The artificial bee colony(ABC) algorithm is a simple and effective global optimization algorithm which has been successfully applied in practical optimization problems of various fields. However, the algorithm is still insufficient in balancing exploration and exploitation. To solve this problem, we put forward an improved algorithm with a comprehensive search mechanism.The search mechanism contains three main strategies. Firstly, the heuristic Gaussian search strategy composed of three different search equations is proposed for the employed bees, which fully utilizes and balances the exploration and exploitation of the three different search equations by introducing the selectivity probability Ps. Secondly, in order to improve the search accuracy, we propose the Gbest-guided neighborhood search strategy for onlooker bees to improve the exploitation performance of ABC. Thirdly, the selfadaptive population perturbation strategy for the current colony is used by random perturbation or Gaussian perturbation to enhance the diversity of the population. In addition, to improve the quality of the initial population, we introduce the chaotic oppositionbased learning method for initialization. The experimental results and Wilcoxon signed ranks test based on 27 benchmark functions show that the proposed algorithm, especially for solving high dimensional and complex function optimization problems, has a higher convergence speed and search precision than ABC and three other current ABC-based algorithms. The artificial bee colony (ABC) algorithm is a simple and effective global optimization algorithm which has been applied successfully in practical optimization problems of various fields. However, the algorithm is still insufficient in balancing exploration and exploitation. To solve this problem, we put forward an improved algorithm with a comprehensive search mechanism. The heuristic Gaussian search strategy composed of three different search equations is for the employed bees, which fully explo and balances the exploration and exploitation of the three different search equations by introducing the selectivity probability Ps. Secondly, in order to improve the search accuracy, we propose the Gbest-guided neighborhood search strategy for onlooker bees to improve the exploitation performance of ABC. Thirdly, the selfadaptive population perturbation strategy for the current colony is used by random perturbation or Ga ussian perturbation to enhance the diversity of the population. In addition, to improve the quality of the initial population, we introduce the chaotic opposition based based method for initialization. The experimental results and Wilcoxon signed ranks test based on 27 benchmark functions show that the proposed algorithm , especially for solving high dimensional and complex function optimization problems, has a higher convergence speed and search precision than ABC and three other current ABC-based algorithms.