Cooperative two-engine multi-objective bee foraging algorithm with reinforcement learning

This paper proposes a novel multi-objective bee foraging algorithm (MOBFA) based on two-engine co-evolution mechanism for solving multi-objective optimization problems. The proposed MOBFA aims to handle the convergence and diversity separately via evolving two cooperative search engines with different evolution rules. Specifically, in the colony-level interaction, the primary concept is to first assign two different performance evaluation principles (i.e., Pareto-based measure and indicator-based measure) to the two engines for evolving each archive respectively, and then use the comprehensive learning mechanism over the two archives to boost the population diversity. In the individual-level foraging, the neighbor-discount-information (NDI) learning based on reinforcement learning (RL) is integrated into the single-objective searching to adjust the flight trajectories of foraging bee. By testing on a suit of benchmark functions, the proposed MOBFA is verified experimentally to be superior or at least comparable to its competitors in terms of two commonly used metrics IGD and SPREAD.