R. J. Kuo

Automatic kernel clustering with bee colony optimization algorithm

Cluster analysis is important in data mining, especially if there is unsupervised data. Recently, many clustering methods have been proposed. Unfortunately, most of these require the definition of the number of clusters, in advance. This study addresses this weakness by proposing a new automatic clustering algorithm: automatic kernel clustering with bee colony optimization (AKC-BCO). AKC-BCO determines the appropriate number of clusters and assigns data points to correct clusters. This is accomplished by the kernel function, which increases clustering capability. This method is validated using several benchmark data sets. The result is compared with several existing automatic clustering methods. The experiment results demonstrate that the proposed AKC-BCO is more stable and accurate than others. Furthermore, the proposed method is also applied to a real-world medical problem.

The gradient evolution algorithm

This study presents a new metaheuristic method that is derived from the gradient-based search method. In an exact optimization method, the gradient is used to find extreme points, as well as the optimal point. This study modifies a gradient method, and creates a metaheuristic method that uses a gradient theorem as its basic updating rule. This new method, named gradient evolution, explores the search space using a set of vectors and includes three major operators: vector updating, jumping and refreshing. Vector updating is the main updating rule in gradient evolution. The search direction is determined using the Newton-Raphson method. Vector jumping and refreshing enable this method to avoid local optima. In order to evaluate the performance of the gradient evolution method, three different experiments are conducted, using fifteen test functions. The first experiment determines the influence of parameter settings on the result. It also determines the best parameter setting. There follows a comparison between the basic and improved metaheuristic methods. The experimental results show that gradient evolution performs better than, or as well as, other methods, such as particle swarm optimization, differential evolution, an artificial bee colony and continuous genetic algorithm, for most of the benchmark problems tested.

Application of an artificial immune system-based fuzzy neural network to a RFID-based positioning system

Due to the rapid development of globalization, which makes supply chain management more complicated, more companies are applying radio frequency identification (RFID), in warehouse management. The obvious advantages of RFID are its ability to scan at high-speed, its penetration and memory. In addition to recycling, use of a RFID system can also reduce business costs, by indentifying the position of goods and picking carts. This study proposes an artificial immune system (AIS)-based fuzzy neural network (FNN), to learn the relationship between the RFID signals and the picking carts position. Since the proposed network has the merits of both AIS and FNN, it is able to avoid falling into the local optimum and possesses a learning capability. The results of the evaluation of the model show that the proposed AIS-based FNN really can predict the picking cart position more precisely than conventional FNN and, unlike an artificial neural network, it is much easier to interpret the training results, since they are in the form of fuzzy IF-THEN rules.

Integration of artificial immune network and K-means for cluster analysis

This study is dedicated to propose a cluster analysis algorithm which is integration of artificial immune network (aiNet) and K-means algorithm (aiNetK). Four benchmark data sets, Iris, Wine, Glass, and Breast Cancer, are employed to testify the proposed algorithm. The computational results reveal that aiNetK is superior to particle swam optimization and artificial immune system-related methods.

Hybrid of artificial immune system and particle swarm optimization-based support vector machine for Radio Frequency Identification-based positioning system

This study intends to propose a hybrid of artificial immune system (AIS) and particle swarm optimization (PSO)-based support vector machine (SVM) (HIP-SVM) for optimizing SVM parameters, and applied it to radio frequency identification (RFID)-based positioning system. In order to evaluate HIP-SVMs capability, six benchmark data sets, Australian, Heart disease, Iris, Ionosphere, Sonar and Vowel, were employed. The computational results showed that HIP-SVM has better performance than AIS-based SVM and PSO-based SVM. HIP-SVM was also applied to classify RSSI for indoor positioning. The experiment results indicated that HIP-SVM can achieve highest accuracy compared to those of AIS-SVM and PSO-SVM. It demonstrated that RFID can be used for storing information and in indoor positioning without additional cost.

Hybrid ant colony optimization algorithms for mixed discrete–continuous optimization problems

Abstract This paper presents three new hybrid ant colony optimization algorithms that are extended from the ACO R developed by Socha and Dorigo for solving mixed discrete–continuous constrained optimization problems. The first two hybrids, labeled ACO R -HJ and ACO R -DE, differs in philosophy with the former integrating ACO R with the effective Hooke and Jeeves local search method and the latter a cooperative hybrid between ACO R and differentia evolution. The third hybrid, labeled ACO R -DE-HJ, is the second cooperative hybrid enhanced with the Hooke and Jeeves local search. All three algorithms incorporate a method to handle mixed discrete–continuous variables and the Deb’s parameterless penalty method for handling constraints. Fourteen problems selected from various domains were used for testing the performance of both algorithms. It was showed that all three algorithms greatly outperform the original ACO R in finding the exact or near global optima. An investigation was also carried out to determine the relative performance of applying local search with a fixed probability or varying probability.

An application of a metaheuristic algorithm-based clustering ensemble method to APP customer segmentation

This study proposes a metaheuristic-based clustering ensemble method. It integrates the clustering ensembles algorithm with the metaheuristic-based clustering algorithm. In the clustering ensembles, this study performs an improved generation mechanism and a co-association matrix in the co-occurrence approach. In order to improve the efficiency, a principle component analysis is employed. Furthermore, three metaheuristic-based clustering algorithms are proposed. This paper uses a real-coded genetic algorithm, a particle swarm optimization and an artificial bee colony optimization to combine with clustering ensembles algorithm. The experimental results indicate that the proposed metaheuristic-based clustering ensembles algorithms have better performance than metaheuristic-based clustering without clustering ensembles method. Furthermore, the proposed algorithms are applied to solve a customer segmentation problem. The real problem is come from a mobile application. Among all of the proposed algorithms, the artificial bee colony optimization-based clustering ensembles algorithm outperforms other algorithms. Therefore, the marketing strategy for the real application is made based on the best result.

The integration of association rule mining and artificial immune network for supplier selection and order quantity allocation

This study firstly uses one of the association rule mining techniques, a TD-FP-growth algorithm, to select the important suppliers from the existing suppliers and determine the importance of each supplier. A hybrid artificial immune network (Opt-aiNet) and particle swarm optimization (PSO) (aiNet-PSO) is then proposed to allocate the order quantity for the key suppliers at minimum cost. In order to verify the proposed method, a case company’s daily purchasing ledger is used, with emphasis on the consumer electronic product manufacturers. The computational results indicate that the TD-FP-growth algorithm can select the key suppliers using the historical data. The proposed hybrid method also provides a cheaper solution than a genetic algorithm, particle swam optimization, or an artificial immune system.

Solving bi-level linear programming problem through hybrid of immune genetic algorithm and particle swarm optimization algorithm

Bi-level linear programming, consisting of upper level and lower level objectives, is a technique for modeling decentralized decision. This study presents a hybrid of immune genetic algorithm and vector-controlled particle swarm optimization (IGVPSO) to solve the bi-level linear programming problem (BLPP). It is applied to a supply chain model that is a BLPP. Using four problems from the literature and the supply chain distribution models, the computational results indicate that the proposed method is superior to some algorithms.

Solving CVRP with time window, fuzzy travel time and demand via a hybrid ant colony optimization and genetic algortihm

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.