A Modified Artificial Bee Colony Algorithm for Dynamic Optimization Problems

<span style="color: rgb(51, 51, 51); font-family: &quot;HelveticaNeue Regular&quot;, sans-serif; font-size: 18px;">Dynamic optimization problems (DOPs) are characterized by time-varying search spaces, which pose challenges to traditional algorithms. To address these challenges, a modified Artificial Bee Colony (ABC) algorithm is proposed in which the best solution is re-evaluated at the beginning of each cycle. When a change is detected, all candidate solutions are updated, the best one is retained, and the remaining solutions are transferred to the scout phase to explore new solutions in the search space. New solutions are compared, and the best one is preserved. Experiments were conducted on the dynamic Rastrigin, Rosenbrock, Sphere, and Griewank functions. The proposed method was shown to achieve faster convergence and higher solution quality compared to the standard ABC, while diversity and convergence stability were maintained relative to recent DOP-ABC variants.</span>