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In this article we propose the use of fuzzy systems for dynamic adjustment of parameters in the galactic swarm optimization (GSO) method. This algorithm is inspired by the movement of stars, galaxies and superclusters of galaxies under the force of gravity. GSO uses various cycles of exploration and exploitation phases to achieve a trade-off between the exploration of new solutions and exploitation...
In this paper we propose a new method for dynamic parameter adaptation in the bat algorithm (BA). BA is a metaheuristic algorithm inspired by the behavior of micro bats, which has been applied to different optimization problems obtaining good results. In this paper we propose dynamic parameter adaptation of the BA using Interval Type-2 fuzzy logic. Simulation results show that the proposed method...
In this article we use the Differential Evolution (DE) algorithm which using fuzzy logic has the mutation parameter (F) dynamic, this modification of the algorithm we call Fuzzy Differential Evolution algorithm (FDE), a comparison algorithm using type 1 fuzzy logic and interval type 2 fuzzy logic is performed by for a set of functions Benchmark.
In this paper we propose a new method based on an interval type-2 fuzzy logic system for dynamic parameter adaptation in the harmony search (HS) algorithm. The main contribution of this paper is an improvement of HS in its abilities to search in a global and local fashion, by dynamically changing some of its parameters using an interval type-2 fuzzy logic system. The fuzzy harmony search algorithm...
This work describes a method to construct type-1 intuitionistic fuzzy inference systems. This type of systems is able to handle more uncertainty than a type-1 fuzzy inference system and performs faster than a type-2 fuzzy inference system. The concepts of intuitionistic membership, and intuitionistic center of area are proposed, in order to implement a system which is similar in design than the traditional...
The main goal of the paper is the use of fuzzy logic for dynamic parameter adaptation in the Grey Wolf Optimizer (GWO) algorithm. The proposed approach of a fuzzy GWO is compared with the traditional GWO algorithm with a set of benchmark functions. Simulation results show that there is a significant advantage of the proposed fuzzy GWO.
We describe in this paper the Bat Algorithm and a proposed enhancement using a fuzzy system to dynamically adapt its parameter. The original method is compared with the proposed method and also compared with the genetic algorithm, providing a more complete analysis of the effectiveness of the bat algorithm. Simulation results on a set of mathematical functions with the fuzzy bat algorithm outperform...
This paper develops a new fuzzy harmony search algorithm (FHS) for solving optimization problems. FHS employs a novel method using fuzzy logic for adaptation of the harmony memory accepting parameter that enhances the accuracy and convergence rate of the harmony search (HS) algorithm. In this paper the impacts of constant parameters on harmony search algorithm are discussed and a strategy for tuning...
The proposed method in this paper describes the enhancement of the Cuckoo Search (CS) Algorithm via Lévy flights using a fuzzy system to dynamically adapt its parameters. The original CS method is compared with the proposed method called Fuzzy Cuckoo Search (FCS) on a set of benchmark mathematical functions. In this case we consider a fuzzy system to dynamically change parameters during the execution...
We describe in this paper the Bat Algorithm and a proposed enhancement using a fuzzy system to dynamically adapt its parameter, original method is compared with the proposed method and also compared with genetic algorithm, providing a more complete analysis of the effectiveness of the bat algorithm. Simulation results on a set of mathematical functions with the fuzzy bat algorithm outperform the traditional...
In this paper we show a statistical comparison using fuzzy systems for the benchmark case of three tank water level control. In this statistical comparison an empirical type-1 fuzzy system is applied and a type-1 fuzzy system with genetic algorithm is also used. After that a type-2 fuzzy system is used to achieve the control and a genetic algorithm is used to optimize this type-2 fuzzy system. These...
In this paper we describe an evolutionary method for the optimization of type-2 fuzzy systems based on the level of uncertainty. The proposed evolutionary method produces the best fuzzy inference systems (based on the memberships functions) for particular applications. The optimization of membership functions of the type-2 fuzzy systems is based on the level of uncertainty considering three different...
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