Honey Bee Algorithm optimization

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. Keywords: