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Artificial Bee Colony (ABC) algorithm is an optimization algorithm based on a particular intelligent behavior of honeybee swarms. The standard ABC has been utilized to deal with a lot of optimization problems in real world. However, there are still some defects of the standard ABC such as weak local-search capability and low solution precision. In order to improve the performance of ABC, in this paper, we propose two improved versions of ABC-EO and IABC-EO presented in our previous work, called ABC-EO II and IABC-EO II, where Extremal Optimization (EO) is introduced to ABC and IABC in different ways. There are some advanced characteristics of our proposed algorithms: (1) Compared with ABC-EO and IABC-EO, the improved versions have lower computational costs by introducing EO in different ways; (2) An easier-operated mutation method is introduced which can increase the diversity of new offspring …