Thammano, Arit

Preferred name

Thammano, Arit

Alternative Name

Thammano, A.

Thummano, Arit

Main Affiliation

School of Information Technology

arit.th@kmitl.ac.th

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Printed Thai character recognition using the hybrid approach
(2002-01-01)
Thammano, Arit
;
Ruxpakawong, Phongthep
Many researchers have been conducted on the recognition of Thai characters. Different approaches, such as neural network, syntactic, and structural methods, have been proposed. However, the success in recognizing Thai characters is still limited, compared to English characters. This paper proposes an approach to recognize the printed Thai characters using the hybrid of global feature, local features, fuzzy membership function and the neural network. The global feature classifies all characters into seven main groups. Then the local features and the neural network are applied to identify the characters.
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A self-adaptive differential evolution algorithm for continuous optimization problems
(2014-09-01)
Jitkongchuen, Duangjai
;
Thammano, Arit
This paper proposes a new self-adaptive differential evolution algorithm (DE) for continuous optimization problems. The proposed self-adaptive differential evolution algorithm extends the concept of the DE/current-to-best/1 mutation strategy to allow the adaptation of the mutation parameters. The control parameters in the mutation operation are gradually self-adapted according to the feedback from the evolutionary search. Moreover, the proposed differential evolution algorithm also consists of a new local search based on the krill herd algorithm. In this study, the proposed algorithm has been evaluated and compared with the traditional DE algorithm and two other adaptive DE algorithms. The experimental results on 21 benchmark problems show that the proposed algorithm is very effective in solving complex optimization problems.
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Swarm optimization algorithm based on the ant colony life cycle
(2019-01-01)
Kiatwuthiamorn, Jiraporn
;
Thammano, Arit
Optimization is very important to the success of any business. One technique for solving optimization is swarm intelligence; it has been successfully applied to solve a wide range of optimization problems. We devised a new swarm intelligence optimization algorithm based on the cooperative behavior of three different kinds of ants in a colony. Our algorithm consists of both exploration and exploitation processes to achieve better search performance. A new local search, inspired by the foraging of desert ants, was introduced to help the search move away from the local optima. Performance was evaluated on 23 standard benchmark functions of varying complexity. Our algorithm was able to find the global optima in more than 80 percent of the test functions, whereas the second-place algorithm only found around 10 percent of the functions tested.
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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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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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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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SMBO: A self-organizing model of marriage in honey-bee optimization
(2012-04-01)
Thammano, Arit
;
Poolsamran, Patcharawadee
This paper proposes a novel swarm intelligence technique, which is an adaptation of Abbass's marriage in honey-bee optimization (MBO), with the aim to achieve better overall performance than the original version of the MBO while also lowering the computation time for finding the optimal solution. The original MBO has been proven to be one of the best swarm intelligence algorithms for solving optimization problems. However, many parameters need to be properly set in order for the MBO to perform at its best. Therefore, long computation time caused by a large number of trial and error iterations involved in trying to find the right combination of parameters is unavoidable. The framework of the proposed algorithm is similar to the original MBO, which is based on the marriage behavior of honey-bees. In order to improve the efficiency of the MBO algorithm, several aspects of the original MBO have been adapted, such as (1) the proposed algorithm is adapted to obtain the ability to automatically search for the proper number of queens, (2) the proposed algorithm divides the problem space into several colonies, each of which has its own queen. In order to keep the number of colonies to a minimum, the proposed algorithm, therefore, encourages the queens to compete with each other for a larger colony and also urges the newly-born brood which is fitter than the queen of the colony to overthrow the queen. (3) the fuzzy c-means algorithm is employed to assign the drones to the proper colonies. The proposed algorithm has been evaluated and compared to the original MBO algorithm. The experimental results on six benchmark problems demonstrate the potential of the proposed algorithm in offering an efficient and effective solution to the problem. © 2011 Elsevier Ltd. All rights reserved.
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A new algorithm for flexible job-shop scheduling problem based on particle swarm optimization
(2016-03-01)
Teekeng, Wannaporn
;
Thammano, Arit
;
Unkaw, Pornkid
;
Kiatwuthiamorn, Jiraporn
This paper proposes a new algorithm, named EPSO, for solving flexible job-shop scheduling problem (FJSP) based on particle swarm optimization (PSO). EPSO includes two sets of features for expanding the solution space of FJSP and avoiding premature convergence to local optimum. These two sets are as follows: (I) particle life cycle that consists of four features: (1) courting call—increasing the number of more effective offspring (new solutions), (2) egg-laying stimulation—increasing the number of offspring from the better parents (current solutions), (3) biparental reproduction—increasing the diversity of the next generation (iteration) of solutions, and (4) population turnover—succeeding the population (the current set of all solutions) in the previous generation by a population in a new generation that is as able but more diverse than the previous one; and (II) discrete position update mechanism—moving particles (solutions) towards the flight leader (the best solution), namely, interchanging some integers in every solution with those in both the best solution and itself, using similar swarming strategy as the update procedure of the continuous PSO. The basic objective function used was to minimize makespan which is the most important objective, hence, providing the simplest way to measure the effectiveness of the generated solutions. Benchmarking EPSO with 20 well-known benchmark instances against two widely-reported optimization methods demonstrated that it performed either equally well or better than the other two.
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Differential Evolution with Self-Adaptive Mutation and Population Improvement Strategy for Optimization Problems
(2024-01-01)
Farda, Irfan
;
Thammano, Arit
;
Morris, John
Differential Evolution (DE) algorithms are widely recognized as effective metaheuristic techniques used to solve continuous optimization problems. However, DE algorithms encounter limitations in exploration and exploitation due to their sensitivity to parameter settings and reliance on mutation strategies. To overcome these drawbacks, we introduced a self-adaptive mutation and population improvement strategy in differential evolution, SAMPIDE. The algorithm adjusts the mutation operator automatically according to the successful mutation operator value from the previous generation. Additionally, to enhance the algorithm's convergence, we adopted a random learning mechanism to update the population. SAMPIDE was compared with nine state-of-the-art DE variants and four other metaheuristic algorithms across thirty-two benchmark functions. SAMPIDE consistently outperformed others on unimodal, step and noisy functions, while maintaining competitive performance on multimodal functions. Additionally, when tested on five real-world problems, SAMPIDE outperformed other metaheuristic algorithms. Thus, the self-adaptive mutation and population improvement strategy incorporated into SAMPIDE positioned it as a promising solution for a wide range of optimization problems.
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An artificial neural network model in economic forecasting: A study in Thailand's natural rubber industry
(1998-12-01)
Thammano, Arit
;
Raviwongse, Rawin
;
Tanatammatid, Monticha
In the past forty years, the world demand for rubber, both natural and synthetic, as raw material has increased drastically. However, the use of natural rubber in industry has been declining due to several reasons such as unreliable quality, delayed delivery time, and fluctuating prices. Since Thailand is among the top three natural rubber producers in the world, in addition to the need to promote the use of natural rubber, the country must also improve its competitive edge in exporting them over other exporters. As such, one way to support the improvement is to develop a reliable model to forecast the demand and supply of natural rubber in the world market. In this study, an artificial neural network technique is applied to (i) study and forecast the world demand of natural rubber by region and (ii) estimate the appropriate export quantity for Thailand's natural rubber industry. The output from the neural network model can be directly applied to the economic planning for Thailand's natural rubber industry.

Thammano, Arit

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