ABC Versus PSO: A Comparative Study and Analysis on Optimization Aptitude

Lecture notes in networks and systems(2021)

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Many real-world optimization problems can be solved very efficiently by evolutionary algorithms. There exist many solutions in this field to solve multi-dimensional optimization problems, which is an important issue in today’s world. Swarm-based algorithms are very popular to solve these types of problems because of its effectiveness and quality of solutions. All swarm-based algorithms have different advantages and disadvantages. This paper has discussed two such mostly used algorithms, i.e., particle swarm optimization (PSO) and artificial bee colony (ABC). A fair comparison has been done between these two algorithms by implementing those to minimize five different standard benchmark functions. Results have been recorded and the performance of both the algorithms is analyzed with corresponding to the quality of solutions. It has been observed from the simulation results that ABC outperforms PSO in most of the cases corresponding to solution quality but it requires more time to converge than PSO.
Artificial bee colony, Particle swarm optimization, Exploration, Exploitation, Benchmark function
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