Improved Artificial Bee Colony Algorithm Based on Reinforcement Learning

In order to overcome the basic artificial bee colony algorithm converges slowly and prematurely, the reinforcement learning is added into the artificial bee colony algorithm, in which several different updating strategies is mapped into an action used to update the nectar source location. According to the calculation of Q function value, each nectar source selects the optimal updating strategy to speed up the convergence rate. At the same time, the selection probability based on ranking is used instead of roulette wheel selection probability to keep population diversity and avoid premature convergence. Comparing with several different algorithms through the test functions and the parameter identification of Chaotic system. The results show that the proposed algorithm has higher accuracy and faster convergence rate, the feasibility and effectiveness of the algorithm is validated.