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Convergence analysis of ABC algorithm based on difference model
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AI
DOI：10.1016/j.asoc.2023.110627.png)
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Abstract
En 中文
In the cross-mutation phase of the Artificial Bee Colony optimization algorithm (ABC algorithm), the value of parameter & phi; is very important to the stability of ABC algorithm, however, most literatures did not specify the origin of the value range of parameter & phi;. Through the theoretical analysis of the stability of the ABC algorithm, appropriate parameters can be selected for the algorithm under different convergence conditions and different application conditions, and then the rapid convergence of the algorithm can be achieved, so the stability analysis of the algorithm is very worth exploring. This paper firstly introduces the ABC algorithm, and then differentiates the motion trajectory equations of employed bees in the ABC algorithm to obtain a differential model; then uses the discrete system stability theory to analyze the stability of the system, and obtains the relationship between the value range of the parameters in the cross-mutation stage and the stability of the algorithm. Finally, the correctness of the conclusion will be verified by experiments using the test function. The results obtained show that ABC algorithm with & phi; & ISIN; (-2, 0) converges faster, and has better global optimization performance than ABC algorithm with other values of & phi;, which are investigated in this paper. & COPY; 2023 Elsevier B.V. All rights reserved.
Keywords:
ABC algorithm
Motion trajectory
Difference model
Stability
Journal
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6.6
Papers:
1.4W
Citations:
4.8W
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