Recurrent fuzzy system design using mutation-aided elite continuous ant colony optimization

Chen, Chi‐Chung; Shen, Li

doi:10.1109/fuzz-ieee.2016.7737887

Cited by 5 publications

(1 citation statement)

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“…Although those variants indeed helped accomplish the accurate design of fuzzy systems, there is still possible room for further improvement, especially on the balance between the exploration and convergence rate of solutions. This paper introduces the dynamic mutation [ 29 ] into ACO R to enhance such balance.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Ant Colony Optimization with Dynamic Mutation and Ad Hoc Initialization for Improving the Design of TSK-Type Fuzzy System

Chen

Liu

2018

Computational Intelligence and Neuroscience

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This paper proposes an enhanced ant colony optimization with dynamic mutation and ad hoc initialization, ACODM-I, for improving the accuracy of Takagi-Sugeno-Kang- (TSK-) type fuzzy systems design. Instead of the generic initialization usually used in most population-based algorithms, ACODM-I proposes an ad hoc application-specific initialization for generating the initial ant solutions to improve the accuracy of fuzzy system design. The generated initial ant solutions are iteratively improved by a new approach incorporating the dynamic mutation into the existing continuous ACO (ACOR). The introduced dynamic mutation balances the exploration ability and convergence rate by providing more diverse search directions in the early stage of optimization process. Application examples of two zero-order TSK-type fuzzy systems for dynamic plant tracking control and one first-order TSK-type fuzzy system for the prediction of the chaotic time series have been simulated to validate the proposed algorithm. Performance comparisons with ACOR and different advanced algorithms or neural-fuzzy models verify the superiority of the proposed algorithm. The effects on the design accuracy and convergence rate yielded by the proposed initialization and introduced dynamic mutation have also been discussed and verified in the simulations.

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Section: Introductionmentioning

confidence: 99%