A Flock-of-Starling Optimization Algorithm with Reinforcement Learning Capability
Rong Xie · 2020
Aiming at the issues of weak learning capability and low learning efficiency of classical Particle Swarm Optimization (PSO), we propose a flock-of-starling optimization algorithm (StarlingOpt) based on reinforcement learning theory to improve PSO. Agent, as the protagonist of the algorithm, is modeled to imitate the behavior of flock of starlings. Attention mechanism is applied for agent to pay attention to its own state as well as its k-neighbor's state so that it can focus on more attentions to those valuable state information. Meanwhile, attention alignment is proposed for agent to integrate multiple attentions for rapid learning and state update. The experimental results show that the proposed algorithm can effectively accelerate learning speed of agent in optimization process, which enable improve the capability and efficiency to find the optimal solution on optimization problem.