PARTICLE SWARM OPTIMIZATION (PSO) COMPLETED BY NICHING AND CLUSTERING TECHNIQUES FOR MULTIMODAL FUNCTION
Multimodal is one of the most important found in optimization while there is an optimal solution both minimum and maximum global and local, and one of the biggest problems with developing optimization methods is how to localize the whole solution in multimodal functions, for this reason a method tha...
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id-itb.:466522020-03-10T11:00:15ZPARTICLE SWARM OPTIMIZATION (PSO) COMPLETED BY NICHING AND CLUSTERING TECHNIQUES FOR MULTIMODAL FUNCTION larwuy, Lennox Indonesia Theses Multimodal Optimization, Particle Swarm Optimization, Niching, Clustering, Benchmark Functions INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/46652 Multimodal is one of the most important found in optimization while there is an optimal solution both minimum and maximum global and local, and one of the biggest problems with developing optimization methods is how to localize the whole solution in multimodal functions, for this reason a method that can be used to answer these problems is needed. In this research the PSO method was modified to solve multimodal problems by adding the Niching and Clustering techniques then the results of the two methods were compared to see which was more efficient. Results of comparison of the two methods show that in terms of time efficiency, PSO equipped with Niching is superior to PSO equipped with Clustering, while in terms of level of accuracy, PSO equipped with Clustering has better accuracy where the results are closer to the actual solution. In addition, PSO equipped with Niching will be more efficiently applied to resolve multimodal which does not contain optimum local but there are many optimum global. While PSO equipped with Clustering techniques will be much more efficient to applied in cases where there are more than one global or local optimum point text |
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Multimodal is one of the most important found in optimization while there is an optimal solution both minimum and maximum global and local, and one of the biggest problems with developing optimization methods is how to localize the whole solution in multimodal functions, for this reason a method that can be used to answer these problems is needed. In this research the PSO method was modified to solve multimodal problems by adding the Niching and Clustering techniques then the results of the two methods were compared to see which was more efficient. Results of comparison of the two methods show that in terms of time efficiency, PSO equipped with Niching is superior to PSO equipped with Clustering, while in terms of level of accuracy, PSO equipped with Clustering has better accuracy where the results are closer to the actual solution. In addition, PSO equipped with Niching will be more efficiently applied to resolve multimodal which does not contain optimum local but there are many optimum global. While PSO equipped with Clustering techniques will be much more efficient to applied in cases where there are more than one global or local optimum point
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| format |
Theses |
| author |
larwuy, Lennox |
| spellingShingle |
larwuy, Lennox PARTICLE SWARM OPTIMIZATION (PSO) COMPLETED BY NICHING AND CLUSTERING TECHNIQUES FOR MULTIMODAL FUNCTION |
| author_facet |
larwuy, Lennox |
| author_sort |
larwuy, Lennox |
| title |
PARTICLE SWARM OPTIMIZATION (PSO) COMPLETED BY NICHING AND CLUSTERING TECHNIQUES FOR MULTIMODAL FUNCTION |
| title_short |
PARTICLE SWARM OPTIMIZATION (PSO) COMPLETED BY NICHING AND CLUSTERING TECHNIQUES FOR MULTIMODAL FUNCTION |
| title_full |
PARTICLE SWARM OPTIMIZATION (PSO) COMPLETED BY NICHING AND CLUSTERING TECHNIQUES FOR MULTIMODAL FUNCTION |
| title_fullStr |
PARTICLE SWARM OPTIMIZATION (PSO) COMPLETED BY NICHING AND CLUSTERING TECHNIQUES FOR MULTIMODAL FUNCTION |
| title_full_unstemmed |
PARTICLE SWARM OPTIMIZATION (PSO) COMPLETED BY NICHING AND CLUSTERING TECHNIQUES FOR MULTIMODAL FUNCTION |
| title_sort |
particle swarm optimization (pso) completed by niching and clustering techniques for multimodal function |
| url |
https://digilib.itb.ac.id/gdl/view/46652 |
| _version_ |
1821999661044465664 |