Nootyasakool, Supakit

Preferred name

Alternative Name

Nootyaskool, Supakit

Main Affiliation

School of Information Technology

supakit.no@kmitl.ac.th

Now showing 1 - 8 of 8

Some of the metrics are blocked by your
consent settings
Reinforcement learning applied to scrum team towards large-scale global optimization
(2019-02-02)
Nootyaskool, Supakit
;
Ounsrimuang, Pimolrat
Large-scale problems have size of problem over a thousand dimensions in finding a best solution that uses long computation times. In this work, we use an idea of scrum methodology that is a well-known in software development companies, to create an optimization algorithm. The scrum methodology describing about the team organization likes as rugby team management that player have expert in game. The proposed algorithm is developed based on concept of the evolutionary computation by this work added agent specifics in leaning environment of the problem. The specific of the agent is reinforcement learning by taking an action and getting reward. The proposed algorithm was experimented on a large-scale global optimization finding optimum point of numerical function, comparing between with and without reinforcement learning. The experiment result showed that the usage of reinforcement learning has good results.
Some of the metrics are blocked by your
consent settings
Roulette wheel selection applied to PSO on numerical function in discrete and continuous space
(2016-07-22)
Ounsrimuang, Pimolrat
;
Nootyaskool, Supakit
Particle Swarm Optimization (PSO) successfully finds a solution as shown in various literatures. In some problems creating on discrete space, adjustment control-parameter may be difficult to modify a reach of optimum solution. The paper proposes an approach applying roulette wheel selection to PSO, which can help PSO escape from a local solution. This approach tested on both continuous and discrete space by finding solution of 12-numerical functions and an engineering-problem. The experiment result showed that the proposed technique can help PSO getting the best result both problem spaces, the performance improvement but also maintain easily to implementation.
Some of the metrics are blocked by your
consent settings
Hidden Markov Models predict foreign exchange rate
(2015-01-15)
Nootyaskool, Supakit
;
Choengtong, Wuttichow
This paper presents the prediction Thai baht by using Hidden Markov Models (HMM) with which the prediction model uses four factors, dollar index, interest rate, inflation rate and economic growth. The main idea of this work is a technique of encoding four factors into one observation sequence to train HMM. One result of prediction data will present four factors after decoding. The experiment is done using the data-by-day from 2002 to 2013 and showed that the technique has the mean percentage error of 0.167% to predict Thai currency exchange.
Some of the metrics are blocked by your
consent settings
The hybrid implementation genetic algorithm with particle swarm optimization to solve the unconstrained optimization problems
(2012-10-26)
Nootyaskool, Supakit
Genetic algorithm (GA) has an advantage in exploration search. Particle swarm optimization (PSO) has an advantage in sharing movement information between particles. The combining between GA and PSO is proposed in this research. We design hybrid-GA with PSO, and compare the performance with simple GA and simple PSO, which their models will find the solution of five-difference complexity of numerical functions. The experiment result showed that hybrid GA with PSO can find the solution of a multimodal problem and unimodal with noise signal quickly. © 2012 IEEE.
Some of the metrics are blocked by your
consent settings
Comparison solving discrete space on flower pollination algorithm, PSO and GA
(2016-03-23)
Rathasamuth, Wanthanee
;
Nootyaskool, Supakit
In order to find an optimal solution of a research problem, the problem parameters are encoded in discrete space (e.g. bits or integers). Genetic algorithm (GA) performs the discrete space by binary chromosome. Particle swarm optimization (PSO) uses probability and sigmoid function to convert the next bird's velocity into binary bit. Flower pollination algorithm (FPA) is quite new and also has a few number of the paper implements to discrete space. This research uses a cut-off parameter applied on FPA. The main objective is to examine the performance of the algorithms on discrete space and calculate velocity of PSO as well as Levy fight movement of FPA when using discrete parameter. The experiment used eight-numerical functions to test conversion from a real value to a bit value and measure the effectiveness of each algorithm. Experiment result showed that FPA could perform better than PSO and GA.
Some of the metrics are blocked by your
consent settings
Roulette wheel selection to encourage discrete Particle Swarm Optimization solving toll-keeper scheduling problem
(2015-12-30)
Ounsrimuang, Pimolrat
;
Nootyaskool, Supakit
Particle Swarm Optimization (PSO) has been proven to solve various applications by most applications using the real problem-space. In some specific problem, the discrete problem-space is selected on PSO that getting the solution result slowly by cause of sticky on local solution, and also the researcher cannot modify or difficult to understand how to improve the performance finding solution. The researcher many be tried to adjust velocity value by giving a new c1, c2 and weight to be a smaller or a larger value. This research proposed how to apply roulette wheel select to improve PSO on the discrete problem-space. Experiment tested the idea by a toll-keeper scheduling and a numerical function. Both problems created parameters inform discrete problem-space. The experiment result showed that PSO with roulette wheel selection taking the solution quickly.
Some of the metrics are blocked by your
consent settings
Optimization algorithm using scrum process
(2016-07-02)
Nootyaskool, Supakit
;
Ounsrimuang, Pimolrat
Scrum process is methodology for software development. Members in a scrum team have self-organizing team by planning and sharing knowledge. This paper introduces optimization algorithm using the population as scrum team doing the scrum process to find an optimum solution. The proposed algorithm maintains the level of exploration and the exploitation search by specific of the scrum-team. The experiment has compared the proposed approach with GA and PSO by finding an optimal solution of five numerical functions. The experiment result indicates that the proposed algorithm provides the best solution and finds the result quickly.
Some of the metrics are blocked by your
consent settings
Introducing scrum process optimization
(2017-11-14)
Ounsrimuang, Pimolrat
;
Nootyaskool, Supakit
Scrum is a well-known methodology in software development describing as organization in a developer team. A scrum team is a small size with expert members, rich communication, sharing knowledge, self-organization, and self-planning. Scrum process in this version developed by an attempt to create a self-organization team, which an action-reward function integrated in the proposed algorithm. This paper proposed a scrum process creating an optimization algorithm in the class of evolutionary computation. The proposed algorithm experimented on 30 numerical functions by the benchmark coding from the CEC2017 competition problems. The experiment results indicate that uses action-reward function to organize operation planning can help the proposed algorithm finding the best result.

Nootyasakool, Supakit

Filters

Settings

Sort By

Results per page