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The research aims to enhance UAV path planning through a constrained multi-objective optimization algorithm.

March 2, 2026Results in EngineeringOpen Access

A Constrained Multi-Objective Optimization Algorithm Based on Dynamic Auxiliary Tasks for UAV Path Planning

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This algorithm improves UAV path planning by balancing exploration and convergence, indicating strong real-world potential.

The research aims to enhance UAV path planning through a constrained multi-objective optimization algorithm.
Introduced a constrained multi-objective optimization algorithm based on dynamic auxiliary tasks (DLCMO).
Utilized a dynamic global-local constraint assistance mechanism using an ε-constraint method.
Adjusted selection preferences adaptively to help navigate infeasible solution spaces.
DLCMO showed competitive performance against seven leading algorithms in UAV path planning.
Demonstrated superior exploration capabilities across 33 benchmark test functions.
Achieved improved convergence and diversity in UAV path planning scenarios.

Zheng et al. (2026) studied this question.

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