Hybrid Strategy Improved Horned Lizard Optimization Algorithm for Advanced Global Optimization and Engineering...

Area of Science:

• Computational Intelligence
• Swarm Intelligence Optimization
• Metaheuristic Algorithms

Background:

• The Horned Lizard Optimization Algorithm (HLOA) is a novel swarm intelligence technique inspired by horned lizard behavior.
• Original HLOA exhibits limitations in handling high-dimensional, multimodal, and constrained optimization problems, leading to premature convergence and reduced precision.
• Population diversity decline and inadequate local optimum escape strategies hinder HLOA's effectiveness in complex scenarios.

Purpose of the Study:

• To systematically address the deficiencies of the original HLOA.
• To enhance the global optimization capabilities and engineering applicability of HLOA.
• To propose a hybrid-strategy improved Horned Lizard Optimization Algorithm (HSHLOA).

Main Methods:

• Incorporation of an improved uniform Logistic chaotic mapping for enhanced initial population diversity and quality.
• Implementation of an adaptive optimal guidance strategy with nonlinear dynamic adjustment factors for balanced exploration and exploitation.
• Integration of a lens imaging learning strategy to generate adaptive opposite solutions and improve local optimum escape.

Main Results:

• HSHLOA demonstrated superior performance compared to seven mainstream swarm intelligence algorithms on the CEC 2017 benchmark suite across 30D and 100D settings.
• The algorithm achieved remarkable superiority on unimodal, multimodal, hybrid, and composite functions, showing improved convergence rate, precision, and stability.
• HSHLOA successfully applied to reinforced concrete beam, three-bar truss, and pressure vessel design problems, consistently satisfying constraints and yielding efficient structural solutions.

Conclusions:

• The proposed HSHLOA effectively overcomes the limitations of the original HLOA, particularly in complex, high-dimensional, and constrained optimization tasks.
• HSHLOA exhibits robust performance, superior optimization accuracy, and enhanced ability to escape local optima.
• The algorithm's reliability and effectiveness are validated for practical engineering optimization problems, offering superior structural designs with high efficiency.