Enhancing Particle Swarm Optimization Using Regulators Based on Location and Fitness Deviation
博士 === 國立成功大學 === 資訊管理研究所 === 104 === In spite of the varying position and fitness of each distinct particle, most of the PSO algorithms treat the given swarm of particles simply. This study aims to find good controls for facilitating exploration and exploitation movements to enhance the traditional...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Others |
| Language: | en_US |
| Published: |
2015
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| Online Access: | http://ndltd.ncl.edu.tw/handle/50893787782623821565 |