A new hybrid parallel genetic algorithm for multi-destination path planning problem

This paper proposes a new parallel approach of multi objective genetic algorithm for path planning problem. The main contribution of this work is to reduce the population size that effect in decreasing processing times of finding the optimum path for multi destination problem. This is achieved by combining the local population of island parallel approach and global population of global parallel approach. Various experiments have been conducted to evaluate the new hybrid parallel genetic algorithm (HPGA) in solving multi-objective path planning problems. Three different test areas with 2 destinations were used to assess the performance of HPGA. Furthermore, this work compares HPGA and sequential genetic algorithm (SeqGA), as well as compared to other existing parallel genetic algorithm (GA) methods. From experimental results show that proposed HPGA outperform others, in term of processing time i.e., up to 3.6 times speedup faster, and lowest GA parameter values. This proposed HPGA can be utilized to design robots with fast and consistent path planning, especially with various obstecles.