Robust Variant of Artificial Bee Colony (JA-ABC4b)
Sulaiman, N. . 2017
The simplicity and robustness of the Artificial Bee Colony (ABC) algorithm has attracted the attention of optimization researchers. Although ABC has fewer tuned parameters, making it an easy-to-use tool, it has shown better performance than other prominent optimization algorithms such as differential evolution (DE), evolutionary algorithms (EA) and particle swarm optimization (PSO) algorithms at solving optimization problems. Despite these advantages, researchers have found that the standard ABC actually suffers from slow convergence on unimodal functions and is often trapped in local minima of multimodal functions. Most problematically, it does not balance the exploitation and exploration stages, leading to various inefficiencies in terms of capability. This paper presents a new ABC variant referred to as JA-ABC4b, which has been formulated to balance exploitation and exploration in order to boost optimization performance. JA-ABC4b has been experimentally tested on 27 benchmark functions and economic environmental dispatch (EED) problems. The results have revealed a robust performance of JA-ABC4b in comparison to other existing ABC variants and other optimization algorithms.
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The simplicity and robustness of the Artificial Bee Colony (ABC) algorithm has attracted the attention of optimization researchers.