Multiple Adaptive Strategies-based Rat Swarm Optimizer
Ziyue Xu, Xiaodan Liang, Maowei He, Hanning Chen · 2021
Rat Swarm Optimizer (RSO) is a novel Swarm-intelligence based algorithms for solving global optimization problems. Its main idea is simulating the behavior of rats chasing and fighting their prey. There is an improved RSO according to multiple adaptive strategies, named as MARSO, is proposed. The multiple adaptive strategies include adaptive learning exemplars (ALE) and adaptive population size (APS). In this paper, the performance of MARSO was validated on the 29 IEEE CEC2017 functions by comparing with several classic or novel optimization algorithms. The experimental results show these two strategies enable RSO to get more excellent performance.