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Hybrid Strategy Improved Horned Lizard Optimization Algorithm for Advanced Global Optimization and Engineering Applications
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DOI：10.3390/biomimetics11070463.png)
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Abstract
En 中文
The Horned Lizard Optimization Algorithm (HLOA) is a newly proposed swarm intelligence optimizer mimicking the defensive and survival behaviors of horned lizards. The original HLOA suffers evident drawbacks when tackling high-dimensional, multimodal and heavily constrained complicated optimization problems. Rapid decline in population diversity in late iterations and insufficient local optimum escape strategies further trigger premature convergence and unsatisfactory optimization precision. To systematically address the above deficiencies and boost the global optimization and engineering applicability of HLOA, this paper proposes a hybrid-strategy improved Horned Lizard Optimization Algorithm (HSHLOA). First, an improved uniform Logistic chaotic mapping replaces conventional random initialization. It enhances the ergodicity and uniformity of initial populations across search spaces and upgrades the quality of initial solutions and population diversity. Second, an adaptive optimal guidance strategy is constructed via nonlinear dynamic adjustment factors. It prioritizes global exploration in early iterations and strengthens local exploitation in later iterations to accelerate convergence and raise optimization accuracy. Third, a lens imaging learning strategy is embedded. It generates adaptive opposite solutions following dynamic convex lens optical imaging rules, strengthens the capability to escape local optima and mitigates premature convergence. To verify the optimization performance of the proposed algorithm, comparative experiments are conducted on the CEC 2017 benchmark test suite under 30-dimensional and 100-dimensional high-dimensional settings. Seven mainstream swarm intelligence algorithms are selected for benchmark comparison. Quantitative analyses cover convergence rate, optimization precision, numerical stability and local optimum escaping ability. Experimental results reveal that HSHLOA outperforms all peer competitors on unimodal, multimodal, hybrid and composite functions with remarkable superiority. The proposed HSHLOA is further applied to three typical constrained engineering optimization cases, including reinforced concrete beam design, three-bar truss design and pressure vessel design. Application results prove that HSHLOA satisfies all engineering constraints steadily and obtains superior structural schemes with higher efficiency. The reliability and superiority of HSHLOA for practical engineering problems are therefore verified.
Keywords:
Horned Lizard Optimization Algorithm
hybrid multiple strategies
logistic chaotic mapping
adaptive optimal guidance
lens imaging learning
global optimization
Journal
B
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3.9
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