A novel bio-inspired optimization algorithm called Elephant Search Algorithm (ESA) is proposed in this paper. ESA emerges from the hybridization of evolutionary mechanism and dual balancing of exploitation and exploration. The design of ESA is inspired by the behavioral characteristics of elephant herds; hence the name Elephant Search Algorithm which divides the search agents into two groups representing the dual search patterns. The male elephants are search agents that outreach to different dimensions of search space afar; the female elephants form groups of search agents doing local search at certain close proximities. By computer simulation, ESA is shown to outperform other metaheuristic algorithms over some benchmarking optimization functions. In terms of fitness values in optimization, ESA is ranked after Firefly algorithm showing superior performance over the other ones. The performance of ESA is most stable when compared to all other metaheuristic algorithms.