A Hybrid of Nelder-Mead Simplex and Attractive-Repulsive Particle Swarm Optimization
碩士 === 國立臺灣海洋大學 === 商船學系所 === 98 === In this study, a hybrid method of Attractive and Repulsive Particle Swarm Optimization (ARPSO) and Nelder and Mead simplex method for global optimization algorithm was developed. The proposed algorithm incorporates strategies for search intensification and divers...
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ndltd-TW-098NTOU57280202015-10-13T19:35:32Z http://ndltd.ncl.edu.tw/handle/25048816048508062536 A Hybrid of Nelder-Mead Simplex and Attractive-Repulsive Particle Swarm Optimization 複合Nelder-Mead單純形與吸引排斥型粒子群最佳化演算法 Chien-Peng Yu 游鈐芃 碩士 國立臺灣海洋大學 商船學系所 98 In this study, a hybrid method of Attractive and Repulsive Particle Swarm Optimization (ARPSO) and Nelder and Mead simplex method for global optimization algorithm was developed. The proposed algorithm incorporates strategies for search intensification and diversification. The particles of ARPSO can adjust the aggregation or dispersion directions of particle movement based on particle distribution in space. Comparison with original PSO method, ARPSO has more powerful global search capability. The Nelder and Mead direct search method is a simplex method which don't need gradient calculation, and with fast convergence properties, but applying for local optimization problems. Combining the above search methods, the hybrid ARPSO+NM search method for global optimization was implemented. The accuracy and effectiveness of ARPSO+NM method were verified by some benchmark problems. And also, the ARPSO+NM method was applied to both steady the dynamic shortest path planning. It is shown that ARPSO+NM method retain the advantages of both ARPSO and NM methods. Juan-Chen Huang 黃俊誠 2010 學位論文 ; thesis 90 zh-TW |
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碩士 === 國立臺灣海洋大學 === 商船學系所 === 98 === In this study, a hybrid method of Attractive and Repulsive Particle Swarm Optimization (ARPSO) and Nelder and Mead simplex method for global optimization algorithm was developed. The proposed algorithm incorporates strategies for search intensification and diversification. The particles of ARPSO can adjust the aggregation or dispersion directions of particle movement based on particle distribution in space. Comparison with original PSO method, ARPSO has more powerful global search capability. The Nelder and Mead direct search method is a simplex method which don't need gradient calculation, and with fast convergence properties, but applying for local optimization problems. Combining the above search methods, the hybrid ARPSO+NM search method for global optimization was implemented. The accuracy and effectiveness of ARPSO+NM method were verified by some benchmark problems. And also, the ARPSO+NM method was applied to both steady the dynamic shortest path planning. It is shown that ARPSO+NM method retain the advantages of both ARPSO and NM methods.
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author2 |
Juan-Chen Huang |
author_facet |
Juan-Chen Huang Chien-Peng Yu 游鈐芃 |
author |
Chien-Peng Yu 游鈐芃 |
spellingShingle |
Chien-Peng Yu 游鈐芃 A Hybrid of Nelder-Mead Simplex and Attractive-Repulsive Particle Swarm Optimization |
author_sort |
Chien-Peng Yu |
title |
A Hybrid of Nelder-Mead Simplex and Attractive-Repulsive Particle Swarm Optimization |
title_short |
A Hybrid of Nelder-Mead Simplex and Attractive-Repulsive Particle Swarm Optimization |
title_full |
A Hybrid of Nelder-Mead Simplex and Attractive-Repulsive Particle Swarm Optimization |
title_fullStr |
A Hybrid of Nelder-Mead Simplex and Attractive-Repulsive Particle Swarm Optimization |
title_full_unstemmed |
A Hybrid of Nelder-Mead Simplex and Attractive-Repulsive Particle Swarm Optimization |
title_sort |
hybrid of nelder-mead simplex and attractive-repulsive particle swarm optimization |
publishDate |
2010 |
url |
http://ndltd.ncl.edu.tw/handle/25048816048508062536 |
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