This study introduces a new clustering method based on the multivariate fuzzy K-modes algorithm. The proposed algorithm incorporates attribute weights determined by Gini impurity, which evaluates the significance of attribute values in both within-cluster and between-cluster variances. Additionally, instead of relying on the Hamming distance, the probabilistic distance is employed to compute the dissimilarity between objects or between objects and their corresponding centroids. This study also utilizes a particle swarm optimization (PSO) algorithm to find the optimal centroids, replacing the random generation of initial centroids and ensuring the global optimization. Thus, the proposed algorithm is named the PSO-based multivariate fuzzy weighted fuzzy K-modes algorithm with probabilistic distance (PSO-MFWFKM-PD). The proposed algorithm is evaluated against other benchmark algorithms in terms of accuracy (AC) and Davies-Bouldin Index (DBI) using five benchmark datasets. The result demonstrate that PSO-MFWFKM-PD outperforms the other algorithms. Furthermore, the algorithm is applied to a real-world case study for market segmentation, utilizing a soft-drinks consumer dataset from Thailand collected through an online questionnaire. The results from this application are also promising.

Tuberculosis is one of the top ten causes of death worldwide. Although this disease is curable and preventable, yet many new tuberculosis cases still occur especially in developing countries. Many low-income families cannot afford the medical diagnosis for tuberculosis. Therefore, this paper proposes an initial screening for tuberculosis infection using a data mining approach. In this paper, the initial screening is conducted using classification method developed from non-linear support vector machine and gradient evolution algorithm. Herein, the gradient evolution algorithm is able to find the best parameter setting for the support vector machine algorithm. The classification is performed based on some information which can be easily collected without medical test. The proposed algorithm is also compared with some other metaheuristic-based support vector machine algorithms. The experimental results show that the proposed algorithm has promising results shown by the small error rate. In addition, a C5.0 decision tree is employed to further analyze the rules of TB infection. The result reveals that people who are protected with vaccine still possible to be infected by tuberculosis, especially if there is direct contact with an active tuberculosis patient. Furthermore, people with low body mass index and low education has higher risk to get tuberculosis infection. The result of this study could help people conduct self-diagnosis for the tuberculosis infection before deciding to do a medical test.

Forecasting is one of the crucial factors in applications because it ensures the effective allocation of capacity and proper amount of inventory. Because Box–Jenkins models using linear forecasting have their constraint to predict complexity in the real world, other nonlinear approaches are developed to conquer the challenge of nonlinear forecasting. With the same goal, we are proposing a hybrid of genetic algorithm and artificial immune system (HGAI) algorithm with radial basis function neural network learning for function approximation and further applying it to conduct an industrial personal computer sales forecasting exercise. In addition, five well‐known benchmark problems were used to evaluate the results in the experiment, and the newly proposed HGAI algorithm has returned better results than the Box–Jenkins models and other algorithms.

Cluster analysis is a very useful data analysis tool. It can reveal the hidden information stored inside a dataset. Therefore, many researches proposed different clustering algorithms. This paper intends to propose a gradient evolutionbased Ä-means algorithm. Ä-means algorithm is a well-known clustering algorithm. It offers a simple algorithm to divide the dataset into several clusters. Unfortunately, its results are highly influenced by the initial centroids. Unpromising initial centroid might lead the k-means to the bad clustering result. This paper aims to improve this drawback by adopting a new metaheuristic algorithm, named a gradient evolution (GE) algorithm. In this paper, we proposed a GE-based Ä-means algorithm for solving the clustering problems. The proposed algorithm is validated by using some benchmark datasets. The computation results showed that the proposed algorithm can obtain better results compared with some other metaheuristic-based k-means algorithms.

This study intends to propose a hybrid ant colony optimization (ACO) and genetic algorithm (GA) (HACOGA) for solving the capacitated vehicle routing problem (CVRP) with time window, fuzzy travel time and demand. A mathematical model for CVRP with time window, fuzzy travel time and demand is first constructed. It applies fuzzy credibility and ranking approaches. Then, the proposed HACOGA which combines ACO with GA to accelerate its exploration is employed. It also embeds local search algorithms to generate a better initial solution and improve its performance at the end of evolution. The proposed algorithm is verified using an instance of CVRP with time window and fuzzy travel time first. The simulation result indicates that the proposed HACOGA outperforms previous methods. Furthermore, a simulation example is employed to show the effectiveness of the proposed algorithm for solving CVRP with time window, fuzzy travel time and fuzzy demand. The computational results reveal that HACOGA still has the best performance.

This paper proposes a novel fuzzy neural network for clustering the parts into several families. The proposed network, which has fuzzy inputs as well as fuzzy weights, integrates the Kohonen's feature map neural network and the fuzzy set theory. The model evaluation results show that the proposed fuzzy neural network can provide more accurate decision compared to the fuzzy c-means algorithm and k-means algorithm.