Abstract:
Honey Bee Algorithm Optimization (HBAO) is a swarm-intelligence algorithm that can
address the issues of optimization. It is based on the complex foraging behavior of honeybee
colonies. The algorithm aims to travel solution spaces, find optimum solutions, and adapt to
dynamic changes in the optimization environment, much like bees locate new food sources,
communicate through complex dances, and make decisions as a group to exploit the most
suitable places. Scheduling, routing, clustering, and other combinatorial optimization tasks
are among the domains in which the HBAO is applied. HBAO can achieve the balance
between exploration and exploitation in their foraging activity, which helps the colony find
and utilize rich food sources efficiently. This thesis aims to explore the application of the
HBAO in optimizing problem-solving processes by addressing the delicate balance between
exploitation and exploration. The study focuses on enhancing the algorithm's speed while
maintaining its efficiency. The ultimate goal is to provide a framework that not only refines
problem-solving capabilities but also accelerates the convergence of the algorithm for
practical applications.
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