Micro Multi-Strategy Multi-Objective Artificial Bee Colony Algorithm for Microgrid Energy Optimization

Multi-objective evolutionary algorithm (MOEA) has become a common and effective method to solve real-world multi-objective optimization problems. However, in some practical problems, such as the microgrid energy optimization problem (MEOP), the algorithm needs to run on the micro controller to control each distributed power supply in real time. Due limitation of hardware resources on the micro controller, the MOEAs are not suitable. The emerging micro population MOEAs are suitable for this scenario. But the micro population MOEA is vulnerable to lost diversity, resulting in its performance decline. Therefore, this paper proposes a new micro multi-strategy multi-objective ABC algorithm to solve MEOP, called μMMABC. Multi-strategy ABC optimizer is used to divide the population into multiple subgroups and produce offspring in parallel to balance the exploration and exploitation. In addition, an adaptive updating mechanism is proposed to renew the population adaptively. The mechanism can adaptively select more convergent and diverse solutions at different stages to balance the exploration and exploitation of the algorithm. Furthermore, in order to improve the performance of μMMABC on problems with irregular Pareto fronts, the reference point reconstruction with intermediate strategy is also proposed. Some benchmark test suites are used to test the performance of μMMABC. Finally, it is used to solve the MEOP. The experimental results show that the proposed algorithm is more competitive and effective than the traditional MOEAs and other micro population MOEAs in solving the MEOP.