<![CDATA[The artificial bee colony (ABC) algorithm, a well-known swarm intelligence-based metaheuristic inspired by the food foraging behavior of honeybees, has been widely applied to solve complex optimization problems. Despite its effectiveness, the standard ABC algorithm suffers from drawbacks such as slow convergence rates, limited balance between exploration and exploitation, and a tendency to get stuck in local optima, thereby hindering its overall performance. This study introduces an enhanced variant of the ABC algorithm, integrating the exploration strategy of the arithmetic optimization algorithm (AOA) to overcome these limitations. The enhanced algorithm is thoroughly tested on a set of benchmark functions as well as a flexible manipulator system model. Comprehensive statistical analyses are employed to evaluate and compare the performance of the improved algorithm against the original ABC. The results demonstrate that the enhanced ABC algorithm delivers superior performance in both benchmark scenarios and the flexible manipulator application.]]>
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