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An enhanced snow ablation optimizer for UAV swarm path planning and engineering design problems.

Jinyi Xie¹, Jiacheng He², Zehua Gao³

¹School of Mathematics and Information Science, Guangzhou University, Guangzhou, 510000 China.

|September 24, 2024

Summary

The Enhanced Snow Ablation Optimization (ESAO) algorithm improves upon the original by addressing slow convergence and local optima. ESAO demonstrates superior performance in benchmark tests and practical applications like UAV path planning.

Keywords:

Engineering design problems Metaheuristic algorithm Numerical optimization Snow ablation optimizer UAV swarm path planning

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Area of Science:

Computational Intelligence
Optimization Algorithms
Metaheuristics

Background:

The Snow Ablation Optimizer (SAO) is an effective optimization algorithm but faces challenges with slow convergence and local optima.
Addressing these limitations is crucial for enhancing the performance of metaheuristic algorithms in complex problem-solving.

Purpose of the Study:

To introduce an Enhanced Snow Ablation Optimization algorithm (ESAO) that overcomes the limitations of the original SAO.
To improve the global optimum identification, local optima escape capability, and overall search performance of the optimization algorithm.

Main Methods:

Implemented an adaptive T-distribution control strategy for improved exploratory position adjustments.
Introduced a Cauchy mutation strategy to enhance the ability to escape local extrema.
Developed a leader-based boundary control strategy to boost search performance, accuracy, speed, and stability.

Main Results:

ESAO achieved first-place rankings across 29 CEC2017 benchmark functions against eight popular algorithms.
The algorithm demonstrated superior performance in solving the UAV swarm path planning problem.
ESAO outperformed competitor algorithms in two engineering design problems, showing high solution quality and stability.

Conclusions:

ESAO significantly enhances the effectiveness of the Snow Ablation Optimizer, offering improved accuracy, speed, and stability.
The proposed algorithm shows strong potential for practical applications, particularly in complex optimization tasks like UAV path planning and engineering design.