Agent-based modeling (ABM) offers a contemporary technique for simulating complex systems characterized by agents whose behaviors are governed by straightforward rules. These interactions among agents lead to emergent worldwide behavioral trends that are not directly coded. Over the past decade, the literature has seen a growing number of metaheuristic techniques, with many authors asserting the novelty and optimization prowess of these methods. The rules for simulating individual behavior are often surprisingly alike, even though these frameworks imitate various processes or systems. Creating new optimization frameworks typically involves adapting a collection of reused rules that have shown effectiveness in previous methods, which is the central idea in the design philosophy of many metaheuristic techniques. These shared rules are often developed without a direct focus on the resulting global pattern from individual interactions. ABM seeks to connect individual behavior rules with the resulting global patterns that emerge from collective interactions, in contrast to metaheuristics. This chapter highlights the relationship between agent-based modeling and metaheuristic frameworks. Under such conditions, several ABM approaches that produce complex global search behaviors can serve as effective optimization algorithms. A metaheuristic approach is applied through an agent-based model known as “Heroes and Cowards” to showcase the potential of this methodology. A straightforward set of rules is utilized in this model, which results in the development of two global patterns often labeled as the exploration and exploitation phases in metaheuristic research. The performance of the algorithm is assessed across various benchmark functions, including unimodal, multimodal, and hybrid types. The competitive outcomes highlight the potential synergy between these two approaches.