ABC and PSO: A comparative analysis

Multidimensional optimization is critically important in engineering, economics and other disciplines. There are several deterministic approaches to optimization, each of which has its own pros and cons. Swarm intelligence-based optimization algorithms have been gained popularity for their effectiveness and resource efficiency. Two such popular algorithms, namely artificial bee colony (ABC) and particle swarm optimization (PSO) have been investigated in this paper. These algorithms have been implemented to minimize a few unimodal and multimodal benchmark functions. Results have been analyzed and the comparative analysis has been performed in terms of quality of solutions and computing time. Matlab simulation results show that the ABC algorithm delivers more accurate optimization than PSO does; but it suffers from delayed convergence. On the other hand, PSO is faster; but its accuracy is relatively inferior.