Most of the optimization techniques come under evolutionary and swarm intelligence techniques inspired by the behavior of different species around the world. These techniques play a vital role in solving a wide range of nondeterministic complex optimization problems and intellectually solving various real-world problems. This research paper includes a novel approach inspired by the successive hunting ability of lion from cub to predator is called Evolution of Cub to Predator (ECP). ECP algorithm based on evolving hunting ability of cub learned from parent-lion, environment, and siblings. In the proposed algorithm, lion-cub learns their hunting ability in two ways; initially from parents and resident mate cubs called infant-maturity. Later on, the cub gets matured by develop their hunting ability through real-world executions. The interpretations of cub's intellectual, social behaviour towards its environment place them on top position in the survival of the fittest later on. The investigation includes ten different benchmark test functions for evaluating the performance of the ECP. The result exhibits the proficient execution of ECP for searching the global optimum with several benchmark functions.