A New Optimization Method Based on a Paradigm Inspired by Nature

Astudillo, Leslie; Melín, Patricia; Castillo, Oscar

doi:10.1007/978-3-642-15111-8_17

Cited by 9 publications

(5 citation statements)

References 9 publications

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“…Astudillo et al [4][5][6][7], defined the elementary terminology for characterizing and classifying artificial chemicals. Because the laws of reaction and rep-…”

Section: The Cra Paradigmmentioning

confidence: 99%

“…-Based on the above evaluation, we select some of the elements/compounds to "induce" a reaction. -Taking into consideration the result of the reaction, evaluations of the news element/compounds are obtained and selected elements are those of greater fitness -Repeat the steps until the algorithm meets the terminating criteria (the desired result in the maximum number of iterations is reached) [6].…”

Section: Double-substitution Reactionsmentioning

confidence: 99%

“…The CRA optimization algorithm was originally developed by Astudillo et al [6], which is based on a metaheuristic of a population that does not change in size, in addition to applying a generalization of chemical reactions as exploration and exploitation of mechanisms. The algorithm uses chemical reactions by changing at least one of the substances (element or compound), changing their composition and sets of properties.…”

Section: Introductionmentioning

confidence: 99%

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Chemical Reaction Algorithm for Type-2 Fuzzy Control Optimization in Mobile Robots

O¹,

Castillo²,

Soria³

2018

JAMRIS

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In this work the optimization process of the tracking and reactive controllers for a mobile robot are presented. The Chemical Reaction Algorithm (CRA) is used to find the optimal parameter values of the membership functions and rules for the reactive and tracking controllers. In this case, we are using five membership functions in each variable of the fuzzy controllers. The main goal of the reactive controller is aimed at providing the robot with the ability to avoid obstacles in its environment. The tests are performed on a benchmark maze problem, in which the goal is not necessarily to leave the maze, but rather that the robot avoids obstacles, in this case the walls, and penalizing for unwanted trajectories, such as cycles. The tracking controller's goal is for the robot to keep into to a certain path, this in order that the robot can learn to react to unknown environments. The optimization algorithm that was used is based on an abstraction of chemical reactions. To perform the simulation we use the "SimRobot" toolbox, the results of the tests are presented in a detailed fashion, and at the end we are presenting a comparison of results among the CRA, PSO and GA methods.

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“…Astudillo et al [4][5][6][7], defined the elementary terminology for characterizing and classifying artificial chemicals. Because the laws of reaction and rep-…”

Section: The Cra Paradigmmentioning

confidence: 99%

Section: Double-substitution Reactionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Chemical Reaction Algorithm for Type-2 Fuzzy Control Optimization in Mobile Robots

O¹,

Castillo²,

Soria³

2018

JAMRIS

Self Cite

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show abstract

“…Experimental results include simulations of feedback control systems for nonlinear plants using type-1 and type-2 fuzzy logic controllers (FLCs); a comparative analysis of the systems' response is performed, with and without the presence of uncertainty [28,29]. The main contribution of the chapter is the proposed approach for the design of type-2 FLCs using bio-inspired optimization algorithms [1,8].…”

Section: Introductionmentioning

confidence: 99%

“…Evolutionary algorithms can be a useful tool because of its capabilities of solving nonlinear problems, well-constrained or even nondeterministic polynomial-time hard (NP-hard) problems. However, recently, there have also been proposals of new optimization techniques based on biological or natural phenomena that have achieved good results in real-world problems, for example, ACO, the bat algorithm, firefly algorithm, chemical optimization, and others [1].…”

Section: Introductionmentioning

confidence: 99%

Bio-Inspired Optimization of Interval Type-2 Fuzzy Controller Design

Castillo

Melín

2014

Frontiers of Higher Order Fuzzy Sets

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This chapter presents a general framework for designing interval type-2 fuzzy controllers based on bio-inspired optimization techniques. The problem of designing optimal type-2 fuzzy controllers for complex nonlinear plants under uncertain environments is of crucial importance in achieving good results for realworld applications. Traditional approaches have been using genetic algorithms or trial and error approaches; however, results tend to be not optimal or require very large design times. More recently, bio-inspired optimization techniques, like ant colony optimization or particle swarm intelligence, have also been applied on optimal design of fuzzy controllers. In this chapter, we show how bio-inspired optimization techniques can be used to obtain results that outperform traditional approaches in the design of optimal type-2 fuzzy controllers.

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Fuzzy Chemical Reaction Algorithm with Dynamic Adaptation of Parameters

Castillo

Astudillo

et al. 2017

Fuzzy Logic in Intelligent System Design

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A New Optimization Method Based on a Paradigm Inspired by Nature

Cited by 9 publications

References 9 publications

Chemical Reaction Algorithm for Type-2 Fuzzy Control Optimization in Mobile Robots

Chemical Reaction Algorithm for Type-2 Fuzzy Control Optimization in Mobile Robots

Bio-Inspired Optimization of Interval Type-2 Fuzzy Controller Design

Fuzzy Chemical Reaction Algorithm with Dynamic Adaptation of Parameters

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