Due to the long and time-consuming nature of classical optimization methods, a variety of metaheuristic algorithms have recently been suggested to address real-world problems that are difficult or complex to solve. One of these metaheuristic algorithms is the recently proposed electric eel foraging optimization (EEFO) algorithm. EEFO is a metaheuristic algorithm inspired by the foraging behavior of electric eels by sending electrical signals during hunting. In this study, the Cauchy distribution method is added to the algorithm to improve the performance of EEFO. Thus, the algorithm improved with the Cauchy distribution method is called CD-EEFO. The performance of CD-EEFO was first tested on 12 different benchmark functions for 10, 30 and 50 populations. The results of the CD-EEFO are then compared with the original EEFO results. In addition, the results are supported by using box plots and convergence curves obtained with both CD-EEFO and the original EEFO. Then, the results of CD-EEFO are compared with the results of some existing algorithms in the literature. Furthermore, CD-EEFO is applied to four real-world problems and compared with the results of the original EEFO. The experimental results show that CD-EEFO achieves better results than the original EEFO.