crocodile hunting strategy (chs): a comparative study using benchmark functions

The crocodiles have a good strategy for hunting the fishes in nature. these creatures are divided into two groups of chasers and ambushers when hunting. the chasers direct prey toward shallow water with a powerful splash of its tail without catching them, and the ambushers wait in the shallow and try to snatch the fishes. such behavior inspires the development of a new population-based optimization algorithm called the crocodile hunting strategy (chs). in order to verify the performance of the chs, several classical benchmark functions and four constrained engineering design optimization problems are used. in the classical benchmark function, the comparisons are performed using ant colony optimization, differential evolution, genetic algorithm, and particle swarm optimization. constrained engineering design problems are compared with firefly algorithm, harmony search, shuffled frog-leaping algorithm, and teaching-learning-based optimization. the results of the comparison show that different operators designed in the chs algorithm lead to fast algorithm convergence and show better results compared to other algorithms.