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. (C) 2017 Elsevier B.V. All rights reserved.