Abstract
Artificial Colony Algorithm (ABC) is a random optimization algorithm based on swarm search behavior, which is widely used in recent years. Considering the slow convergence and ease of falling into the local optimum of basic ABC, researchers try various modified methods to overcome its shortcomings. Different modified ABC algorithms have different characteristics and application scope. In order to improve the performance of ABC, and provide some guidance for the future improved development and application of the algorithm, four modified ABC algorithms are studied and compared in this paper. These four modified algorithms include improved search equation artificial bee colony algorithm, artificial bee colony algorithm based on control parameters, artificial bee colony algorithm based on block perturbation strategy and artificial bee colony algorithm based on chaos mapping strategy. To show the performance and characteristics in all aspects, 13 standard test functions are selected for simulation experiments. And the performance of four selected algorithms and basic artificial bee colony algorithm are analyzed from the average value, the optimal value, the worst value, the variance and the convergence ability. This paper summarizes the advantages and disadvantages of each algorithm and its applicable scope, and provides some guidance for the improvement and application of ABC.
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