Improved wolf pack algorithm based on adaptive step size and Levy flight strategy
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Abstract:
Swarm intelligence heuristic algorithms offer several advantages in solving large-scale distributed problems. This paper addresses the shortcomings of the traditional wolf pack algorithm which is prone to fall into local optimal and low precision. The paper proposes an improved wolf pack algorithm incorporating adaptive step size and levy flight search strategy after analyzing the characteristics of the wolf pack. Firstly, optimizing the adaptive step size improves search precision, effectively accelerates the convergence speed. Secondly, the incorporation of the levy flight search strategy of expands the search scope, improving the global search capability of the algorithm. Finally, to verify the algorithm’s performance, simulations and real-world cases were conducted, comparing it with other improved algorithms. The test results show that the improved wolf pack algorithm has obvious advantages in convergence speed, accuracy and stability.
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Supported by National Natural Science Foundation of China（11202062）, Science and Technology Research Project of Hebei Province Colleges and Universities（ZD2019114）.
