Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic

Melín, Patricia; Olivas, Frumen; Castillo, Oscar; Valdez, Fevrier; Soria, José Manuel Nieto; Valdez, Mario García

doi:10.1016/j.eswa.2012.12.033

Cited by 248 publications

(95 citation statements)

References 12 publications

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“…For the decades of the algorithm, we did perform an analysis and decided to utilize a percentage of decades with respect to the total number of decades, that is, when the algorithm starts the decades will be considered to have a linguistic value of "low", and when most of the decades have passed will be considered "high" or close to 100%. This notion is presented below [27]:…”

Section: Proposal Methodologymentioning

confidence: 99%

Imperialist Competitive Algorithm with Dynamic Parameter Adaptation Using Fuzzy Logic Applied to the Optimization of Mathematical Functions

et al. 2017

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Abstract:In this paper we are presenting a method using fuzzy logic for dynamic parameter adaptation in the imperialist competitive algorithm, which is usually known by its acronym ICA. The ICA algorithm was initially studied in its original form to find out how it works and what parameters have more effect upon its results. Based on this study, several designs of fuzzy systems for dynamic adjustment of the ICA parameters are proposed. The experiments were performed on the basis of solving complex optimization problems, particularly applied to benchmark mathematical functions. A comparison of the original imperialist competitive algorithm and our proposed fuzzy imperialist competitive algorithm was performed. In addition, the fuzzy ICA was compared with another metaheuristic using a statistical test to measure the advantage of the proposed fuzzy approach for dynamic parameter adaptation.

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Section: Proposal Methodologymentioning

confidence: 99%

Imperialist Competitive Algorithm with Dynamic Parameter Adaptation Using Fuzzy Logic Applied to the Optimization of Mathematical Functions

et al. 2017

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“…It has two driving wheels that are fixed to the axis that passes through the center of mass "C" represented by {C, Xm, Ym}, and one passive wheel that prevents the robot from tipping over as it moves on a plane [26,27]. The dynamics of the mobile robot is represented by the following set of Equations (6) and (7), [5,29,36]. ( ) ( ) ( ) …”

Section: Problem To Be Optimizedmentioning

confidence: 99%

“…The case selected to apply the proposed algorithm with Type-2 fuzzy approach is the problem of the autonomous robot [1,5,8,29,36], the dynamics of this problem is that the robot is able to follow an assigned trajectory with a minimum error value. In the proposed method an FLS is used to move the parameters of the membership functions of the fuzzy controller.…”

Section: Problem To Be Optimizedmentioning

confidence: 99%

A New Meta-Heuristics of Optimization with Dynamic Adaptation of Parameters Using Type-2 Fuzzy Logic for Trajectory Control of a Mobile Robot

2017

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Fuzzy logic is a soft computing technique that has been very successful in recent years when it is used as a complement to improve meta-heuristic optimization. In this paper, we present a new variant of the bio-inspired optimization algorithm based on the self-defense mechanisms of plants in the nature. The optimization algorithm proposed in this work is based on the predator-prey model originally presented by Lotka and Volterra, where two populations interact with each other and the objective is to maintain a balance. The system of predator-prey equations use four variables (α, β, λ, δ) and the values of these variables are very important since they are in charge of maintaining a balance between the pair of equations. In this work, we propose the use of Type-2 fuzzy logic for the dynamic adaptation of the variables of the system. This time a fuzzy controller is in charge of finding the optimal values for the model variables, the use of this technique will allow the algorithm to have a higher performance and accuracy in the exploration of the values.

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“…Thus, three Fuzzy system were proposed; the first one is responsible for deciding which are the best results of the FPSO + FGA, and the two second ones are in charge of changing the values of the crossover the mutation , the social acceleration , and the cognitive acceleration . In [12] a Fuzzy Logic approach was proposed to dynamically adapt the cognitive and the social factors and to improve the convergence and diversity of the population in PSO algorithm. Three Fuzzy Systems were modelled for adapting the and parameters, with respect to three performance measures, which are the diversity of the swarm, the average error, and the iterations of the algorithm.…”

Section: The Effect Of Updating the Local Pheromone On Acs Performancmentioning

confidence: 99%

The Effect of Updating the Local Pheromone on ACS Performance using Fuzzy Logic

Afia

Bouzbita

Faizi

2017

IJECE

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Fuzzy Logic Controller (FLC) has become one of the most frequently utilised algorithms to adapt the metaheuristics parameters as an artificial intelligence technique. In this paper, the parameter of Ant Colony System (ACS) algorithm is adapted by the use of FLC, and its behaviour is studied during this adaptation. The proposed approach is compared with the standard ACS algorithm. Computational results are done based on a library of sample instances for the Traveling Salesman Problem (TSPLIB).

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Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic

Cited by 248 publications

References 12 publications

Imperialist Competitive Algorithm with Dynamic Parameter Adaptation Using Fuzzy Logic Applied to the Optimization of Mathematical Functions

Imperialist Competitive Algorithm with Dynamic Parameter Adaptation Using Fuzzy Logic Applied to the Optimization of Mathematical Functions

A New Meta-Heuristics of Optimization with Dynamic Adaptation of Parameters Using Type-2 Fuzzy Logic for Trajectory Control of a Mobile Robot

The Effect of Updating the Local Pheromone on ACS Performance using Fuzzy Logic

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