Thammano, Arit

Preferred name

Thammano, Arit

Alternative Name

Thammano, A.

Thummano, Arit

Main Affiliation

School of Information Technology

arit.th@kmitl.ac.th

Now showing 1 - 10 of 56

Some of the metrics are blocked by your
consent settings
A combination of shuffled frog leaping and fuzzy logic for Flexible Job-Shop Scheduling Problems
(2011-01-01)
Teekeng, Wannaporn
;
Thammano, Arit
Flexible Job-Shop Scheduling Problem (FJSP) is a well known NP-hard combinatorial optimization problem. Over the last decade, many algorithms have been proposed to tackle FJSP. Evolutionary algorithms, which solve problems by mimicking the process of natural evolution, are the most widely used techniques in solving FJSP. This paper proposes a novel evolutionary algorithm that integrates the concept of a fuzzy logic into Shuffled Frog Leaping Algorithm. In the proposed SFLA-FS model, the fuzzy roulette wheel selection is used in selecting frogs to form a sub-memeplex. This selection method is proved to be better than the typically used rank selection. The objective of this research is to find a schedule for each of the 10 benchmark problems that minimize their makespan. The experimental results obtained from SFLA-FS show that the SFLA-FS is very efficient for all tested problems. © 2011 Published by Elsevier Ltd.
Some of the metrics are blocked by your
consent settings
Nature-inspired optimization method: Hydrozoan algorithm for solving continuous problems
(2017-08-29)
Tansui, Daranat
;
Thammano, Arit
In this article, a new optimization algorithm that is inspired by the biology of hydrozoa (HA) is proposed. Our aim was to develop an algorithm that is based on the regeneration and transplantation processes of hydrozoa for finding the best solutions for continuous optimization problems. Basically, HA follows the same general processes of evolutionary algorithm; however, its distinctive processes mimic the life cycle of 3 basic forms of hydrozoa: motile planula, polyps, and medusa. In particular, the growth of strong buds from the polyp stage depends on levels of morphogens: activators and inhibitors. These 3 forms develop or evolve into the best solution. HA was performance tested with 20 standard benchmark functions and compared with genetic algorithm and Particle Swarm Optimization (PSO). The test results have confirmed that the proposed algorithm is computationally more efficient than both GA and PSO. It works very well on most benchmark functions.
Some of the metrics are blocked by your
consent settings
Modified Ant System with Threshold for the Vehicle Routing Problem
(2022-01-01)
Rungwachira, Petcharat
;
Thammano, Arit
Vehicle routing problem is a combinatorial optimization problem. Ant system, one of the most popular algorithms for solving the combinatorial optimization problem, is inspired by the ant foraging behavior. However, Ant system has the problem of premature convergence and easily getting trapped into local optimum. This paper proposes the modified ant system with threshold. In the beginning, the proposed algorithm randomly generates the initial population. To obtain a more diverse population, the transition probability and the threshold are used to determine which city should be next in the path. Moreover, the improved pheromone updating process is introduced to help reducing the rate of convergence. Three local searches, swap, insert, and reverse, are used in the proposed algorithm to prevent getting trapped in a local optimum. The datasets used in this research were taken from TSPLIB, the standard benchmark datasets for TSP. The performance of the algorithm was compared with that of the other 3 algorithms: GA-PSO-ACO, FOGS-ACO, and Hybrid VNS. The results on 20 datasets showed that the proposed method outperformed or at least at par with the others on 15 datasets. On the other hand, there was one dataset, rd100, on which the proposed algorithm was better than the best-known solution. According to the result, our modified ant system with threshold was very effective for the small- and medium-sized vehicle routing datasets.
Some of the metrics are blocked by your
consent settings
Dynamic system identification using recurrent neural network with multi-valued connection weight
(2009-01-01)
Thammano, Arit
;
Ruxpakawong, Phongthep
This paper introduces a new concept of the connection weight to the standard recurrent neural networks - Elman and Jordan networks. The architecture of the modified networks is the same as that of the original recurrent neural networks. However, in the modified networks the weight of each connection is multi-valued, depending on the value of the input data involved. The backpropagation learning algorithm is also modified to suit the proposed concept. The modified networks have been benchmarked against their original counterparts. The results on eleven benchmark problems are very encouraging. ©2009 IEEE.
Some of the metrics are blocked by your
consent settings
Differential Evolution for Classification: A Novel Classifier Technique in Data Mining
(2024-01-01)
Farda, Irfan
;
Thammano, Arit
In the realm of Data Mining, the pursuit of innovative classification methodologies remains crucial for advancing robust techniques in handling complex and diverse datasets. This paper explores the application of Differential Evolution (DE), a powerful optimization algorithm, as a unique and effective optimization-based standalone classifier. Our exploration focused on harnessing DE's intrinsic capabilities, adapting it into a classifier while preserving its distinctiveness. The fundamental principle of DE for classification involves iteratively optimizing the center point for each class using DE operators, which are specialized mechanisms for exploring and refining solutions, and utilizing these optimized points for making predictions. The effectiveness of our model was evaluated on ten classification datasets from the UCI Machine Learning Repository and compared against three other classification methods: KNN, ZMP, and BPNN. Experimental results underscore the competitive performance of our proposed model, emphasizing the potential of DE in effectively addressing classification challenges.
Some of the metrics are blocked by your
consent settings
Applying a weighting matrix to the hierarchical neural network model for handwritten thai character recognition
(2006-01-01)
Thammano, Arit
;
Poolsamran, Patcharawadee
This paper proposes a new neural network approach to the off-line handwritten Thai character recognition. This new neural network is a hierarchical neural network; it employs the concept of a weighting matrix in measuring the similarity between the incoming input pattern and the reference patterns. The experiments have been conducted to recognize both slipshod and proper handwritten characters. The results demonstrate a very promising performance of the proposed approach. © 2006 IEEE.
Some of the metrics are blocked by your
consent settings
Printed thai character recognition using the hierarchical cross-correlation ARTMAP
(2005-01-01)
Thammano, Arit
;
Duangphasuk, Pruegsa
Traditionally, Thai characters are composed of circle, zigzag line, curve, and head. However, many new Thai fonts, which are now gaining in popularity, do not follow the traditional writing rule; the head has been omitted from the characters. Without the head, it is very difficult to segregate the characters. Even the best commercial Thai OCR software has difficulty in recognizing this kind of character. Therefore, the hierarchical cross-correlation ARTMAP is proposed in this paper to recognize the no-head Thai characters. © 2005 IEEE.
Some of the metrics are blocked by your
consent settings
Handwritten Thai character recognition using fuzzy membership function and fuzzy ARTMAP
(2003-01-01)
Pornchaikajornsak, A.
;
Thammano, A.
This paper proposes an approach to off-line handwritten Thai character recognition by using the concept of fuzzy membership function and fuzzy ARTMAP neural network. The concept of fuzzy membership function is employed in the feature extraction process, while the fuzzy ARTMAP is used as the recognition engine. The experiments have been conducted to recognize both isolated characters and written documents. The results demonstrate a very promising performance of the proposed approach.
Some of the metrics are blocked by your
consent settings
Solving classification problems using supervised self-organizing map
(2007-12-01)
Thammano, Arit
;
Kiatwuthiamorn, Jiraporn
This paper proposes the new approach to deal with the classification problems by modifying the well-known Kohonen self-organizing map in order to make it able to solve classification problems. During training, the fuzzy membership function is used in place of the Euclidean distance to find the best matching cluster for the input pattern. In order to improve the efficiency of proposed model, the fuzzy entropy concept is employed to reduce the number of nodes in the cluster layer. The performance of the proposed model was compared with the fuzzy ARTMAP neural network. The results on five benchmark problems are very encouraging. ©2007 IEEE.
Some of the metrics are blocked by your
consent settings
Solving Optimization Problems by a Hybrid Algorithm Based on Sand Cat Swarm Optimization and Invasive Weed Optimization Algorithm
(2025-01-01)
Pravesjit, Sakkayaphop
;
Kantawong, Krittika
;
Jitkongchuen, Duangjai
;
Thammano, Arit
;
Longpradit, Panchit
This paper addresses an optimization problem using hybrid algorithms of the Sand Cat Swarm Optimization (SCSO) and Invasive Weed Optimization (IWO). In this study, the reproduction step in the IWO algorithm was incorporated after the initial population step of the SCSO. The proposed algorithm was compared against the following: Intersection Mutation Differential Evolution (IMDE), Differential Evolution (DE), SCSO, and Whale Optimization Algorithm (WOA), whereby the performance was tested on six benchmark functions using a 10-fold cross validation. The results indicate that the proposed algorithm yielded the optimal solution for two out of the six benchmark functions. Additionally, when compared with the other four chosen algorithms, it yielded the best overall results. The findings suggest that the proposed algorithm is able to generate solutions similar to those obtained from the previous methods, essentially for the continuous step function, the multimodal function, and the discontinuous step function.

Thammano, Arit

Filters

Settings

Sort By

Results per page