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

Abstract

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.

Software Development Effort Estimation Using Regression Fuzzy Models

“…The output of an FL model depends on the model type, i.e., Mamdani or Sugeno. Mamdani FL has its output(s) partitioned to memberships with shapes [45, 46]. On the other hand, in Sugeno models (aka Takagi-Sugeno-Kang model), the output is represented as a linear equation or constant.…”

Training High-Order Takagi-Sugeno Fuzzy Systems Using Batch Least Squares and Particle Swarm Optimization

“…The parameter estimation is related with determining the parameters of fuzzy sets and the coefficients of regression functions in the consequence part. These tasks can be achieved by various optimization techniques such as least squares [24,26,32], evolutionary algorithms [5,8,32] or particle swarm optimization. Particle swarm optimization (PSO) is a stochastic optimization method that was developed by Kennedy and Eberhart [9,12].…”