In this paper, the modified artificial bee colony optimization algorithm, in which the accept–reject method is used as a part of the solution process in order to limit the random search space, is proposed for solving the optimization problems. The accept–reject method can find points of desirable quality and high accuracy with the help of probabilistic functions and limited the random search space, which increases the speed of the algorithm for the next steps and also reduces the number of iterations of the artificial bee algorithm. Then, the next phases of the bee algorithm will be executed in the interval determined by the accept–reject method. One of the most important issues in intelligent algorithms, especially the bee algorithm, is that in problems with several local optimal points, sometimes the algorithm gets stuck in the trap of local optimal points and does not reach the global optimal points. By using the motion step determination parameter in the employed bee phase, the algorithm can be guided to reach the global optimal points while correctly adjusting the movement of the bees for searching in random food sources, which will help improving the problem-solving process. This algorithm is tested by the basic benchmark functions as well as the benchmark functions of CEC 2005 and CEC2017 and CEC2019 and an engineering problem related to the planning of a virtual power plant. The results show that the proposed algorithm is superior in comparison to other algorithms.

本文提出了改进的人工蜂群优化算法来解决优化问题，其中接受-拒绝方法被用作求解过程的一部分，以限制随机搜索空间。接受-拒绝方法可以借助概率函数找到质量理想、精度高的点，并限制随机搜索空间，提高了算法下一步的速度，也减少了人工蜜蜂算法的迭代次数。然后，蜜蜂算法的下一阶段将在接受-拒绝方法确定的时间间隔内执行。智能算法尤其是蜜蜂算法中最重要的问题之一是，在具有多个局部最优点的问题中，有时算法会陷入局部最优点的陷阱而达不到全局最优点。通过在所采用的蜜蜂阶段使用运动步数确定参数，可以引导算法达到全局最优点，同时正确调整蜜蜂的运动以在随机食物源中进行搜索，这将有助于改善问题的解决过程。该算法通过基本基准函数以及CEC 2005、CEC2017和CEC2019的基准函数以及虚拟电厂规划相关的工程问题进行了测试。结果表明，与其他算法相比，本文提出的算法具有优越性。该算法可以引导算法达到全局最优点，同时正确调整蜜蜂的运动以搜索随机食物源，这将有助于改善问题的解决过程。该算法通过基本基准函数以及CEC 2005、CEC2017和CEC2019的基准函数以及虚拟电厂规划相关的工程问题进行了测试。结果表明，与其他算法相比，本文提出的算法具有优越性。该算法可以引导算法达到全局最优点，同时正确调整蜜蜂的运动以搜索随机食物源，这将有助于改善问题的解决过程。该算法通过基本基准函数以及CEC 2005、CEC2017和CEC2019的基准函数以及虚拟电厂规划相关的工程问题进行了测试。结果表明，与其他算法相比，本文提出的算法具有优越性。该算法通过基本基准函数以及CEC 2005、CEC2017和CEC2019的基准函数以及虚拟电厂规划相关的工程问题进行了测试。结果表明，与其他算法相比，本文提出的算法具有优越性。该算法通过基本基准函数以及CEC 2005、CEC2017和CEC2019的基准函数以及虚拟电厂规划相关的工程问题进行了测试。结果表明，与其他算法相比，本文提出的算法具有优越性。