| Summary: | 碩士 === 國立臺灣科技大學 === 營建工程系 === 100 === This study hybridizes particle swarm optimization (PSO) and artificial bee colony (ABC) to develop approaches which is more applicable than ABC and PSO.
To hybride of ABC and PSO(HBP) approaches, agents including PSO particles and ABC bees are categorized into two sub-swarms by their species. Sequentially, agents in a sub-swarm are allowed to migrate to the other sub-swarm based on the
sub-swarm fitness. And then, the PSO sub-swarm is permitted to learn from the global information, which involves the best position of the ABCsub-swarm.
Twenty-three benchmark functions are employed to compare performance of HBP approaches against single ABC and PSO approaches. A practical hospital facility layout problem is investigated to validate the practicality of the HBP approaches.
Results reveal the designed HBP approaches have dynamical sub-population sizes,superior performance to single ABC and PSO approaches, improvidences on a referenced hospital layout, and high practicality without judging performance of ABC and PSO on problems.
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