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

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.