A differential evolutionary algorithm improvement framework based on artificial potential fields

The Artificial Potential Field (APF) method, widely utilized in path planning, uses virtual environmental forces derived from potential field gradients for guidance. While Differential Evolution (DE) algorithms are recognized for their simplicity and robust global optimization capabilities, they often suffer from premature convergence and reduced search efficiency in complex scenarios. Motivated by the similarity between APF pathfinding and DE optimization, this study proposes an APF-inspired framework that utilizes virtual forces to guide the population. Attractive forces guide individuals toward potential optima, while repulsive forces generated by treating inferior solutions as virtual obstacles mitigate the risk of stagnation. This mechanism effectively steers the population toward promising areas, thereby enhancing search efficiency. Furthermore, an integrated adaptive strategy enhances performance by dynamically adjusting key parameters based on problem characteristics. Comprehensive experiments on the CEC2020 benchmark suite and 36 real-world engineering problems validate the effectiveness of the framework. The results indicate performance improvements over baseline algorithms and competitiveness in comparative studies.