Empirical Analysis of Artificial Bee Colony Algorithm Parameters

The design of smart systems frequently includes various problems such as parameter estimation and optimisation, feature subset selection or model tuning. As a rule, these problems are quite challenging and as a way of tackling them, bio-inspired algorithms are recently becoming the approach of choic...

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Bibliographic Details
Published in:2018 International Conference on Smart Systems and Technologies (SST) pp. 109 - 116
Main Authors: Bajer, Drazen, Zoric, Bruno, Martinovic, Goran
Format: Conference Proceeding
Language:English
Published: IEEE 01.10.2018
Subjects:
algorithm parameters
artificial bee colony
Benchmark testing
numerical optimisation
Optimization
sensitivity analysis
Sociology
Statistics
Tuning
Online Access:Get full text
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Summary:The design of smart systems frequently includes various problems such as parameter estimation and optimisation, feature subset selection or model tuning. As a rule, these problems are quite challenging and as a way of tackling them, bio-inspired algorithms are recently becoming the approach of choice. Although a multitude of these algorithms is available, the artificial bee colony algorithm represents a viable candidate due to its good performance that has been demonstrated in a wide array of applications. However, the aforementioned performance is heavily reliant on the chosen parameter values. Tuning those parameters represents a significant ordeal. This paper is aimed at the empirical analysis of parameter influence. To this end, a somewhat detailed experimental analysis is conducted in order to compare the effectiveness of numerous parameter combinations. The paper also endeavours to provide certain guidelines with the hope of easing the utilisation of said algorithm for researchers and practitioners alike.
DOI:10.1109/SST.2018.8564632