An improved artificial bee colony algorithm based on balance-evolution strategy for unmanned combat aerial vehicle...

Area of Science:

• Robotics and Autonomous Systems
• Artificial Intelligence
• Operations Research

Background:

• Unmanned Combat Aerial Vehicles (UCAVs) are crucial for military operations in hazardous environments.
• Effective UCAV path planning is essential for mission success, requiring consideration of threats and constraints.
• Existing path planning algorithms may not fully optimize routes under complex battlefield conditions.

Purpose of the Study:

• To introduce a novel Artificial Bee Colony (ABC) algorithm enhanced with a Balance-Evolution Strategy (BES) for UCAV path planning.
• To improve the balance between exploration and exploitation capabilities in the optimization process.
• To evaluate the performance of the proposed BE-ABC algorithm against conventional and other advanced ABC algorithms.

Main Methods:

• Development of a Balance-Evolution Strategy (BES) integrated into the Artificial Bee Colony (ABC) algorithm.
• Utilizing convergence information during iterations to refine exploration/exploitation balance.
• Simulating and comparing the BE-ABC algorithm with standard ABC and other modified ABC algorithms for UCAV path planning.

Main Results:

• The BE-ABC algorithm demonstrated superior performance in UCAV path planning simulations.
• The algorithm effectively balanced local exploitation and global exploration capabilities.
• Simulation results confirmed the enhanced competence of BE-ABC over conventional ABC and other state-of-the-art methods.

Conclusions:

• The proposed BE-ABC algorithm offers a more competent solution for Unmanned Combat Aerial Vehicle path planning.
• The Balance-Evolution Strategy effectively enhances the optimization capabilities of the ABC algorithm.
• This improved algorithm provides a robust approach for generating optimal flight routes in challenging combat scenarios.