The chapter aims to present the basic concepts of learning strategies in metaheuristic algorithms and provide an in-depth review of their essential characteristics and advantages. Most metaheuristic methods prioritize the best solution collectively found so far, often disregarding the individual strategies of each agent within the population. While this approach can yield notable results, it also introduces significant drawbacks, such as premature convergence. This chapter presents a metaheuristic method that balances individual and social learning in search agents. Under this framework, each agent employs two strategies: an individual search mechanism driven by the agent's independent exploration and a social strategy leveraging the best-known collective solution. This approach frames the search process as a learning problem, where agents dynamically adjust the balance between individual and social strategies. A random counter assigned to each agent determines the frequency of each strategy, ensuring diverse search patterns and fostering a dynamic, adaptive process. This mechanism enhances efficiency in solving complex optimization problems. The method's effectiveness was validated through a comparison with established metaheuristic algorithms using 20 benchmark test functions. The results demonstrate that the proposed approach outperforms popular algorithms, delivering superior solutions and achieving faster convergence rates.