight compared to other state-of-the-art methods when working on the local exploitation of feasible solutions, it still may fail to achieve an excellent set of scales between locally accurate exploitation and globally exploratory search. This imbalanced behavior forms an overall perspective, which can be experienced by slow convergence, inaccuracy or inadequate search coverage, and quickly dropping into local solutions. In order to strengthen the performance of the HHO, two strategies of Gaussian mutation and a dimension decision strategy observed in the cuckoo search method are introduced into this optimizer. The mechanism of cuckoo search is very useful in improving the convergence speed of the search agents as well as sufficient excavation of the solutions in the search area, while the Gaussian mutation strategy performs well in increasing the accuracy and jumping out of the local optimum. In order to verify the remarkable performance of the proposed method, the enhanced paradigm is compared with other mate-heuristic algorithms on 30 IEEE CEC2017 benchmark functions and three typical engineering problems. The experimental results illustrate that the novel developed GCHHO has an excellent ability to achieve superior performance in competition with the original HHO as well as other well-established optimizers. Open source codes and information on original HHO algorithm is available at: https://aliasgharheidari.com/HHO.html. Supportive data and info for this research will be publicly provided in http://aliasgharheidari.com.