An enhanced crossover strategy for the artificial lemming algorithm for engineering design optimization

Yan Zhong; Li-Bin Liu; Xiongfa Mai; Haiyan Luo; Yan Zhong; Li-Bin Liu; Xiongfa Mai; Haiyan Luo

doi:10.3934/nhm.2025063

Networks and Heterogeneous Media

2025, Volume 20, Issue 5: 1466-1508. doi: 10.3934/nhm.2025063

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Research article

An enhanced crossover strategy for the artificial lemming algorithm for engineering design optimization

Center for Applied Mathematics of Guangxi, Nanning Normal University, Nanning 530100, China

Received: 20 October 2025 Revised: 01 December 2025 Accepted: 15 December 2025 Published: 23 December 2025

To address the limitations of the artificial lemming algorithm (ALA) in convergence accuracy and premature convergence, this paper proposes an enhanced variant, the cross-strategy-integrated artificial lemming algorithm (CALA). Specifically, the CALA integrates a linear inertia weight strategy to balance exploration and exploitation, a historical best-guided strategy to enhance local search, and a crossover strategy to maintain population diversity. The proposed algorithm was evaluated on the IEEE CEC2017 and CEC2022 benchmark suites, achieving minimum Friedman mean ranks of 1.37 and 1.5, respectively, and outperforming several state-of-the-art algorithms in terms of accuracy and robustness. Furthermore, the CALA was successfully applied to constrained engineering design problems and a photovoltaic model parameter estimation task, demonstrating its effectiveness and practical applicability.
- artificial lemming algorithm,
- linear inertia weight strategy,
- historical best-guided strategy,
- crossover strategy,
- engineering design problems
Citation: Yan Zhong, Li-Bin Liu, Xiongfa Mai, Haiyan Luo. An enhanced crossover strategy for the artificial lemming algorithm for engineering design optimization[J]. Networks and Heterogeneous Media, 2025, 20(5): 1466-1508. doi: 10.3934/nhm.2025063

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Abstract

To address the limitations of the artificial lemming algorithm (ALA) in convergence accuracy and premature convergence, this paper proposes an enhanced variant, the cross-strategy-integrated artificial lemming algorithm (CALA). Specifically, the CALA integrates a linear inertia weight strategy to balance exploration and exploitation, a historical best-guided strategy to enhance local search, and a crossover strategy to maintain population diversity. The proposed algorithm was evaluated on the IEEE CEC2017 and CEC2022 benchmark suites, achieving minimum Friedman mean ranks of 1.37 and 1.5, respectively, and outperforming several state-of-the-art algorithms in terms of accuracy and robustness. Furthermore, the CALA was successfully applied to constrained engineering design problems and a photovoltaic model parameter estimation task, demonstrating its effectiveness and practical applicability.

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