A knowledge-driven monarch butterfly optimization algorithm with self-learning mechanism

The Monarch Butterfly Optimization (MBO) algorithm has been proved to be an efficient meta-heuristic to directly address continuous optimization problems. In the MBO algorithm, the migration operator cooperates with the butterfly adjusting operator to generate the entire offspring population. Since the individual iterations of the MBO algorithm are not self-learning, the cooperative intelligence mechanism is a random process. In this study, an improved MBO algorithm with a knowledge-driven learning mechanism (KDLMBO) is presented to enable the algorithm to evolve effectively with a self-learning capacity. The neighborhood information extracted from the candidate solutions is treated as the prior knowledge of the KDLMBO algorithm. The learning mechanism consists of the learning migration operator and the learning butterfly adjusting operator. Then, the self-learning collective intelligence is realized by the two cooperative operators in the iterative process of the algorithm. The experimental results demonstrate and validate the efficiency and significance of the proposed KDLMBO algorithm.