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 35

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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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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.
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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.
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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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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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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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A modified marriage in Honey-bee Optimization for function optimization problems
(2011-01-01)
Poolsamran, Patcharawadee
;
Thammano, Arit
Many researches have implemented a genetic algorithm with real-coded chromosomes to solve a wide variety of problems. Their experimental results suggested that the real-coded genetic algorithms gave superior results to binary-coded genetic algorithm on most of the test problems. Inspired by the above founding, this study aims to (1) propose a modified Marriage in Honey-bee Optimization (MBO) technique (2) compare the performance of the proposed technique to that of the real-coded GA technique. In this study, two main ideas are proposed. Firstly, to handle the real encoding of genotypes, we present a new crossover operator and a new heuristic worker, named the scroll-based worker, for manipulating the real value of genes. Secondly, to reduce the number of user-defined parameters, we provide the original MBO with a self-organizing capability. With a self-organizing capability, the proposed model can automatically determine the proper number of queens itself. The experimental results on five benchmark test functions show that the proposed model is very effective in solving the function optimization problems. © 2011 Published by Elsevier Ltd.
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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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Sea turtle foraging algorithm for continuous optimization problems
(2016-01-01)
Tansui, Daranat
;
Thammano, Arit
For several modern algorithms-such as Genetic Algorithm (GA), Bee Colony Foraging Algorithm (BCFA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO)- Their evolving searching and learning processes to obtain the best answer in a reasonable time imitate the behaviors of animals in nature. This article presents a new algorithm called Sea Turtle Foraging Algorithm (STFA) that imitates sea turtles' food searching behavior of tracking the odor trail of Dimethyl Sulfide (DMS) originated from food sources. The displacement of a turtle is dictated by its active swimming movement and its passive movement due to ocean current. Our proposed STFA was performance tested with 5 standard test functions, and it was found that STFA was very effective and efficient.

Thammano, Arit

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