KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Search Results

Now showing 1 - 10 of 15

Some of the metrics are blocked by your
consent settings
A Novel Robust Adaptive Control for PMDC Servo Motor Incorporating Recursive Least Square and Particle Swarm Optimization
(2023-01-01)
Yanyong, Sarucha
;
Kaitwanidvilai, Somyot
Position control is a crucial control system used extensively in various industrial applications. In many of these applications, system parameters, such as the mass of an object can change unpredictably. Non-adaptive position control proves inadequate in successfully managing these fluctuations. Thus, an online adaptive control system plays a vital role in maintaining high performance despite changing parameters. This paper proposes a novel online adaptive fixed-structure robust controller for a PMDC (Permanent Magnet Direct Current) servo motor. This controller employs Recursive Least-square and Particle Swarm Optimization techniques to form the adaptive system. Simulation results demonstrate the effectiveness of our proposed technique and its potential application.
Some of the metrics are blocked by your
consent settings
Thai food recommendation system using hybrid of particle swarm optimization and K-means algorithm
(2021-04-23)
Puraram, Tanakorn
;
Chaovalit, Pimwadee
;
Peethong, Apatha
;
Tiyanunti, Pongsak
;
Charoensiriwath, Supiya
A food recommendation system is an information filtering tool that helps suggest appropriate food menus to users based on their dietary behavior, nutrition, health, or activity. In this paper, a hybrid method of Particle Swarm Optimization (PSO) and K-Means algorithm is proposed to improve the user's dietary behavior clustering and using Principal Component Analysis (PCA) to reduce the data dimension. Moreover, the User-Based Collaborative Filtering technique is used to predict the rating of relevant Thai food menus and recommendation. The experimental result shows the hybrid method improves the clustering performance from 3 models: Hierarchical Clustering, K-Means, and K-Means with PCA, in terms of silhouette coefficient score. In addition, the hybrid method improves the Davies-Bouldin index score by 44%, 19%, and 17% compared to those models, respectively. The rating prediction result shows the hybrid method outperforms the other methods.
Some of the metrics are blocked by your
consent settings
Improving knn algorithm based on weighted attributes by pearson correlation coefficient and pso fine Tuning
(2020-10-21)
Sinhashthita, Wanarase
;
Jearanaitanakij, Kietikul
Assigning proper weights to attributes in some datasets according to their importances can significantly improve the classification accuracy. Weighted attributes can support the classification methods effectively if their weights truly represent by their importances. In this research, we improve the K-Nearest Neighbors (KNN) algorithm by using Pearson correlation coefficient along with Particle Swarm Optimization (PSO) to find the optimal set of weights for attributes in the dataset. The experimental results show that the proposed method can significantly improve the classification accuracy when compared to the traditional KNN algorithm.
Some of the metrics are blocked by your
consent settings
Mutation Variations in Improving Local Optima Problem of PSO
(2020-01-01)
Adsawinnawanawa, Ekkarat
;
Kruatrachue, Boontee
This paper experiment on various concepts in performing mutation to lessen trap in a local optima problem of Particle swarm optimization (PSO). The first concept is when to perform mutation. The earlier mutation favors exploration more than exploitation and usually leads to slow convergence, while the late mutation tends to have opposite characteristics. The second concept is the reset of a known best position (GBEST) when trapping in local optima. The reset reduces the chance of trapping in the same local optima but may lead to slower convergence. On the other hand, mutations without reset best position exploit previous knowledge and converge faster if the GBEST closes to optima. The performances of each concept are compared using 27 benchmark test functions. The results are mixing, but the early mutation without reset GBEST perform better in many of test function.
Some of the metrics are blocked by your
consent settings
A time improvement PSO base algorithm using multithread programming
(2019-12-01)
Thongkrairat, Somsin
;
Chutchavong, Vanvisa
This paper proposes a method to improve the processing time of algorithm that bases on Particle Swarm Optimization (PSO) such as Original PSO, AS-PSO (Ant Supervised by Particle Swarm optimization), ACO (ant colony optimization) by using multithread programming technique. Typically, the PSO Base algorithm creates several particles to find the optimized solution. Each particle owns its local process and resource, but original implementation has been processed in a single thread or sequential computation system. That causes the system to take a longer time to compute when increasing the number of particles. It makes the system slow down when implementing a massive amount of particles and ambiguous to estimated efficiency for each algorithm. This method takes advantage of a multithread computing system for utilizing resources on the system to reduce computation time. The proposed method use thread as particle to manage local resource and implement multithreading system and use thread synchronization system to manage global resource and exchange data between particle. To measure the efficiency of this method Traveling Salesman Problem (TSP) used to be a problem to optimize and compare computation time between the original method and the proposed method. The experimental result show correctness and time improvement of the proposed method compared with the original method.
Some of the metrics are blocked by your
consent settings
Applied Particle Swarm Optimization in Solving Container Loading Problem for Logistics
(2019-04-10)
Koonsintananan, Sasithom
;
Kimpan, Warangkhana
Container loading problem (CLP) is a major problem for logistics in distributing cargoes to customers. As cost and time savings are important in shipping management, the process of cargo handling is essential. Cargoes must be effectively packed into a container. This paper, therefore, proposes container loading problem solving using a strategy for positioning non placement heuristics to reduce space between different buildings. Particle Swarm Optimization (PSO) is used to select the orientation of boxes to be packed into a single container to minimize the space which simulated in 3D. The condition for this solution is packing must be placed within the container with no part of the box overhanging the container, and the boxes must be placed parallel to the container surface area or other boxes. Weight and orientation of the box condition are also considered. The experiments are 12 cases. Different sizes of boxes and different sizes of containers are used. The experimental results are satisfied and it can be concluded that varied sizes of boxes are applicable for the space utilization in the container.
Some of the metrics are blocked by your
consent settings
Enhance particle's exploration of particle swarm optimization with individual particle mutation
(2019-03-01)
Adsawinnawanawa, Ekkarat
;
Kruatrachue, Boontee
;
Siriboon, Kritawan
This paper proposed the restart of slow improvement particle with Mutation of its position. This can alleviate the trapped in local optima problem of Particle swarm optimization (PSO). The main characteristic of this method is the gradually restart of particle instead of restart all particle at the same time (rerun). This seems to maintain fast convergence of PSO and avoid overhead of restart the whole swarm. The performance of the proposed method is compared to others algorithms with 26 benchmark test functions. The results confirm the faster convergence with optimum results of the proposed method in most test functions.
Some of the metrics are blocked by your
consent settings
Combine multi particle swarm in supporting trapping in local optima
(2018-08-13)
Poempool, Lukkana
;
Kruatrachue, Boontee
;
Siriboon, Kritawan
This paper proposed using multi swarm to lessen trapping in local optima problem of Particle Swarm Optimization (PSO). The use of multiple swarms can increase wider global search at the cost of decrease narrower local search. Hence, the use of multiple swarms alone can't solve the trapping problem. This paper extends the use of multi swarm by merging all the swarm into single group to enhanced local search when needed. This can increase the local search ability of multi swarm and increase the chance of the trapped swarm to move to other nearby local optima. The proposed method is compared to similarly comparable modified PSO and other similarly multi particle swarm without merging using 26 benchmark functions.
Some of the metrics are blocked by your
consent settings
Improving Particle Swarm Optimization by using incremental attribute learning and centroid of particle's best positions
(2017-11-03)
Srimakham, Sornnarong
;
Jearanaitanakij, Kietikul
Particle Swarm Optimization (PSO) is a powerful algorithm that can search a solution for a function which contains a large number of peaks and valleys. However, PSO might encounter a difficulty when the function gets more complex or the number of attributes (dimensions) grows larger. This paper proposes a modification of PSO by using the incremental attribute strategy along with the centroid of particle's best positions to avoid the local minima which can easily occur in a multimodal problem. The experimental results from four standard benchmarks show that the proposed method can improve PSO in terms of optimality and stability when compared with the conventional PSO and another incremental attribute-based PSO.
Some of the metrics are blocked by your
consent settings
A modified particle swarm optimization with dynamic mutation period
(2014-01-01)
Ratanavilisagul, Chiabwoot
;
Kruatrachue, Boontee
The particle swarm optimization (PSO) is an algorithm that attempts to search for better solution in the solution space by attracting particles to converge toward a particle with the best fitness. PSO is typically troubled with the problems of trapping in local optimum and premature convergence. In order to overcome both problems, we propose an improved PSO algorithm that is applied mutation operator dynamically when particles are in local optimum. Moreover, the mutation period can be adjusted to solve the problem appropriately. The proposed technique is tested on benchmark functions and gives more satisfied search results in comparison with PSOs for the benchmark functions. © 2014 IEEE.

KMITL

Browse

Filters

Settings

Sort By

Results per page

Search Results