An Improved Artificial Bee Colony Optimization Algorithm Based on Slime Mold and Marine Predator
Liyi Zhang, Jinyan Tang, Ting Liu, Zuochen Ren · Automatic Control and Computer Sciences · 2022
Abstract Artificial bee colony (ABC) algorithm is widely used in optimization problems due to its few parameters and simple structure. However, there are shortcomings such as relatively slow convergence and easy to optimize stagnation. This paper proposes an artificial bee colony algorithm based on slime mold and marine predators (SMA-MPA-ABC) to overcome the insufficiency of ABC algorithm. The slime mold foraging strategy is introduced into the neighborhood search process of employed bees. The employed bee generates positive feedback and negative feedback according to the size of the food concentration, generates adaptive weights, and adjusts the search mode to search in the direction of the optimal solution to speed up the convergence speed. In addition, the fish gathering device effect in the marine predator algorithm is introduced in the scout bee phase to avoid optimization stagnation. The simulation experiment results of 15 benchmark test functions show that SMA-MPA-ABC algorithm has a faster convergence speed, higher convergence accuracy, and better stability than the ABC algorithm and other improved ABC algorithms.