Comparative performance analysis of bat algorithm and bacterial foraging optimization algorithm using standard benchmark functions

Abstract

Optimization problem relates to finding the best solution from all feasible solutions. Over the last 30 years, many meta-heuristic algorithms have been developed in the literature including that of Simulated Annealing (SA), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Harmony Search Algorithm (HS) to name a few. In order to help engineers make a sound decision on the selection amongst the best meta-heuristic algorithms for the problem at hand, there is a need to assess the performance of each algorithm against common case studies. Owing to the fact that they are new and much of their relative performance are still unknown (as compared to other established meta-heuristic algorithms), Bacterial Foraging Optimization Algorithm (BFO) and Bat Algorithm (BA) have been adopted for comparison using the 12 selected benchmark functions. In order to ensure fair comparison, both BFO and BA are implemented using the same data structure and the same language and running in the same platform (i.e. Microsoft Visual C# with .Net Framework 4.5). We found that BFO gives more accurate solution as compared to BA (with the same number of iterations). However, BA exhibits faster convergence rate.