This book focuses on the fields of fuzzy logic and metaheuristic
algorithms, particularly the harmony search algorithm and fuzzy control.
There are currently several types of metaheuristics used to solve a
range of real-world of problems, and these metaheuristics contain
parameters that are usually fixed throughout the iterations. However, a
number of techniques are also available that dynamically adjust the
parameters of an algorithm, such as probabilistic fuzzy logic.
This book proposes a method of addressing the problem of parameter
adaptation in the original harmony search algorithm using type-1,
interval type-2 and generalized type-2 fuzzy logic. The authors applied
this methodology to the resolution of problems of classical benchmark
mathematical functions, CEC 2015, CEC2017 functions and to the
optimization of various fuzzy logic control cases, and tested the method
using six benchmark control problems - four of the Mamdani type: the
problem of filling a water tank, the problem of controlling the
temperature of a shower, the problem of controlling the trajectory of an
autonomous mobile robot and the problem of controlling the speed of an
engine; and two of the Sugeno type: the problem of controlling the
balance of a bar and ball, and the problem of controlling control the
balance of an inverted pendulum. When the interval type-2 fuzzy logic
system is used to model the behavior of the systems, the results show
better stabilization because the uncertainty analysis is better. As
such, the authors conclude that the proposed method, based on fuzzy
systems, fuzzy controllers and the harmony search optimization
algorithm, improves the behavior of complex control plants.
