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 83

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Hybrid Nature-Inspired Optimization Algorithm: Hydrozoan and Sea Turtle Foraging Algorithms for Solving Continuous Optimization Problems
(2020-01-01)
Tansui, Daranat
;
Thammano, Arit
In this paper, we develop a hybrid optimization algorithm inspired by the reproduction processes of hydrozoans and the foraging behavior of sea turtles for solving continuous optimization problems. Our hybrid algorithm combines the exploration capability of the hydrozoan algorithm with the exploitation capability of the sea turtle foraging algorithm. Moreover, a new adaptive crossover operator was introduced and integrated into the hybrid algorithm to further enhance exploration capability. Our hybrid algorithm was evaluated and compared to the individual algorithms and 12 state-of-the-art algorithms. Results on 21 standard benchmark functions showed that our algorithm was very effective and was among the best of the group, specifically it converged faster than the individual algorithms on most functions and reached optimal or near-optimal results on all functions.
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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.
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A modified genetic algorithm with fuzzy roulette wheel selection for job-shop scheduling problems
(2015-05-19)
Thammano, Arit
;
Teekeng, Wannaporn
The job-shop scheduling problem is one of the most difficult production planning problems. Since it is in the NP-hard class, a recent trend in solving the job-shop scheduling problem is shifting towards the use of heuristic and metaheuristic algorithms. This paper proposes a novel metaheuristic algorithm, which is a modification of the genetic algorithm. This proposed algorithm introduces two new concepts to the standard genetic algorithm: (1) fuzzy roulette wheel selection and (2) the mutation operation with tabu list. The proposed algorithm has been evaluated and compared with several state-of-the-art algorithms in the literature. The experimental results on 53 JSSPs show that the proposed algorithm is very effective in solving the combinatorial optimization problems. It outperforms all state-of-the-art algorithms on all benchmark problems in terms of the ability to achieve the optimal solution and the computational time.
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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.
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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.
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Firefly Mating Algorithm for Continuous Optimization Problems
(2017-01-01)
Ritthipakdee, Amarita
;
Thammano, Arit
;
Premasathian, Nol
;
Jitkongchuen, Duangjai
This paper proposes a swarm intelligence algorithm, called firefly mating algorithm (FMA), for solving continuous optimization problems. FMA uses genetic algorithm as the core of the algorithm. The main feature of the algorithm is a novel mating pair selection method which is inspired by the following 2 mating behaviors of fireflies in nature: (i) the mutual attraction between males and females causes them to mate and (ii) fireflies of both sexes are of the multiple-mating type, mating with multiple opposite sex partners. A female continues mating until her spermatheca becomes full, and, in the same vein, a male can provide sperms for several females until his sperm reservoir is depleted. This new feature enhances the global convergence capability of the algorithm. The performance of FMA was tested with 20 benchmark functions (sixteen 30-dimensional functions and four 2-dimensional ones) against FA, ALC-PSO, COA, MCPSO, LWGSODE, MPSODDS, DFOA, SHPSOS, LSA, MPDPGA, DE, and GABC algorithms. The experimental results showed that the success rates of our proposed algorithm with these functions were higher than those of other algorithms and the proposed algorithm also required fewer numbers of iterations to reach the global optima.
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Human face recognition using modified Hausdorff ARTMAP
(2005-01-01)
Thammano, Arit
;
Ruensuk, Songpol
This paper proposes a new neural network approach specifically designed for solving two dimensional binary image recognition problems. The proposed neural network is an extension of the Hausdorff ARTMAP introduced by Thammano and Rungruang [1]. The objectives of this research are to improve the accuracy and correct the drawbacks of the original network. The performance of this proposed model has been compared with that of the original Hausdorff ARTMAP. The experimental results on two benchmark databases, the ORL and Yale face databases, show that the proposed network surpasses the original Hausdorff ARTMAP in both performance and processing time. © Springer-Verlag Berlin Heidelberg 2005.
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A Self-adaptive Differential Evolution Algorithm for Solving Optimization Problems
(2022-01-01)
Farda, Irfan
;
Thammano, Arit
This research proposes a novel self-adaptive differential evolution algorithm for solving continuous optimization problems. This paper focuses on redesiging the self-adaptive strategy for the mutation parameters. The new mutation parameters adjust themselves to the current situation of the algorithm. When the search is stagnant, the first mutation parameter that scales the difference between the best vector and the target vector will be increased. In contrast, the second mutation parameter that scales the difference between two random target vectors will be decreased. On the other hand, when the search progresses well towards the global optimum, the algorithm will enhance the search of the surrounding space by doing the opposite of the above actions. The performance of the proposed self-adaptive differential evolution algorithm was evaluated and compared with the classic differential evolution algorithm on 7 benchmark functions. The experimental results showed that the proposed algorithm converged much faster than the classic differential evolution algorithm on all benchmark functions.
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Primate swarm algorithm for continuous optimization problems
(2017-08-29)
Ritthipakdee, Amarita
;
Thammano, Arit
In primate life, there are a number of various social behavior, such as communication among members in a group, and food sharing, which are vital to maintain their survival. Similar to those of Swarm Intelligence, such as ant colony optimization, the behavior of primates motivates us to develop an algorithm with the aim of solving continuous problems. Our algorithm is inspired by the behavior of the primate. The communication among them is studied and is also a key part in their food finding strategy. Our proposed algorithm developed upon the behavior is tested with twelve standard benchmark functions and most of which converged to the optimal value.
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English-Thai example-based machine translation using n-gram model
(2006-01-01)
Kritsuthikul, Nattapol
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Thammano, Arit
;
Supnithi, Thepchai
The necessity on exchanging information among countries become a major task in information based society. Machine translation is an application that enables users to communicate each other without language barrier problem. With the great support on computer's efficiency, corpus-based technology becomes a fundamental concept for developing software based on a large amount of data. We introduce the first example-based English to Thai machine translation using n-gram model and implemented the system. Some advantages and disadvantages of this method are discussed. © 2006 IEEE.

Thammano, Arit

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