Bare bones artificial bee colony algorithm with parameter adaptation and fitness-based neighborhood

Concerning this issue that the solution search equation of artificial bee colony algorithm (ABC) does well in exploration but badly in exploitation, a bare bones ABC with parameter adaptation and fitness-based neighborhood is proposed, BABC for short reference. The proposed method employs a Gaussian search equation to produce a new candidate individual at the onlooker phase which exploits the valuable information hidden in the best individual to improve the exploitation, while, at the employed bee phase, a parameter adaptation strategy and a fitness-based neighborhood mechanism are integrated into the search equation which can take advantage of the information from the previous search and better individuals to enhance the search ability. The proposed framework can be applied to any ABC with minimal changes. Furthermore, the proposed framework is applied to the original ABC and several highly regarded ABC variants. The comparison results demonstrate that the proposed framework is able to significantly improve the performance of the original ABC and the modified ABC variants.