Random Fitness-Diversity Weight-based Hummingbird Optimization Algorithm

Optimization problems are widespread in engineering, physics, economics, biology, etc. With the deepening of research in various fields, optimization problems have become more complex. Evolutionary algorithm is an effective solution for the complex optimization problem. In this study, we propose a novel evolutionary algorithm, namely RHO, which adopts the search strategy that simulates the foraging path of hummingbirds and random fitness-diversity weight. It balances two sets of critical factors in EA, i.e, exploitation and exploration, convergence and diversity. Experimental results on numerical optimization problem set CEC2017 show that RHO has the dominant advantage over other state-of-the-art evolutionary algorithm-based optimization methods.