| Summary: | 博士 === 國立臺灣科技大學 === 營建工程系 === 99 === The construction site layout (CSL) design presents a particularly interesting area of study because of its relatively high level of attention to aesthetics and usability qualities, in addition to common engineering objectives such as cost and performance. However, they present a difficult combinatorial optimization problem for engineers. Swarm intelligence (SI), an approach to decision making that integrates collective social behavior models such as the bee algorithm (BA) and particle swarm optimization (PSO), is being increasingly used to resolve various complex optimization problems. In order to integrate BA global search ability with the local search advantages of PSO, this study proposes a new optimization hybrid swarm algorithm – the particle bee algorithm (PBA) which imitates the intelligent swarming behavior of honeybees and birds. This study also proposes a neighborhood-windows (NW) technique for improving searching efficiency as well as a self-parameter-updating (SPU) technique for preventing trapping into a local optimum in high dimensional problems. This study compares the performance of PBA with that of genetic algorithm (GA), evolutionary algorithms (EA), differential evolution (DE), bee algorithm (BA) and particle swarm optimization (PSO) for multi-dimensional benchmark function problems. Besides, this study compares PBA performance against bee algorithm (BA) and particle swarm optimization (PSO) performance in those hypothetical floor level (FL) and site level (SL) CSL problems. Results show PBA performance is comparable to those of the mentioned algorithms in the benchmark functions and can be efficiently employed to solve those hypothetical floor level and site level CSL problems with high dimensionality.
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