An artificial bee colony algorithm with an adaptive search strategy selection mechanism and its application on workload prediction

Abstract

As one category of evolutionary algorithms, artificial bee colony (ABC) algorithms have many advantages such as few control parameters, simple structures and competitive performance. However, most ABC algorithms suffer from slow convergence and poor performance on optimization problems with strong correlations. To overcome these limitations, we propose a novel ABC with an Adaptive Search Strategy Selection (AS3) mechanism, called ABC-AS3. The basic idea of ABC-AS3 is to adaptively adjust search strategies between a one-dimensional search strategy and an all-dimensional search strategy based on probabilities that they are selected respectively within the most recent generations. We prove the rotation-variant and rotation-invariant properties of the one-dimensional and all-dimensional search strategies applied in the ABC-AS3 algorithm, respectively. The one-dimensional search strategy is used to maintain superiority on optimization with weak correlations. The all-dimensional one aims to accelerate the convergence speed and improve the performance on optimization with strong correlations. Simulation results show that ABC-AS3 outperforms state-of-the-art ABC variants and evolutionary algorithms in terms of solution quality, convergence speed, and numerical stability. In addition, we apply ABC-AS3 to optimize parameters of a multi-layer perceptron for workload prediction in data centers, which achieves better prediction accuracy and stability than baseline methods.