Multi-strategy fusion improved Northern Goshawk optimizer is used for engineering problems and UAV path planning.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristics

Background:

• The Northern Goshawk Optimizer (NGO) faces challenges with exploration-exploitation balance, slow convergence, local optima, and low precision.
• Addressing these limitations is crucial for developing more effective optimization techniques.

Purpose of the Study:

• To introduce the Multi-Strategy Integrated Northern Goshawk Optimizer (MINGO) to overcome the limitations of the original NGO.
• To enhance exploration strategies, exploitation approaches, and convergence precision.
• To improve the algorithm's ability to escape local optima and maintain population diversity.

Main Methods:

• Integration of Levy flight strategies to preserve population diversity and enhance convergence precision.
• Incorporation of Cauchy mutation strategies to mitigate local optima entrapment.
• Application of Differential Evolution's crossover strategy to eliminate poor-fitness individuals and improve population quality.

Main Results:

• MINGO demonstrated superior performance compared to NGO and other advanced algorithms on CEC-2017 and CEC-2022 benchmark problems.
• Statistical analysis using Wilcoxon rank-sum tests confirmed MINGO's effectiveness.
• Validation on six real-world engineering problems and UAV 3D trajectory planning showcased MINGO's applicability and superiority.

Conclusions:

• MINGO effectively addresses the limitations of NGO, achieving a better balance between exploration and exploitation.
• The proposed enhancements lead to improved convergence speed, precision, and robustness against local optima.
• MINGO represents a significant advancement in metaheuristic optimization, with proven efficacy on complex problems.