Artificial bee colony algorithm with multiple search strategies and neighborhood search

The artificial bee colony (ABC) algorithm does well in exploration but badly in exploitation in the process of solution search. During the evolution, How to balance the exploration and the exploitation of ABC algorithms has been a new challenge. To solve this problem, one new ABC algorithms was proposed which called NMuABC in this paper. In NMuABC algorithms, three different search methods are used in the employed bee phase, and everyone in the population dynamically selects the appropriate strategy for updating the solution during the evolutionary process. In onlooker bee phase, it uses the neighborhood selection mechanism to search near the elite solution to improve the development of the algorithm. In order to evaluate the performance of the NMuABC algorithm, 22 classic functions were used for testing. NMuABC was compared with other ABCs Algorithm. The results show that NMuABC algorithm performs better than compared algorithm, and it has fast global convergence speed and can get better solution.