Title: Bacterial foraging optimisation algorithm, particle swarm optimisation and genetic algorithm: a comparative study
Author: Soheila Sadeghiram
Address: Department of Information Technology, Faculty of Engineering, University of Mohaghegh Ardabili, Iran
Abstract: Nature inspired meta-heuristic algorithms have been widely used in order to find efficient solutions for optimisation problems, and granted results have been achieved. Particle swarm optimisation (PSO) algorithm is one of the most utilised algorithms in recent years, which has indicated acceptable efficiency. On the other hand, bacterial foraging optimisation algorithm (BFOA) is relatively new compared to other meta-heuristic algorithms, and like PSO has shown a good ability to solve different optimisation problems. Genetic algorithms (GAs) are a well-known group of meta-heuristic algorithms which have been in use earlier than the other in various research fields. In this paper, we compare the efficiency of BFOA and PSO algorithms in an identical condition by minimising different test functions (from two to 20 dimensional). In this experiment, GA is used as a basic method in comparing the two algorithms. The methodology and results are presented. Although results verify the accurate convergency of both algorithms, the efficiency of BFOA on high-dimensional functions is dramatically better than that of PSO.
Keywords: particle swarm optimisation algorithm; bacterial foraging optimisation algorithm; BFOA; genetic algorithms; high-dimensional functions.
Int. J. of Bio-Inspired Computation, 2017 Vol.10, No.4, pp.275 - 282
Available online: 03 Nov 2017