Hybrid Artificial Intelligence–Based PBA for Benchmark Functions and Facility Layout Design Optimization

Abstract

Facility layout design (FLD) 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, FLD 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, is being increasingly used to resolve various complex optimization problems. 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 and 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), differential evolution (DE), bee algorithm, and particle swarm optimization for multidimensional benchmark function problems. Additionally, this study compares PBA performance against bee algorithm and particle swarm optimization (PSO) performance in practical FLD problems. Results show that PBA performance is comparable to those of the mentioned algorithms in the benchmark functions and can be efficiently employed to solve practical FLD problem with high dimensionality.