Abstract
<jats:p>Optimization deals with finding the best solution within complex, high-dimensional, and often constrained search spaces. Optimization methods can be solved using traditional analytical methods which employ mathematical procedures to guarantee optimality but are usually limited in applicability to non-linear, non-differentiable or non-convex problems. To avoid these limitations, nature-inspired heuristic methods are utilized to generate acceptable solutions. Among nature-inspired meta-heuristics, Swarm Intelligence (SI)-based methods stand out for their scalability, flexibility, and applicability due to their decentralized and self-organizing abilities. Multiple interactions, positive and negative feedback mechanisms, stochastic fluctuations in SI-based systems lead to collective intelligence to effectively search discrete, high-dimensional and complex solution space. One of the prominent examples of SI-based algorithms, the Artificial Bee Colony (ABC) algorithm mimics the foraging behavior of honeybee colonies. In ABC, there are three distinct types of agents: employed bees which exploit discovered high-quality solutions and share their information by dancing to recruit other bees to promising regions; onlooker bees search the vicinity of solutions selected probabilistically based on the information transmitted through dancing; and scout bees which explore new food source regions to bring diversity to the population. By iteratively balancing exploration and exploitation through self-organization mechanisms, ABC has been applied to a wide range of complex problems successfully in engineering, computer science, and computational biology.</jats:p>