Fuzzy-PID controller is proposed for AGC of multi-area power system.TLBO algorithm is applied to optimize the parameters of fuzzy-PID controller.The superiority of proposed approach over LCOA, GA, PS and SA based PID controller is shown.Robustness analysis is performed under wide changes in system parameters and disturbance. This paper deals with the design of a novel fuzzy proportional-integral-derivative (PID) controller for automatic generation control (AGC) of a two unequal area interconnected thermal system. For the first time teaching-learning based optimization (TLBO) algorithm is applied in this area to obtain the parameters of the proposed fuzzy-PID controller. The design problem is formulated as an optimization problem and TLBO is employed to optimize the parameters of the fuzzy-PID controller. The superiority of proposed approach is demonstrated by comparing the results with some of the recently published approaches such as Lozi map based chaotic optimization algorithm (LCOA), genetic algorithm (GA), pattern search (PS) and simulated algorithm (SA) based PID controller for the same system under study employing the same objective function. It is observed that TLBO optimized fuzzy-PID controller gives better dynamic performance in terms of settling time, overshoot and undershoot in frequency and tie-line power deviation as compared to LCOA, GA, PS and SA based PID controllers. Further, robustness of the system is studied by varying all the system parameters from -50% to +50% in step of 25%. Analysis also reveals that TLBO optimized fuzzy-PID controller gains are quite robust and need not be reset for wide variation in system parameters.

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Teaching-learning based optimization algorithm based fuzzy-PID controller for automatic generation control of multi-area power system

A Fuzzy Hierarchical Operator in the Grey Wolf Optimizer Algorithm

A PSO-adaptive KNN based gene selection method is proposed to select useful genes.A heuristic for selecting the optimal values of K efficiently is also proposed.The proposed technique is applied on SRBCT, ALL_AML and MLL microarray datasets.The usefulness of the identified genes is reconfirmed using SVM classifier.The method finds 6, 3 and 4 genes for SRBCT, ALL_AML, and MLL with high accuracy. These days, microarray gene expression data are playing an essential role in cancer classifications. However, due to the availability of small number of effective samples compared to the large number of genes in microarray data, many computational methods have failed to identify a small subset of important genes. Therefore, it is a challenging task to identify small number of disease-specific significant genes related for precise diagnosis of cancer sub classes. In this paper, particle swarm optimization (PSO) method along with adaptive K-nearest neighborhood (KNN) based gene selection technique are proposed to distinguish a small subset of useful genes that are sufficient for the desired classification purpose. A proper value of K would help to form the appropriate numbers of neighborhood to be explored and hence to classify the dataset accurately. Thus, a heuristic for selecting the optimal values of K efficiently, guided by the classification accuracy is also proposed. This proposed technique of finding minimum possible meaningful set of genes is applied on three benchmark microarray datasets, namely the small round blue cell tumor (SRBCT) data, the acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) data and the mixed-lineage leukemia (MLL) data. Results demonstrate the usefulness of the proposed method in terms of classification accuracy on blind test samples, number of informative genes and computing time. Further, the usefulness and universal characteristics of the identified genes are reconfirmed by using different classifiers, such as support vector machine (SVM).

Gene selection from microarray gene expression data for classification of cancer subgroups employing PSO and adaptive K-nearest neighborhood technique

Display Omitted Dynamic parameter adaptation approach ACO based on interval type-2 fuzzy systems.Apply this method to a variety of problems without finding the best parameters.Fuzzy logic controls the diversity of the solutions. A dynamic parameter adaptation methodology for Ant Colony Optimization (ACO) based on interval type-2 fuzzy systems is presented in this paper. The idea is to be able to apply this new ACO method with parameter adaptation to a wide variety of problems without the need of finding the best parameters for each particular problem. We developed several fuzzy systems for parameter adaptation and a comparison was made among them to decide on the best design. The use of fuzzy logic is to control the diversity of the solutions, and in this way controlling the exploration and exploitation abilities of ACO. The travelling salesman problem (TSP) and the design of a fuzzy controller for an autonomous mobile robot are the benchmark problems used to test the proposed methodology.

Ant colony optimization with dynamic parameter adaptation based on interval type-2 fuzzy logic systems

Optimization of type-2 fuzzy inference systems using GAs and PSO are presented.Optimized type-2 fuzzy systems are used to estimate the type-2 fuzzy weights.Simulation results and a comparative study are presented to illustrate the method.Bio-inspired optimization of the type-2 fuzzy systems is viable for this problem. In this paper the optimization of type-2 fuzzy inference systems using genetic algorithms (GAs) and particle swarm optimization (PSO) is presented. The optimized type-2 fuzzy inference systems are used to estimate the type-2 fuzzy weights of backpropagation neural networks. Simulation results and a comparative study among neural networks with type-2 fuzzy weights without optimization of the type-2 fuzzy inference systems, neural networks with optimized type-2 fuzzy weights using genetic algorithms, and neural networks with optimized type-2 fuzzy weights using particle swarm optimization are presented to illustrate the advantages of the bio-inspired methods. The comparative study is based on a benchmark case of prediction, which is the Mackey-Glass time series (for ?=17) problem.

Optimization of type-2 fuzzy weights in backpropagation learning for neural networks using GAs and PSO

In this paper a new method for dynamic parameter adaptation in particle swarm optimization (PSO) is proposed. PSO is a metaheuristic inspired in social behaviors, which is very useful in optimization problems. In this paper we propose an improvement to the convergence and diversity of the swarm in PSO using fuzzy logic. Simulation results show that the proposed approach improves the performance of PSO. First, benchmark mathematical functions are used to illustrate the feasibility of the proposed approach. Then a set of classification problems are used to show the potential applicability of the fuzzy parameter adaptation of PSO.

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

Optimization problems are present in many fields of science, engineering, and industry. In most cases, optimization problems involve a large number of complex decision variables. Often, the application of fuzzy systems to traditional control strategies requires the use of optimization techniques to define the parameters of the membership functions capturing the designer’s experience. Currently, bioinspired algorithms are one of the most widely used methods for fuzzy system optimization. In this paper, we review the literature on the application of different bioinspired techniques such as Genetic Algorithms, Ant Colony Optimization, Particle Swarm Optimization, Galaxy Swarm Optimization, Harmony Search, Firefly, Differential Evolution, to optimize the parameters of the fuzzy controllers. We found particular trends in the current literature, with many methods using dynamic parameter adaptation, hybrids, distributed algorithms, and type-2 fuzzy logic.

Optimization of Fuzzy Logic Controllers with Distributed Bio-Inspired Algorithms

Google Adwords has a bidding price optimization scheme for its campaigns, where the user proposes the maximum bidding price, and then Adwords adapts the final bidding price according to the performance of a particular campaign. This paper proposes a bidding price controller based on a fuzzy system. Specifically, two approaches are considered: a type-1 fuzzy inference system, and an interval type-2 fuzzy inference system. The results show that the proposed methods are superior to the Adwords optimization method, and that there isn't enough statistical evidence to support the superiority of the interval type-2 fuzzy inference system against the type-1 fuzzy inference system, although type-2 is slightly better.

Bidding Strategies Based on Type-1 and Interval Type-2 Fuzzy Systems for Google AdWords Advertising Campaigns.

Research in educational adaptive hypermedia systems has been concerned with the generation of personalized courses, in this work we focus on a task of these kind of systems: the semi-automatic sequencing of didactic resources. Sequencing defines the order in which topics (and didactic resources) in a course will be presented to learners, considering for this, their previous knowledge and particular objectives. This task is based on subjective information, for example the learner knowledge, preferences, learning style, and even assessment results are perceived differently depending the context. In this paper we define an architecture for sequencing of didactic materials using fuzzy attributes and rules.

Mario García Valdez

Papers

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

Optimization of Fuzzy Logic Controllers with Distributed Bio-Inspired Algorithms

Bidding Strategies Based on Type-1 and Interval Type-2 Fuzzy Systems for Google AdWords Advertising Campaigns.

A Fuzzy Approach for the Sequencing of Didactic Resources in Educational Adaptive Hypermedia Systems