Int. J. of Bio-Inspired Computation   »   2017 Vol.10, No.4

 

 

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.

 

DOI: 10.1504/IJBIC.2016.10004342

 

Int. J. of Bio-Inspired Computation, 2017 Vol.10, No.4, pp.275 - 282

 

Available online: 03 Nov 2017

 

 

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