Improved Artificial Bee Colony Algorithm with Adaptive Parameter for Numerical Optimization

Abstract

The problem that ABC (Artificial Bee Colony) algorithm is good at exploration but poor at exploitation for the numerical optimization is investigated in this paper. PA-ABC (Parameter Adaptive ABC) algorithm is proposed, which adopts different search equations with different search abilities for the employed bee and the onlooker bee. Firstly, the best-so-far solution is introduced into each search equation to enhance exploitation; secondly, the employed bee uses two random solutions to search, so as to keep high ability of exploration; thirdly, the onlooker bee searches around a random solution to keep population diversity; most importantly, adaptive parameter computed by fitness function is introduced in the search equation of the onlooker bee, which makes the search step adjust according to the search process. So the search equation of the employed bee has balanced abilities of exploration and exploitation, while the search equation of the onlooker bee can make the search focus transfer from exploration to exploitation adaptively. The experiment results on benchmark functions show that the search performance of PA-ABC is higher than or at least comparable to basic ABC and typical improved ABCs. In addition, compared to the performance of the state-of-the-art ABC variants under their original parameter configuration, PA-ABC is verified to have similar performance to them.