In the structure of ANFIS, there are two different parameter groups: premise and consequence. Training ANFIS means determination of these parameters using an optimization algorithm. In the first ANFIS model developed by Jang, a hybrid learning approach was proposed for training. In this approach, while premise parameters are determined by using gradient descent (GD), consequence parameters are found out with least squares estimation (LSE) method. Since ANFIS has been developed, it is used in modelling and identification of numerous systems and successful results have been achieved. The selection of optimization method utilized in training is very important to get effective results with ANFIS. It is seen that derivate based (GD, LSE etc.) and non-derivative based (heuristic algorithms such us GA, PSO, ABC etc.) algorithms are used in ANFIS training. Nevertheless, it has been observed that there is a trend toward heuristic based ANFIS training algorithms for better performance recently. At the same time, it seems to be proposed in derivative and heuristic based hybrid algorithms. Within the scope of this study, the heuristic and hybrid approaches utilized in ANFIS training are examined in order to guide researchers in their study. In addition, the final status in ANFIS training is evaluated and it is aimed to shed light on further studies related to ANFIS training.

Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey

The learning of image representation is always the most important problem in computer vision community. In this paper, we propose a novel image representation method by learning and using kernel classifiers. We firstly train classifiers using the one-against-all rule, then use them classify the candidate images, and finally using the classification responses as the new representations. The Euclidean distance between the classification response vectors are used as the new similarity measure. The experimental results from a large scale image database show that the proposed algorithm can outperform the original feature on image retrieval problem.

An Effective Image Representation Method Using Kernel Classification

Feature selection, which is a type of optimization problem, is generally achieved by combining an optimization algorithm with a classifier. Genetic algorithms and particle swarm optimization (PSO) are two commonly used optimal algorithms. Recently, cat swarm optimization (CSO) has been proposed and demonstrated to outperform PSO. However, CSO is limited by long computation times. In this paper, we modify CSO to present an improved algorithm, ICSO. We then apply the ICSO algorithm to select features in a text classification experiment for big data. Results show that the proposed ICSO outperforms traditional CSO. For big data classification, the results show that using term frequency-inverse document frequency (TF-IDF) with ICSO for feature selection is more accurate than using TF-IDF alone.

Feature selection based on an improved cat swarm optimization algorithm for big data classification

Sentiment analysis is crucial in various systems such as opinion mining and predicting. Considerable research has been done to analyze sentiment using various machine learning techniques. However, the high error rates in these studies can reduce the entire system’s efficiency. We introduce a novel big data and machine learning technique for evaluating sentiment analysis processes to overcome this problem. The data are collected from a huge volume of datasets, helpful in the effective analysis of systems. The noise in the data is eliminated using a preprocessing data mining concept. From the cleaned sentiment data, effective features are selected using a greedy approach that selects optimal features processed by an optimal classifier called cat swarm optimization-based long short-term memory neural network (CSO-LSTMNN). The classifiers analyze sentiment-related features according to cat behavior, minimizing error rate while examining features. This technique helps improve system efficiency, analyzed using experimental results of error rate, precision, recall, and accuracy. The results obtained by implementing the greedy feature and CSO-LSTMNN algorithm and the particle swarm optimization (PSO) algorithm are compared; CSO-LSTMNN outperforms PSO in terms of increasing accuracy and decreasing error rate.

A big data approach to sentiment analysis using greedy feature selection with cat swarm optimization-based long short-term memory neural networks

This paper presents an in-depth survey and performance evaluation of cat swarm optimization (CSO) algorithm. CSO is a robust and powerful metaheuristic swarm-based optimization approach that has received very positive feedback since its emergence. It has been tackling many optimization problems, and many variants of it have been introduced. However, the literature lacks a detailed survey or a performance evaluation in this regard. Therefore, this paper is an attempt to review all these works, including its developments and applications, and group them accordingly. In addition, CSO is tested on 23 classical benchmark functions and 10 modern benchmark functions (CEC 2019). The results are then compared against three novel and powerful optimization algorithms, namely, dragonfly algorithm (DA), butterfly optimization algorithm (BOA), and fitness dependent optimizer (FDO). These algorithms are then ranked according to Friedman test, and the results show that CSO ranks first on the whole. Finally, statistical approaches are employed to further confirm the outperformance of CSO algorithm.

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Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation.

Cat Swarm Optimization (CSO) is one of the new swarm intelligence algorithms for finding the best global solution. Because of complexity, sometimes the pure CSO takes a long time to converge and cannot achieve the accurate solution. For solving this problem and improving the convergence accuracy level, we propose a new improved CSO namely 'Adaptive Dynamic Cat Swarm Optimization'. First, we add a new adaptive inertia weight to velocity equation and then use an adaptive acceleration coefficient. Second, by using the information of two previous/next dimensions and applying a new factor, we reach to a new position update equation composing the average of position and velocity information. Experimental results for six test functions show that in comparison with the pure CSO, the proposed CSO can takes a less time to converge and can find the best solution in less iteration.

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A Novel Cat Swarm Optimization Algorithm for Unconstrained Optimization Problems

This paper introduces a novel approach for tuning the parameters of the adaptive network-based fuzzy inference system (ANFIS). In the commonly used training methods, the antecedent and consequent parameters of ANFIS are trained by gradient-based algorithms and recursive least square method, respectively. In this study, a new swarm-based meta-heuristic optimization algorithm, so-called "Cat Swarm Optimization", is used in order to train the antecedent part parameters and gradient descent algorithm is applied for training the consequent part parameters. Experimental results for prediction of Mackey–Glass model and identification of two nonlinear dynamic systems reveal that the performance of proposed algorithm is much better and it shows quite satisfactory results.

Meysam Orouskhani

Papers

A Novel Cat Swarm Optimization Algorithm for Unconstrained Optimization Problems

A hybrid method of modified cat swarm optimization and gradient descent algorithm for training anfis