Guerrilla Warfare Strategy Ameliorate Besiege and Conquer Algorithm for Complex Optimization Problems

Besiege and Conquer Algorithm (BCA) is a novel evolutionary algorithm recently proposed, inspired by human warfare. It outperforms other algorithms in both single-objective and multi-objective optimization scenarios. However, its design has certain defects and cannot balance exploration and exploitation. In response to this problem, we are inspired by the guerrilla warfare principle and propose some mechanisms to balance exploration and exploitation and propose an improved BCA algorithm Guerrilla Warfare Strategy Ameliorate Besiege and Conquer Algorithm (GW-BCA). First, soldiers' ambush actions are increased to wait for potential enemies, thereby reducing the likelihood of the algorithm being trapped by local optima. Second, during the soldiers' updating process, valuable information is collected to build a backstage battlefield, helping the algorithm escape local optima and achieve global optima. Third, following the idea of guerrilla warfare, G W-BCA dynamically selects different best armies to besiege and conquer, which effectively increases the mobility of the army. The GW-BCA algorithm was compared with several state-of-the-art evolutionary algorithms on the CEC 2017 benchmark functions. Experimental results show that the proposed algorithm effectively addresses the shortcomings of BCA and demonstrates excellent capabilities compared to other algorithms. The source code will be available at www.//anhuailana.com.