This chapter focuses on an empirical study to understand the behaviour, strengths, and weaknesses of the Bees Algorithm through experiments. It discusses the challenges of benchmarking metaheuristics, including the No Free Lunch Theorem, which states that no universally best optimisation algorithm exists. The chapter emphasises the importance of using diverse benchmarks and carefully selecting control algorithms for comparison. The study compares the Bees Algorithm with an Evolutionary Algorithm (EA), Particle Swarm Optimisation (PSO), and Artificial Bee Colony (ABC) using both standard and purpose-built benchmarks. The results show that the Bees Algorithm’s performance is comparable to other metaheuristics on standard benchmarks. However, the chapter highlights that these benchmarks might not adequately reflect real-world problems. Purpose-built benchmarks, designed to simulate specific characteristics of optimisation landscapes, are introduced to evaluate the algorithms’ performance in more realistic scenarios. These benchmarks test the algorithms’ ability to handle different search space properties, such as the number of optima, their distribution, and the presence of plateaus. The chapter concludes by emphasising the importance of using diverse and relevant benchmarks to gain a comprehensive understanding of an algorithm’s strengths and limitations.