Many real-world problems can be transformed into multimodal functional optimization. Each of these problems may include several globally optimal solutions, rendering the solution of the problem progressively more difficult. In our study, we present a crowding artificial bee colony, called IABC, which exploits the concepts of crowding and explores search solutions. A crowding approach formed in niches is used to make it capable of tracking and maintaining multiple optima, resulting in good convergence of the search space with a better chance of locating multiple optima. Two new solution search mechanisms are proposed to increase population diversity and explore new search spaces. Experiments were carried out on 14 benchmark functions selected from previous literature. The results of our experiments show that our method is both effective and efficient. In terms of the quality of the success rate, the average number of optima found, and the maximum peak ratio, IABC performs better, or at least comparably, to other cutting-edge approaches.