A Hybrid Swarm Intelligence Based Particle Bee Algorithm for Benchmark Functions and Construction Site Layout Optimization

Abstract

The construction site layout (CSL) design presents a particularly interesting area of study because of its relatively high level of attention to usability qualities, in addition to common engineering objectives such as cost and performance. However, it is difficult combinatorial optimization problem for engineers. Swarm intelligence (SI) was very popular and widely used in many complex optimization problems which was collective behavior of social systems such as honey bees (bee algorithm, BA) and birds (particle swarm optimization, PSO). 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 compares the performance of PBA with that of genetic algorithm (GA), differential evolution (DE), bee algorithm (BA) and particle swarm optimization (PSO) for multi-dimensional benchmark numerical problems. Besides, this study compares the performance of PBA with that of BA and PSO for practical construction engineering of CSL problem. The results show that the performance of PBA is comparable to those of the mentioned algorithms in the benchmark functions and can be efficiently employed to solve a hypothetical CSL problem with high dimensionality.