Optimized Artificial Bee Colony Algorithm with Markov Chain

The shortcomings of the artificial bee colony algorithm (ABC) and its improved algorithm are analyzed. This paper proposes the improved Markov ABC (IMABC) dividing the ABC algorithm into two stages by verifying the Markov property of the artificial bee colony algorithm in the time dimension. In the first stage, this paper runs the ABC algorithm to obtain the initial solution space. In the second stage, this paper uses the Markov chain to reconstruct the solution space generated in the first stage and further predicts the new solution. The IMABC algorithm reduces the randomness of the artificial bee colony algorithm. At the same time, it avoids the premature algorithm caused by relying on a certain optimal value. The pseudo code of IMABC algorithm is given and its convergence complexity and optimization ability are also analyzed. The IMABC algorithm, GABC algorithm and ABC algorithm are run on 9 typical test functions to compare the convergence accuracy, convergence efficiency and running time of the algorithms. The conclusion shows that the IMABC algorithm is superior to the GABC algorithm and ABC algorithm. The influences of the segmentation parameters and solution space dimensions on the function optimization process are verified by comparison