Enhancing performance of particle swarm optimization through an algorithmic link with genetic algorithms
Evolutionary Algorithms (EAs) are emerging as competitive and reliable techniques for several optimization tasks. Juxtapositioning their higher-level and implicit correspondence; it is provocative to query if one optimization algorithm can benefit from another by studying underlying similarities and...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer US
2016
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| Online Access: | http://hdl.handle.net/1721.1/103318 https://orcid.org/0000-0001-5833-5178 |