Chaotic Harris Hawks Optimization for Unconstrained Function Optimization

Swarm-based techniques, a form of meta-heuristic techniques, are derived from the swarm system's social conduct in nature. A newly brought optimization algorithm is Harris Hawks Optimization (HHO) that is stimulated through looking conduct of Harris Hawks (agents) of finding food (optimal solution). Balancing between exploitative and exploratory processes of the original HHO algorithm is desirable for achieving better performance to many optimization problems. An algorithm, Chaotic Harris Hawks Optimization (CHHO), is proposed in this work for the unconstrained function optimization. The CHHO algorithm is presented based on adjusting the exploration mechanism of the original HHO algorithm. Ten chaotic maps are used to control this mechanism instead of random adjusting. The unimodal, multimodal, and multimodal based fixed-dimension benchmark functions are used to compare the CHHO algorithm with the HHO algorithm. Visibility of the CHHO algorithm for optimizing the unconstrained benchmark function, with the intelligible effect of the Gauss and Logistic chaotic maps over other maps, is shown in the experiments.