A Novel Improved Dung Beetle Optimization Algorithm for Collaborative 3D Path Planning of UAVs.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristics

Background:

• The standard Dung Beetle Optimizer (DBO) suffers from premature convergence and limited precision in high-dimensional, complex search spaces.
• Addressing these limitations is crucial for advancing metaheuristic algorithm performance.

Purpose of the Study:

• To propose a novel, improved Dung Beetle Optimizer, the Environment-aware Chaotic Force-field Dung Beetle Optimizer (ECFDBO).
• To enhance global exploration and local exploitation capabilities for complex optimization problems.

Main Methods:

• ECFDBO integrates a chaotic perturbation-based nonlinear contraction strategy, an intelligent boundary-handling mechanism, and a dynamic attraction-repulsion force-field mutation.
• The algorithm was evaluated on the CEC2017 benchmark suite with 30 independent runs.
• Statistical tests (Wilcoxon, Friedman) were performed to assess performance differences and rankings.

Main Results:

• ECFDBO achieved statistically significant improvements over seven other metaheuristic algorithms on the CEC2017 benchmark suite.
• The algorithm demonstrated robust global optimization capabilities across varying problem dimensions.
• In a 3D UAV path planning simulation, ECFDBO met global optimality requirements for cooperative planning.

Conclusions:

• ECFDBO effectively addresses the limitations of the standard DBO, offering enhanced global exploration and local exploitation.
• The proposed algorithm shows strong potential for high-dimensional global optimization applications, including cooperative UAV path planning.