Abstract—This paper presents a comparative study of three Swarm Intelligence approaches which are: Bat Algorithm (BA), Firefly Algorithm (FA) and Artificial Bee Colony (ABC) Algorithm applied to a set of standard benchmark functions. The results of this study were analyzed and compared on the basis of mean value of obtained objective values. All of these approaches were investigated taking into consideration several dimensionalities which are 10, 20,30, 50 and 100. Statistical results indicated that ABC significantly surpassed BA and FA
on the majority of the experimental instances and FA was able to significantly archive better objective values than BA in most of the test cases. The paper finally concludes with some future work directions.
Index Terms—ABC, BA, FA, optimization, swarm intelligence.
Saad T. Alharbi is with the Department of Computer Science, University Saudi Arabia, Madinah, KSA (e-mail: stharbi@taibahu.edu.sa).
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Cite: Saad T. Alharbi, "A Comparative Study of Recent Swarm Intelligence
Approaches on Global Optimization," International Journal of Modeling and Optimization vol. 9, no. 2, pp. 62-66, 2019.
