Rule-Based Cooperative Continuous Ant Colony Optimization to Improve the Accuracy of Fuzzy System Design

被引：53

作者：

Juang, Chia-Feng ^{[1
]}

Hung, Chi-Wei ^{[1
]}

Hsu, Chia-Hung ^{[1
]}

机构：

[1] Natl Chung Hsing Univ, Dept Elect Engn, Taichung 402, Taiwan

来源：

IEEE TRANSACTIONS ON FUZZY SYSTEMS | 2014年 / 22卷 / 04期

关键词：

Ant colony optimization; cooperative evolution; evolutionary fuzzy systems; swarm intelligence (SI); PARTICLE-SWARM OPTIMIZATION; NEURAL-NETWORKS; EVOLUTIONARY APPROACH; INFERENCE SYSTEM; CONTROLLER; ALGORITHM; INTERPRETABILITY; IDENTIFICATION; CONVERGENCE; PREDICTION;

D O I：

10.1109/TFUZZ.2013.2272480

中图分类号：

TP18 [人工智能理论];

学科分类号：

140502 [人工智能];

摘要：

This paper proposes a cooperative continuous ant colony optimization (CCACO) algorithm and applies it to address the accuracy-oriented fuzzy systems (FSs) design problems. All of the free parameters in a zero-or first-order Takagi-Sugeno-Kang (TSK) FS are optimized through CCACO. The CCACO algorithm performs optimization through multiple ant colonies, where each ant colony is only responsible for optimizing the free parameters in a single fuzzy rule. The ant colonies cooperate to design a complete FS, with a complete parameter solution vector (encoding a complete FS) that is formed by selecting a subsolution component (encoding a single fuzzy rule) from each colony. Subsolutions in each ant colony are evolved independently using a new continuous ant colony optimization algorithm. In the CCACO, solutions are updated via the techniques of pheromone-based tournament ant path selection, ant wandering operation, and best-ant-attraction refinement. The performance of the CCACO is verified through applications to fuzzy controller and predictor design problems. Comparisons with other population-based optimization algorithms verify the superiority of the CCACO.

引用

页码：723 / 735

页数：13

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