An improved Wolf pack algorithm for optimization problems: Design and evaluation

Abstract

Wolf Pack Algorithm (WPA) is a swarm intelligence algorithm that simulates the food searching process of wolves. It is widely used in various engineering optimization problems due to its global convergence and computational robustness. However, the algorithm has some weaknesses such as low convergence speed and easily falling into local optimum. To tackle the problems, we introduce an improved approach called OGL-WPA in this work, based on the employments of Opposition-based learning and Genetic algorithm with Levy’s flight. Specifically, in OGL-WPA, the population of wolves is initialized by opposition-based learning to maintain the diversity of the initial population during global search. Meanwhile, the leader wolf is selected by genetic algorithm to avoid falling into local optimum and the round-up behavior is optimized by Levy’s flight to coordinate the global exploration and local development capabilities. We present the detailed design of our algorithm and compare it with some other nature-inspired metaheuristic algorithms using various classical test functions. The experimental results show that the proposed algorithm has better global and local search capability, especially in the presence of multi-peak and high-dimensional functions.

Establishment and analysis of artificial neural network diagnosis model for coagulation-related molecular subgroups in coronary artery disease

“…This may involve combining regularization techniques with swarm intelligence optimization algorithms and ensemble methods to develop more reliable and generalizable models for genomic analysis. For example, the initial parameters of the model can be further optimized through the swarm intelligence optimization algorithm, such as the whale optimization algorithm (Brodzicki et al, 2021), Harris Hawks optimization algorithm (Qu et al, 2021), and wolf pack Frontiers in Genetics frontiersin.org optimization algorithm (Chen et al, 2021), to obtain more accurate results. Additionally, efforts to improve data quality and increase sample sizes can help reduce the risk of overfitting and enhance the robustness of artificial neural network-based predictive models in clinical applications.…”

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“…The act of rounding up is the last step of the wolf pack's hunting behavior, in which the wolves summoned by the head wolf attack the prey. When the hunting is completed, the prey obtained will be distributed to the wolf that has contributed the most to the hunting action in priority, in order of the degree of contribution in the hunting, and finally to the wolf with the least merit [20].…”

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“… 23 (2018), which attracted researchers and practitioners and applied the LWOA algorithm to many dominions, because the LWOA algorithm effectively adaptation, few control parameters, and simplicity of structure. Xuan Chen et al 24 (2021) employments of Opposition-based learning and the Genetic algorithm with Levy’s flight to improve the Wolf Pack Algorithm and achieved maintain the diversity of the initial population during the global search. Their experimental results show that their proposed algorithm has a better global and local search capability, especially in the presence of multi-peak and high-dimensional functions.…”