IEEE Trans Cybern. 2020 Feb;50(2):650-663. doi: 10.1109/TCYB.2018.2870981. Epub 2018 Oct 8.
This paper proposes a new rule-based cooperative framework for multiobjective evolutionary fuzzy systems (FSs). Based on the framework, a multiobjective rule-based cooperative continuous ant-colony optimization (MO-RCCACO) algorithm is proposed to optimize all of the free parameters in FSs. Instead of optimization using a single colony of FSs (solutions), the MO-RCCACO consists of r subcolonies of size N cooperatively optimizing an FS that consists of r rules, with a subcolony optimizing only a single fuzzy rule. In addition, an auxiliary colony is created to store all of the fuzzy rules in the best-so-far N FSs to enhance the optimization ability of MO-RCCACO. The performance ranking of different fuzzy rules in the same subcolony is performed based on the multiobjective function values of their participating FSs by using Pareto nondominated sorting and the crowding distance. The MO-RCCACO is applied to find the Pareto-optimal fuzzy controllers (FCs) of a mobile robot for wall following with multiple control objectives. The optimization ability of the MO-RCCACO is verified through comparisons with various multiobjective population-based optimization algorithms in the robot wall-following control problem. Experimental results verify the effectiveness of the MO-RCCACO-based FCs for the boundary following control of a real robot.
本文提出了一种新的基于规则的多目标进化模糊系统（FS）合作框架。基于该框架，提出了一种多目标基于规则的合作连续蚁群优化（MO-RCCACO）算法，用于优化 FS 中的所有自由参数。MO-RCCACO 由 r 个子群组成，每个子群的大小为 N，共同优化由 r 条规则组成的 FS，每个子群只优化单个模糊规则。此外，创建了一个辅助群体来存储迄今为止最好的 N 个 FS 中的所有模糊规则，以增强 MO-RCCACO 的优化能力。同一子群中不同模糊规则的性能排名是基于它们参与的 FS 的多目标函数值进行的，使用 Pareto 非支配排序和拥挤距离。将 MO-RCCACO 应用于移动机器人的墙壁跟踪多控制目标的 Pareto 最优模糊控制器（FC）的寻找。通过在机器人墙壁跟踪控制问题中与各种基于种群的多目标优化算法进行比较，验证了 MO-RCCACO 的优化能力。实验结果验证了基于 MO-RCCACO 的 FC 对真实机器人边界跟踪控制的有效性。
