Accelerating Artificial Bee Colony Algorithm with New Multi-Dimensional Selection Strategies

As a new intelligent swarm optimization algorithm, artificial bee colony (ABC) algorithm has been used to solve a lot of function optimization problems and successfully applied in many engineering fields. However, the single-dimensional search feature of the ABC algorithm results in a slower convergence rate. In this paper, we develop an improved ABC algorithm with new multi-dimension selection strategies (MDSABC) to enhance the search efficiency and improve the accuracy of the solution by selecting how many dimensions and which dimensions are updated. It specifically includes a multi-dimensional update strategy, neighbor and dimension selection strategies. The property of the MDSABC algorithm is tested on variety of benchmark functions with the original ABC algorithm and some classic improved ABC algorithms published in recent years. The experimental results show that the MDSABC algorithm can obviously improve the search efficiency and better than other algorithms.